# Increased zinc and copper availability in organic waste amended soil potentially involving distinct release mechanisms

Marie Tella, Matthieu N. Bravin, Laurent Thuriès, Patrick Cazevieille, Claire Chevassus-Rosset, Blanche Collin, Perrine Chaurand, Samuel Legros, Emmanuel Doelsch

► To cite this version:

Marie Tella, Matthieu N. Bravin, Laurent Thuriès, Patrick Cazevieille, Claire Chevassus-Rosset, et al. Increased zinc and copper availability in organic waste amended soil potentially involving distinct release mechanisms. Environmental Pollution, 2016, 212, pp.299-306. 10.1016/j.envpol.2016.01.077. hal-01427332

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Distributed under a Creative Commons Attribution 4.0 International License
1 Increased zinc and copper availability in organic waste amended soil
2 potentially involving distinct release mechanisms

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4 Marie Tella¹,², Matthieu N. Bravin³, Laurent Thuriès³, Patrick Cazevieille¹, Claire Chevassus-
5 Rosset¹, Blanche Collin²,⁴, Perrine Chaurand², Samuel Legros¹, Emmanuel Doelsch¹*

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7 ¹CIRAD, UPR Recyclage et risque, F-34398 Montpellier, France.

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²Aix-Marseille Univ., CNRS, IRD, CEREGE UMR 7330, F-13545 Aix en Provence cedex 4,
France

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³CIRAD, UPR Recyclage et risque, F-97408 Saint-Denis, Réunion, France

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⁴ CEA, IBEB, Laboratory of Microbial Ecology of the Rhizosphere and Extreme Environments,
Saint-Paul-lez-Durance, F-13108, France

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*Corresponding author. Tel.: +33 (0)4 42 97 17 75; fax: +33 (0)4 42 97 15 59; e-mail address:
doelsch@cirad.fr

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## Abstract

This study aimed at determining the fate of trace elements (TE) following soil organic waste (OW) application. We used a unique combination of X-ray absorption spectroscopy analyses, to determine TE speciation, with incubation experiments for *in situ* monitoring of TE availability patterns over a time course with the technique of the diffusive gradients in thin films (DGT). We showed that copper (Cu) and zinc (Zn) availability were both increased in OW-amended soil, but their release was controlled by distinct mechanisms. Zn speciation in OW was found to be dominated by an inorganic species, i.e. Zn sorbed on Fe oxides. Zn desorption from Fe oxides could explain the increase in Zn availability in OW-amended soil. Cu speciation in OW was dominated by organic species. Cu release through the mineralization of organic carbon from OW was responsible for the increase in Cu availability.

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## Capsule

Distinct mechanisms influence Zn and Cu availability in OW-amended soil: Zn release was dominated by desorption from Fe-oxides while organic C mineralization determined Cu availability.

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Graphical abstract

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Keywords

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Bioavailability; Metals; Municipal solid waste compost; Pig manure; Sewage sludge

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# 1. Introduction

The world population has grown substantially over the past century, while becoming more urban. Waste production has in turn risen tenfold and is expected to double again by 2025 (Hoornweg et al., 2013). There is now a global consensus on the necessity to recycle resources to ensure sustainable development (Lehmann, 2013; Schiermeier, 2013). Organic wastes (OW) can be effectively recycled by spreading them on cultivated soils, with many beneficial effects, such as: (i) improving the soil physical properties; (ii) providing major nutrients, thereby reducing the use of inorganic fertilizers, and (iii) improving soilborne microflora and fauna activity by providing organic carbon. These advantages should be assessed together with the potential (eco)toxicological impacts due to the presence of trace elements (TEs) and other contaminants in OW. Many studies have highlighted that OW application in crop fields can drastically boost soil TE levels (Belon et al., 2012; Hargreaves et al., 2008; McBride, 2003; Nicholson et al., 2003). Zinc (Zn) and copper (Cu) account for the highest TE inputs in agricultural soils in many countries. Livestock manure is always identified as the major source of these TEs (Belon et al., 2012). Although the presence of TEs in OW has been demonstrated in many studies, major critical knowledge gaps must be overcome to be able to predict the fate of these potentially toxic compounds following OW application. The first major limitation is the lack of speciation data on TEs in OW. To date, most results concerning TE speciation in OW are inconsistent due to several factors. First, OW are complex matrices composed of different organic and/or inorganic phases (solid and liquid for manure) (Tella et al., 2013). Second, there is still no fully reliable benchmark method available to study TE speciation in such complex materials. Chemical extraction methods are often used but are criticized due their lack of clear chemical selectivity and the variability in the results obtained according to the protocols used (Doelsch et al., 2006b; Doelsch et al., 2008; Romeo et al., 2014). In order to overcome this problem, X-ray absorption spectroscopy (XAS) has been used to describe the speciation of target elements. XAS is sensitive to the oxidation state as well as the distance, number and nature of atoms immediately surrounding the selected element. This spectroscopy method was used for pig slurry, revealing that Cu is mainly (96%) in sulfide form ($Cu_2S$) with a Cu(I) oxidation state (Legros et al., 2010a) and that 14% of Zn is present as $ZnS$ (Legros et al.,
2010b). The detected presence of sulfide was unexpected and reported for the first time in animal manure. Similar Cu and Zn speciation was recently reported in sewage sludge (Donner et al., 2011). These results are crucial for understanding the fate of Cu and Zn from these OW when applied to agricultural soils (Legros et al., 2013).

