# Silver origins and spatial distribution in soils of southwestern France: A first assessment with cokriging

Lionel Savignan, Stéphane Faucher, Stéphanie Jalabert, Alexandre Lee, Gaetane Lespes, Philippe Chéry

► To cite this version:

Lionel Savignan, Stéphane Faucher, Stéphanie Jalabert, Alexandre Lee, Gaetane Lespes, et al.. Silver origins and spatial distribution in soils of southwestern France: A first assessment with cokriging. CATENA, 2023, 224, pp.106992. 10.1016/j.catena.2023.106992. hal-03996549

HAL Id: hal-03996549

https://univ-pau.hal.science/hal-03996549v1

Submitted on 26 Mar 2025

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 Silver origins and spatial distribution in soils of southwestern France: a first

2 assessment with cokriging

3 Lionel Savignan<sup>a</sup>, Stéphane Faucher<sup>b</sup>, Stéphanie Jalabert<sup>a</sup>, Alexandre Lee<sup>a</sup>, Gaëtane Lespes<sup>b*</sup>,

4 Philippe Chéry<sup>a*</sup>

5 <sup>a</sup> Bordeaux Science Agro, UMR 5805 EPOC, 1 cours du Général De Gaulle, 33175 Gradignan, France

6 <sup>b</sup> Université de Pau et des Pays de l'Adour / E2S UPPA, CNRS, Institut des Sciences Analytiques et de

7 Physico-Chimie pour l'Environnement et les Matériaux (IPREM), UMR 5254, Helioparc, 2 avenue

8 Pierre Angot, 64053 Pau Cedex 09, France

9 * correspondence: gaetane.lespes@univ-pau.fr; philippe.chery@agro-bordeaux.fr

10 **Keywords:** trace elements, soil monitoring, GIS, geostatistics

11 **Abstract:**

12 Ag is a rare, non-essential metal with many applications in different forms, including nanoparticles
13 (Ag-NPs). Its release into the soil environment can represent a risk, due to the toxicity of silver. This
14 study is based on the analysis of samples from the two sampling campaigns of the Soil Quality
15 Measurement Network, RMQS1 and RMQS2, to establish the spatial distribution and origin of Ag in
16 the soils of southwestern France (area of 90,293 km²). Given the progress of the second campaign
17 (completion in 2027), we chose to do a cokriging to estimate the spatial distribution, with Pb, Cd or
18 Cu as potential secondary variables. The results of the analyzes of the 35 samples of the RMQS2
19 indicate a range of Ag concentration in the soils going from 0.015 to 0.222 mg kg⁻¹ and a median of
20 0.081 mg kg⁻¹. Correlations were found between the concentrations of Ag, Cd, Cu and Pb, and also
21 between Ag concentrations and distance to Pb mines and deposits (r = 0.62). This suggests a common
22 origin for Ag and Pb linked to historical exploitations of argentiferous galena. No link could be
23 demonstrated between the concentration of Ag and the high density of anti-hail ground generators in
24 the region. The analysis of the (auto-)variograms showed that Pb is the most relevant variable
25 compared to Cd and Cu to achieve a cokriging. Thus, based on Ag concentrations from RMQS2 and
26 Pb concentrations from RMQS1 (356 samples), a linear co-regionalization model was established and
27 the co-kriging of Ag concentrations in soils calculated. Compared to ordinary kriging, cokriging
28 improved the quality of the interpolation (decrease in the root mean square error).

29

29
## 1. Introduction

Silver (Ag) is a rare, non-essential metal with high economic value due to its preciousness and its industrial applications. Its abundance in the earth's crust is 0.07 mg kg⁻¹ (Wedepohl, 1995). It is found in the native state Ag⁰ or associated with other elements such as S, Sb, As, Bi, Pb, Cu, Te and Au. It can be found mainly in 0 and +1 oxidation states. It is a metal used since antiquity for currency in particular but also for its antimicrobial properties, for example for dental amalgams (Fromm, 2011). Other applications of Ag are in electronics, jewelry and film photography, the latter being in decline (The Silver Institute, 2019). Nowadays, Ag is experiencing growing interest for its applications in nanoparticle form (Ag-NPs) for its antimicrobial properties, in the biotechnology and bioengineering sector (e.g. cosmetics, body care products, textiles, packaging); thus, among the manufactured nanoparticles, the Ag-NPs are those whose rate of use is growing the fastest. Silver is also increasingly used in the form of silver iodide for cloud seeding to prevent hail formation (Dessens et al., 2016; Lorenz et al., 2012; Peters et al., 2016; Tsiouris et al., 2002).