The second major limitation is the lack of knowledge on TE dynamics following soil OW application. While some authors have suggested that organic matter is the major sorbent of TEs in OW (McBride, 2003), others have indicated that inorganic constituents (Fe and Mn oxides) play a dominant role (Hettiarachchi et al., 2006) and debate regarding the nature and stability of key sorbent phases is ongoing (Mamindy-Pajany et al., 2014). If the prevailing sorbent is organic matter, then TEs may be progressively released over the time course due to organic compound mineralization by soil micro-organisms. Alternatively, if contaminants are sorbed by inorganic waste constituents, their behavior may change as a result of changing physico-chemical conditions (pH or dissolved organic carbon (DOC) concentration) in OW-amended soils (Doelsch et al., 2009; Doelsch et al., 2010). Whatever the processes involved, there is a lack of experimental evidence to be able to determine the main processes that control the TE availability in OW-amended soils.

This study aimed at combining TE speciation characterization in OW with TE availability when OWs are applied on soils. XAS analyses were first used to decipher the TE-bearing components within OW. Then an incubation experiment was carried out using one soil and selected OW applied at doses consistent with current agricultural conditions. Mineralization of organic carbon derived from OW, variations in the physicochemical properties of the soil solution (pH, DOC concentration) and TE availability in OW-amended soils were monitored over a time course during an incubation experiment. This combined approach should enable us to identify parameters controlling the fate of trace elements following OW soil application and to contribute to the debate regarding the nature and stability of TE-bearing components during agricultural OW recycling.

## 2. Experimental

### 2.1 Organic wastes and soil characteristics
This study was focused on six OW selected among OW previously studied (Tella et al., 2013).

The waste types were: fine fractions from municipal solid waste compost (OW1 and OW2), municipal solid waste compost (OW3 and OW4), sewage sludge (OW5) and pig manure compost (OW6). The characteristics of the six OW are displayed in Table 1. The selected soil was a Nitisol that was sampled in the 0-25 cm horizon at La Mare (20°53'24''S, 55°32'13''E) on the island of Réunion. This soil was selected for its intermediate physicochemical properties (pH=6, 2.3 g Corg kg⁻¹) and its naturally low Zn and Cu concentrations (170 and 34 mg kg⁻¹, respectively). A more complete description of the soil and sampling procedure can be found elsewhere (Doelsch et al., 2010).

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## 2.2 X-ray absorption spectroscopy

Zinc K-edge (9.66 keV) and Cu K-edge (8.99 keV) X-ray absorption spectra for the six OW were recorded on the FAME beamline at ESRF (Grenoble, France) and SAMBA beamline at SOLEIL (Gif-sur-Yvette, France). Unfortunately, Zn and Cu concentrations in OW4 were too low for EXAFS acquisition. Spectra were thus recorded for the fine size-fraction of this waste (0.2-20 µm, see Tella et al. (2013) for details) in which 27 and 36% of total Cu and Zn were recovered, respectively. Spectra acquisition was performed on freeze-dried samples pressed into thin pellets at liquid helium temperature to prevent X-ray beam sample damage. Measurements were carried out in fluorescence mode with a 30-elements solid-state Ge detector. To obtain representative spectra, each scan was focused on a different specimen position. The energy was calibrated using Zn or Cu foils (threshold energy taken at the zero-crossing point of the second derivative spectrum). Each spectrum was the sum of at least three scans. Normalization and data reduction was performed according to standard methods (Doelsch et al., 2006a) using Athena software (Ravel and Newville, 2005).

The Zn and Cu-bearing components in the samples were specified as follows: (i) principal component analysis (PCA) of EXAFS spectra database was performed to determine the minimum number of components; (ii) using a library of Zn and Cu model compounds, target transformation (SPOIL function) was used to identify the probable components, and (iii) those components were quantified from the experimental EXAFS spectra with least-square linear combination fitting (LCF)

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(Beauchemin et al., 2002; Manceau et al., 2002). Details on the database of model compounds, PCA, SPOIL function and LCF results are given in Supplementary material (SI-1 and 2). The Zn and Cu K-edge EXAFS spectra, corresponding Fourier transforms and LCF fits are reported in Figures S1, S2 and SI-3. The LCF were satisfactory for all samples (70-125% recovery), with low R-factor values (0.01 to 0.09; Table S3 and S6).

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## 2.3 Incubation experiment

A 120 day-long incubation experiment was carried out in controlled conditions (AFNOR-XPU44-163, 2009) using the selected soil amended with the six OW (the experiment included a treatment without OW, i.e. control soil). OW were added to soil at a 80 t ha⁻¹ equivalent rate, in accordance with actual fertilizing practice. Corresponding organic waste amounts (1.00±0.01 g) were uniformly incorporated into 30 g air-dried soil triplicate microcosms, moistened, maintained at 66% water holding capacity (WHC) with deionized water and incubated in closed chambers (1 L) at 28 ± 1°C for 120 days. Control soils without organic wastes were treated similarly. Preliminary 20-day incubation experiments enabled us to check that the soil with or without the addition of OW remained under fully oxic conditions (Eh > 500 mV) when moistened at 66% WHC (results not shown). The steady profile of Fe accumulation on DGT during the 90-day incubation experiment, regardless of whether OW was added or not, further confirmed that the soil at 66% WHC remained under fully oxic conditions (Figure S5B). CO₂-C released from mixtures or controls was measured by gas chromatography (Agilent M200, Santa Clara, California, USA) after 1, 3, 7, 14, 30, 60, 90 and 120 days. The carbon (C) mineralization coefficient was determined from the quantities of CO₂-C released by OW (expressed in % organic carbon from OW).

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To monitor the physicochemical parameters during incubation, the soil solution for each treatment was collected after 1, 3, 7, 14, 30, 60 and 90 days by centrifuging the soil at 10 000 rpm for 30 min on a geotextile (NYCOM PA REF 03 30/20, Buisine France). pH was measured in the unfiltered soil solutions. The filtered soil solutions (cellulose acetate filter, 0.22 µm, Whatman) were analyzed for DOC content (TOC-L CSH, Shimadzu).
TE availability was monitored *in situ* with the diffusive gradients in thin films technique (DGT, DGT Research Ltd, Lancaster UK). DGT units were equipped with chelex resin and a diffuse layer (thickness 0.8 mm). DGT units were placed at the bottom of vessels used for incubation and were covered by the mixture or control (soil without OW) samples. DGT units were retrieved in triplicate after 1, 3, 7, 14, 30, 60 and 90 days. Three DGT not exposed to the soil were used as an analytical blank for each retrieval date. At retrieval, DGT units were jet-washed with deionized water to remove soil particles and then opened to collect the resin gel. The resin gels were eluted with 1.2 mL of 1 M HNO₃ (Suprapur, Sigma) in closed microvials for 24 h at 20°C. TE concentrations in the eluates were then determined by ICP-MS (X SERIES 2 or Element XR Thermo Scientific). TE masses trapped in resin were calculated from TE concentrations in the eluates (Zhang et al., 2001). After 90 days of exposure to the soil amended or not with OW, DGT binding layers accumulated less than 0.5 µmol⁺ of high selectivity metals (Al, Cd, Cr, Cu, Fe, Ni, Pb, and Zn (Hooda et al., 1999)), which was more than 30-fold lower than the binding capacity of the binding layer (estimated at about 15 µmol⁺ (Garmo et al., 2003)). DGT binding layers were thus far from saturated after 90 days and likely served as an infinite sink. DGT still accumulated Cu and Zn after 60 days of deployment and even 90 days in some cases, so substantial fouling of the outer filter membrane or the diffusive layer of DGT units was very unlikely. We cannot however definitely exclude that slight and partial fouling might have occurred. As for C mineralization, the Zn and Cu availability coefficient represents the quantities of Zn and Cu released by the control soil or by the soil amended with OW and trapped by the DGT (expressed as % of the amount of Cu and Zn supplied by OW).

## 2.4 Modeling carbon mineralization data

The CO₂-C data were modeled with the Transformation of Added Organics (TAO) model, which is a three-compartment model that was calibrated in its TAO-C original version (Thuries et al., 2001; Thuries et al., 2002) with a wide range of OW (C: N ratios ranging from 1 to 27) incubated in the
same controlled conditions as described above. The remaining added organic C fraction (RAOCF)

was calculated for a known incubation time (t) after adding initial organic wastes (t0), by:

$$190 \quad \text{RAOCF} = 1 - \text{P}'\text{Le} - \text{kL}'t - t0 - 1 - \text{P}'\text{L} - \text{PSe} - \text{kR}'t - t0 - \text{PS}$$

where P'_{L} is the very labile fraction, P_{S} is the stable fraction and P_{R} (1 - P'_{L} - P_{S}) is the resistant

fraction of organic C from OW; the very labile and the resistant fractions being mineralized

according to their corresponding mineralization rates k'_{L} and k'_{R}, respectively. By definition, a null

mineralization was assigned to the stable fraction (P_{S}). In a previous work^{25}, it has been shown that

RAOCF mainly depended on the three compartment sizes: P'_{L}, P_{S} and P_{R}. A simplified version of

RAOCF was proposed^{25} with constant mineralization rates (k'_{L} = 0.4 ± 0.15 d^{-1} and k'_{R} = 0.012 ±

0.003 d^{-1}). This version has been validated with OWs from different types and geographical

origins^{40}. Simulations were run (Vensim™, Ventana Systems, Harvard, MA, USA) to best fit the

measured C mineralization data, and resulted in optimized P'_{L} and P_{S} values. The significance of

optimized TAO predictions was assessed with an *F* test.
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**3. Results and Discussion**

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**3.1 Zinc mainly sorbed on Fe oxides in organic wastes**

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Eight different reference compounds were necessary to fit all the Zn K-edge EXAFS spectra (Figure 1, Table S3). Some of these were pooled together as they presented similar Zn chemical and structural environmental features. Zn sorbed on goethite and Zn sorbed on ferrihydrite references were pooled in the “Zn sorbed on Fe oxides” group. Zn-phytate and hopeite were pooled in the “Zn-phosphate” group, while Zn-citrate, Zn-acetate and Zn-methionine were pooled in the “Zn-organic complex” group and represented Zn bound to organic matter. Zn-kerolite was named “Zn-phyllosilicate” and sphalerite represented “Zn-sulfide”. For five of the six studied OW, Zn speciation was mainly dominated by “Zn sorbed on Fe oxides” since this contribution ranged from 46% for OW6 to 83% for OW2. A secondary “Zn-phosphate” contribution (33–46%) was detected for OW3, OW5, OW6 and MSWS_S, and 51% of “Zn-organic complex” was present for OW4. “Zn-phyllosilicate” and “Zn-sulfide” were only present as minor Zn-bearing phases (≤18%).

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Our main conclusion concerning the predominance of Zn-inorganic bearing phases in OW was in agreement with the findings of the very few reported studies carried out using synchrotron-based spectroscopy techniques to study Zn speciation. These studies were only focused on two types of OW: sewage sludge (Donner et al., 2011; Donner et al., 2012; Hettiarachchi et al., 2006) and fresh pig slurry (Legros et al., 2010b). We extended these conclusions to a broader range of OW. The predominance of “Zn sorbed on Fe oxides” in OW was consistent with the high sorption affinity of Fe oxide surfaces for Zn (Dzombak and Morel, 1990). We found the highest proportion of “Zn sorbed on Fe oxides” for both fine fractions from municipal solid waste composts (OW1 and OW2). These wastes originated from fill dumping and were characterized by the presence of soilborne Fe oxides (2013). The second highest proportion of “Zn sorbed on Fe oxides” was observed for sewage sludge (OW5). Up to 75% of Zn was previously reported to be sorbed on Fe oxides in sewage sludge (Donner et al., 2011). Fe is commonly added as coagulant agent during the sludge treatment process and Fe oxides are thus common in sewage sludge (Merrington et al., 2003). “Zn-phosphate” is present in large proportions in OW6, OW5, OW4 and OW3. Phosphate minerals are common in manure (Legros et al., 2010b) and sewage sludge (Merrington et al., 2003). Phosphate can sorb Zn³⁰
or co-precipitate with Zn (e.g. Zn₃(PO₄)₂ represented 22% of Zn in sludge from a pilot wastewater treatment plant (Ma et al., 2014)). We found that Zn-bearing phyllosilicates were abundant only in wastes from Madagascar (OW1 and OW3). This result could indicate a geographical fingerprint of these wastes, but additional observations are mandatory to confirm this trend. Organic complexes of zinc were observed in OW6 and OW4, these OW were characterized by the highest organic carbon contents (Table 1). “Zn-sulfide” was only detected in OW2 as a minor contribution (17%). Recent results demonstrated that, although sulfide minerals play a dominant role in metal binding in freshly dewatered biosolids, Zn has different speciation in aged biosolids (Donner et al., 2011). All of the studied OW had been stockpiled or composted for several months or years (Tella et al., 2013), which therefore explains the minor contribution of “Zn-sulfide” we observed.

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## 3.2 Copper mainly bound to organic matter in organic wastes

Cu speciation was dominated by “Cu(II)-organic complexes”, whose proportion represented from 50% (OW4) to 100% (OW1, OW2, OW3 and OW6) (Figure 1, Table S6). Six different reference compounds were necessary to fit all the Cu K-edge EXAFS spectra. Among those references, Cu-histidine, Cu-malonate and Cu-malate were pooled in the “Cu(II)-organic complex” group. We differentiated “Cu(I)-sulfide” (corresponding to Cu(I)-thiophenolate + Cu₂S) from “Cu(II)-sulfide” (corresponding to CuS). The fact that Cu was predominantly complexed with organic matter was consistent with the findings of most previous studies based on sequential chemical extraction procedures. These studies indicated that Cu was predominantly bound to organic matter regardless of the type of OW: 90% in sewage sludge (Chen et al., 2008), 50% in animal manure (Miaomiao et al., 2009) and 80% in municipal solid waste compost (Paradelo et al., 2011). “Cu(II)-sulfide” was only present in OW4 (13%) whereas “Cu(I)-sulfide” represented 37% for OW4 and 19% for OW5. As previously mentioned for zinc, we mainly studied aged OW (composting and/or stockpiling), so we detected very low amounts of Cu-sulfide in comparison to levels in fresh pig slurry (96%) (Legros et al., 2010a) or fresh sewage sludge (65-95%) (Donner et al., 2013).

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## 3.3 Carbon mineralization and variations in physicochemical conditions during incubation
Mineralization of organic C from OW was low during the 120 days of incubation, as the C-mineralization coefficients ranged from 4 to 22% (Figure 2). From t=7 days until the end of the incubation, OW were ranked as follows: OW4 > OW5 > OW2 > OW3 > OW6 > OW1. TAO predictions were all highly significant (F tests, α = 0.001, Figure 2) and could be considered as good estimates of the measured data. P'_L, P_R, and P_S were thus representative of the actual proportions of labile, resistant and stable organic C fractions from OW. P'_L or P_R represented less than 10% of organic C from OW (Table S7), except for OW4 with P_R=17.6%. The organic C fraction derived from OW sensitive to mineralization was thus low. This was in accordance with the relatively low C-mineralization coefficient measured (Figure 2). In contrast, the P_S values were high and ranged from 72 to 95% of organic C (Table S7). Similar values have only been recorded for very stable composts (Thuries et al., 2001). The fraction of the labile pool (P'_L) quickly mineralized during incubation, whereas the resistant fraction (P_R) progressively mineralized over the time course of the study (Figure S3). The moderate C-mineralization coefficient and progressive C dynamics of the studied OW is typical of soil improvers such as composts (AFNOR-NFU44051, 1999).

During incubation, the physicochemical parameters of soil solutions were altered, i.e. acidification and decreased DOC concentrations. At the beginning of the incubation period, the control soil pH was 5.8 (Figure 3A) and OW incorporation in the soil led to an increase in the amended soil pH, which ranged from 6.5 (OW1) to 7.4 (OW5). OW incorporation in the soil led to an increase in the DOC concentrations that were the highest at t=1 day, with the values ranging from 280 mg L⁻¹ for OW5 to 45 mg L⁻¹ for OW1. During incubation, the soil solution pH tended to decrease in both the control and amended soils. We observed a -0.8 pH unit decrease for the control soil between the beginning and end of the incubation period. For amended soils, the pH decrease ranged from -0.1 (OW2) to -1.4 pH units (OW5) throughout the incubation. A decrease in pH is common during soil incubation (Doelsch et al., 2010) and could be attributed to the acidic effect of decomposable OW products (Usman et al., 2004). The DOC values (Figure 3B) decreased and were almost similar for all OW at 45 ± 15 mg L⁻¹ at t=14 days, except for OW5 (97 mg L⁻¹).

**3.4 Zinc speciation in organic waste determines its availability in amended soil**
For four of the six studied OW, OW application significantly increased the mass of Zn accumulated by DGT at t=90 days in comparison to the control soil (Figure 4A). The Zn mass accumulated by DGT was 207±50 ng for the control soil and similar Zn masses were accumulated for the soil incubated with OW3 (170±40 ng) and OW4 (191±30 ng). For the other OW, the Zn masses accumulated by DGT were higher than for the control soil: 360±60 ng for OW6; 690±190 ng for OW5 and 1300±50 ng for both OW1 and OW2.

The Zn-availability coefficient was positively and significantly correlated with the percentage of “Zn sorbed on Fe oxides” ($R^2_{adj} = 0.63$, Figure 5A), which was the main Zn speciation contribution (we noted that the OW3 data substantially altered this correlation, probably due to the low Zn concentration in this OW, $R^2_{adj} = 0.94$, data not shown). No relationship was found between Zn and Fe fluxes measured by DGT during OW-amended soil incubations as the mass of Fe accumulated by DGT was steady during the experiment (Figure S5). This suggests that oxide dissolution could not explain the Zn availability, as mentioned for a soil historically contaminated with sewage sludge (Luo et al., 2013). Therefore, the Zn availability would be mainly governed by Fe oxide desorption phenomena.

pH and DOC variations were not significantly correlated with the Zn-availability coefficient (Figure S4). The alteration of these physicochemical parameters could not explain the increase in the Zn-availability coefficient. However, the decreased pH observed during incubation (Figure 3A) could have contributed to the Zn desorption phenomena (Dzombak and Morel, 1990). As for pH and DOC concentrations, the C-mineralization coefficient was not correlated with the Zn-availability coefficient ($R^2_{adj} < 0.05$, Figure 5 B). We can therefore conclude that C mineralization had no direct impact on Zn availability.

## 3.5 Carbon mineralization determines copper availability in organic waste amended soil

For all the OW-amended soil mixtures, the amount of Cu accumulated by DGT at t=90 days was higher than in the control (Figure 4B). The Cu mass accumulated by DGT after 90 days of incubation was 70±3 ng for the control soil (Figure 4B). The accumulated Cu masses ranged from 84±2 ng for OW3 to 252±30 ng for OW2.
Contrary to Zn, the Cu-availability coefficient was significantly correlated with the C-mineralization coefficient ($R_{adj}^2$ = 0.88, Figure 6A). This suggests that organic carbon mineralization triggered the release of Cu. It should be noted that the relationship between the Cu-availability coefficient and Cu speciation in OW (i.e. “Cu(II)-organic complex” contribution) (Figure S7) must be considered with caution since Cu speciation was dominated by “Cu(II)-organic complexes” (100% for OW1, OW2, OW3 and OW6)

Accordingly, the Cu-availability coefficient was significantly correlated with the sum of the C-pools susceptible to mineralization, namely the labile ($P'_L$) and resistant ($P_R$) pools ($R_{adj}^2$ = 0.82, Figure 6B). This correlation confirmed that OW mineralization could explain the increase in Cu-availability during incubation. Our results suggested that the amount and state of decomposition of the organic matter derived from OW was a key parameter determining the availability of Cu in amended soil. A similar conclusion was reached in a study on a soil contaminated with sewage sludge for 20 years, where sludge-derived organic matter contributed to Cu accumulation in macroaggregates, which disaggregated after cessation of application leading to high Cu losses(Parat et al., 2007).

As there was no significant correlation between the Cu-availability coefficient and the pH or DOC concentration ($R_{adj}^2$ < 0.16, Figure S6), we assumed that the alteration of these physicochemical parameters could not explain the Cu availability.

The relationship between the TAO parameters and Cu availability opens new prospects for rapidly screening the Cu availability potential. Indeed, TAO parameters ($P'_L$, $P_R$ or $P_S$) could be estimated by the quick and inexpensive near infrared spectroscopy technique$^{40}$.
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## 4. Conclusions

We showed that the application of most OW increased the Cu and Zn availability in the amended soil. But distinct processes were responsible for the increase in Zn and Cu availability. Zn speciation in OW was dominated by an inorganic species, i.e. Zn sorbed on Fe oxides. We thus concluded that Zn desorption from Fe oxides governed the increase in Zn availability. By contrast, Cu speciation in OW was dominated by organic species and the increase in Cu availability was explained by the mineralization of organic C from OW. Information on TE speciation and organic matter mineralization in OWs is thus essential for better understanding and predicting the fate of these trace elements in OW amended soil.

Despite the original set of experiments we conducted to identify TE-bearing phases and TE availability in OW-amended soil, we were limited by the low number of soils and OW investigated. A complementary future line of research will be to extend this study to a larger selection of soils and OW (e.g. digestates produced by anaerobic digestion or vinasse from fermentation industries). These selected OW should be representative of the diversity of both TE speciation in OW as well as the C mineralization dynamics. Finally, to confirm our assumptions, it would be necessary to carry out further experiments would be required to directly detect the release mechanisms of metals.

Our results further suggest that sustainable management of OW recycling on cultivated soils would benefit from more systematic monitoring of OW mineralization, which determines the fate of TEs in amended soil. This approach should be implemented especially in the long-term field experiments to assess the fate of TEs in soils regularly amended over time, as is the case under current farming conditions.

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Acknowledgements

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The authors wish to thank P. Cambier, M. N'Dienor and T. Rafolisy who provided us with the samples and considerable advice on sample selection. We gratefully acknowledge G. Pokrovski and A. Voeglin for the kind gift of XAS standard spectra. We acknowledge the European Synchrotron Radiation Facility for provision of synchrotron radiation facilities and we would like to thank Jean-Louis Hazemann and Isabelle Kieffer for assistance in using the B30B-Fame beamline. We acknowledge SOLEIL for providing access to the synchrotron radiation facilities and we would like to thank Valérie Briois and Emiliano Fonda for assistance in using the Samba beamline. This study was supported by CNRS-NSF collaboration agreement No. 7383, and was part of the ISARD project, funded by ANR (Agence Nationale de la Recherche, France) under Grant ANR-08-STRA-15-01.

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Figure 1. Proportion of Zn (A) and Cu (B) ligands determined from the linear combination fitting of the organic waste Zn and Cu K-edge extended X-ray absorption fine-structure spectroscopy (EXAFS) data.

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Figure 2. Variation in the carbon mineralization coefficient during incubation. Symbols represent the mean value of the experimental data (n=3), and plain lines represent the TAO model outputs. Vertical bars represent the maximum of cumulative confidence intervals at 95% and are shifted for more clarity.

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Figure 3. Variations in pH (A) and dissolved organic carbon (DOC) concentrations (B) in soil solution during incubation. Uncertainties for pH are constant (0.1 pH unit) and represented only for measurements at 90 days of incubation for more clarity.

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Figure 4. Masses of Zn (A) and Cu (B) accumulated on diffusive gradients in thin films (DGT) exposed to the soil during incubation. Error bars correspond to ±1σ.

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Figure 5. Zn-availability coefficient (difference between the mass accumulated by DGT at t=90 d in the soil+organic waste mixture and the mass accumulated in the control soil, normalized by the Zn concentration in OW) as a function of the % of Zn sorbed on Fe oxides (determined from the linear combination fitting of the organic waste Zn K-edge EXAFS data, A) and as a function of C-mineralization coefficient (B). Symbols, red line and dashed line represent the experimental data, model data and confidence interval at 95%, respectively.

395

396

397

398

399

400

401

Figure 6. Cu-availability coefficient (difference between the mass accumulated by DGT at t=90 d in the soil+organic waste mixture and the mass accumulated in the control soil, normalized by the Cu concentration in organic waste) as a function of C-mineralization coefficient (top curve). Cu availability as a function of the characteristics of organic matter from TAO modelling (P'~L~ and P~R~ are the labile and resistant fractions of organic C from the organic waste, B). Symbols, red line and dashed line represent the experimental data, model data and confidence interval at 95%, respectively.
402

403 **Table legends**

404 Table 1. Organic wastes and soil characteristics.

405

406

407
407
408

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<figure><img src="image_3.png" /><figcaption>Figure 1. Proportion of Zn (A) and Cu (B) ligands determined from the linear combination fitting of the organic waste Zn and Cu K-edge extended X-ray absorption fine-structure spectroscopy (EXAFS) data.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2. Variation in the carbon mineralization coefficient during incubation. Symbols represent the mean value of the experimental data (n=3), and plain lines represent the TAO model outputs. Vertical bars represent the maximum of cumulative confidence intervals at 95% and are shifted for more clarity.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3. Variations in pH (A) and dissolved organic carbon (DOC) concentrations (B) in soil solution during incubation. Uncertainties for pH are constant (0.1 pH unit) and represented only for measurements at 90 days of incubation for more clarity.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4. Masses of Zn (A) and Cu (B) accumulated on diffusive gradients in thin films (DGT) exposed to the soil during incubation. Error bars correspond to ±1σ.</figcaption></figure>
<figure><img src="image_7.png" /></figure>

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

Figure 5. Zn-availability coefficient (difference between the mass accumulated by DGT at t=90 d in the soil+organic waste mixture and the mass accumulated in the control soil, normalized by the Zn concentration in OW) as a function of the % of Zn sorbed on Fe oxides (determined from the linear combination fitting of the organic waste Zn K-edge EXAFS data, A) and as a function of C-mineralization coefficient (B). Symbols, red line and dashed line represent the experimental data, model data and confidence interval at 95%, respectively.
<figure><img src="image_9.png" /></figure>

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

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

<figure><img src="image_12.png" /></figure>

Figure 6. Cu-availability coefficient (difference between the mass accumulated by DGT at t=90 d in the soil+organic waste mixture and the mass accumulated in the control soil, normalized by the Cu concentration in organic waste) as a function of C-mineralization coefficient (top curve). Cu availability as a function of the characteristics of organic matter from TAO modelling (P'L and PR are the labile and resistant fractions of organic C from the organic waste, B). Symbols, red line and dashed line represent the experimental data, model data and confidence interval at 95%, respectively.
<table><caption>Table 1. Organic waste and soil characteristics.</caption><thead><tr><th rowspan="2">Sample ID</th><th rowspan="2">Type of organic wastes and geographical origin</th><th colspan="2">Origin</th><th rowspan="2">Treatment</th><th>Zn</th><th>Cu</th><th>C organic</th><th>pH</th></tr><tr><th>Geographical</th><th>Production and type of soil</th><th>mg kg<sup>-1</sup></th><th>mg kg<sup>-1</sup></th><th>% mass</th><th>1:2 soil:water ratio</th></tr></thead><tbody><tr><td>OW1</td><td>Fine fraction from municipal solid waste compost _ Madagascar (sample ID in Tella et al. (2013): FF-MSWC_M)</td><td>Madagascar</td><td>Urban</td><td>Stockpiled (>10 years)</td><td>1 900</td><td>280</td><td>8</td><td>7.8</td></tr><tr><td>OW2</td><td>Fine fraction from municipal solid waste compost _ Senegal (sample ID in Tella et al. (2013): FF-MSWC_S)</td><td>Senegal</td><td>Urban</td><td>Stockpiled (>10 years)</td><td>2 190</td><td>330</td><td>9</td><td>7.6</td></tr><tr><td>OW3</td><td>Municipal solid waste compost _ Madagascar (sample ID in Tella et al. (2013): MSWC_M)</td><td>Madagascar</td><td>Urban</td><td>Composted</td><td>240</td><td>48</td><td>16</td><td>9.0</td></tr><tr><td>OW4</td><td>Municipal solid waste compost _ Senegal (sample ID in Tella et al. (2013): MSWC_S)</td><td>Senegal</td><td>Urban</td><td>Composted</td><td>100</td><td>24</td><td>24</td><td>8.6</td></tr><tr><td>OW5</td><td>Sewage sludge _ Senegal</td><td>Senegal</td><td>Urban</td><td>Dried</td><td>1 000</td><td>340</td><td>25</td><td>6.8</td></tr><tr><td>OW6</td><td>Manure compost from pig slurry _ France (sample ID in Tella et al. (2013): MC_Pi_F)</td><td>France, Réunion</td><td>Agricultural</td><td>Stockpiled (some months)</td><td>700</td><td>180</td><td>30</td><td>8.4</td></tr><tr><td colspan="4">Soil</td><td>France, Réunion</td><td>Nitisol</td><td></td><td></td><td></td></tr></tbody></table>