Nearly 30,000 tons of Ag are produced worldwide each year mainly by mining (The Silver Institute, 2019). According to Eckelman and Graedel (2007), 13,000 tonnes Ag are emitted, intentionally or not, into the environment each year, with mining activities and all types of landfills representing 75% of flows. These flows show the importance of managing waste, which represents a significant part of the risk of soil contamination by Ag. These flows are also to be linked with the emerging uses of nanoparticles. Gottschalk et al. (2009) modelled these flows using a probabilistic approach: their study predicts an increase in the concentration of Ag-NPs in Europe of 1581 ng kg⁻¹ y⁻¹ in soils treated with sewage sludge, and of 22 ng kg⁻¹ y⁻¹ in untreated soils. In soils deemed uncontaminated, Ag is generally found at concentrations below 0.1 mg kg⁻¹ (Hooda, 2010). High concentrations can be linked to geochemical backgrounds and natural deposits (154 mg kg⁻¹) (Fernández-Caliani et al., 2019), mining residues (Mariet et al., 2016), industrial pollution (Douay et al., 2008) as well as landfills and sewage sludge (Lottermoser, 2012).

In its Ag⁺ free ionic form, silver has biocidal effects. It is the most toxic metal cation for soil microbiota compared to Cu²⁺ and Ni²⁺; Ag⁺ is also toxic to plant growth and germination (Ratte,
1999). However, in the soil, Ag has a strong affinity for organic matter and sulfur in particular, its main chemical form then being Ag₂S (Bell and Kramer, 1999; Kleja et al., 2016; Sikora and Stevenson, 1988). In this form, the toxicity of Ag is greatly reduced since Ag₂S is very poorly soluble (Ratte, 1999). The more recent use of Ag-NPs has questioned the toxicity of silver. Indeed, the dissolution of Ag-NPs can release Ag⁺ in soils (Cornelis et al., 2012). Moreover, in this nanoparticle form, Ag⁰ like Ag⁺ proves to be toxic for plants, invertebrates and soil microbiota (Courtois et al., 2019). For example, exposure to Ag-NPs at low concentration (0.3 mg kg⁻¹ for 63 days) inhibited the growth and germination of the plant *Arabidopsis thaliana* (Geisler-Lee et al., 2014). The presence of high concentration Ag-NPs in soils amended with sewage sludge inhibits the reproduction of earthworms (*Eisenia fetida*) (Garcia-Velasco et al., 2016). In addition, the presence of Ag-NPs in soils also disrupts nitrification cycles (Schlich et al., 2018). Thus, the growing use of Ag-NPs represents a potential risk for the environment.

There are a limited number of studies on the spatial distribution of Ag in soils over large regional, national or continental areas (Chen et al., 1991; De Vos and Tarvainen, 2005; Reimann et al., 2018; Reimann and de Caritat, 2017; Smith et al., 2014). Over the past two decades, national soil monitoring programs have been established in Europe to determine soil quality and track temporal and spatial changes in soils (Morvan et al., 2008). In France, the Soil Quality Measurement Network (Réseau de mesure de la qualité des sols, RMQS) (Arrouays et al., 2003) was designed to include these temporal monitoring objectives; the elements concerned are as follows: As, Cd, Cr, Cu, Hg, Mo, Ni, Pb, Tl, Zn. Thus a first campaign took place between 2000 and 2009 (RMQS1) while the second campaign (RMQS2) started in 2016 and will end in 2027. The results of the first campaign made it possible to establish at regional and national scales the spatial distribution of trace elements monitored in soils using geostatistics (Marchant et al., 2017, 2010; Saby et al., 2006; Savignan et al., 2021). Among geostatistical interpolation techniques, kriging based on a single variable is one of the most common methods. It is also possible to improve the quality of the interpolation by using cokriging, based on more densely acquired secondary variables. Other advantages of cokriging are cost reduction and quickness when secondary variables are cheaper and faster to acquire. In practice, cokriging has
been used to interpolate soil properties such as the concentration of a trace element from secondary
variables such as pH, organic carbon, other trace elements or physical measurements such as
reflectance and magnetic susceptibility (Chen et al., 2016; Golden et al., 2020; Rosenbaum and
Söderström, 1996; Wu et al., 2006).

During the RMQS2 campaign, in addition to the monitored elements listed above, we chose to
focus on Ag as an emerging element. Given the progress of this sampling campaign with regard to its
duration, we propose to carry out a preliminary spatial and geostatistical analysis concerning Ag. The
geostatistical approach was based on cokriging from the data obtained during (i) a previous sampling
campaign for which the concentrations of Pb, Cd and Cu were determined. These elements were taken
here as reference elements because of their uses and presence in soils, possibly common with those of
Ag; and (ii) an ongoing sampling campaign involving the same elements, and also Ag. Our objectives
were (i) to evaluate the contribution of such approach in terms of information that can be acquired
 origin and spatial distribution) and spatial resolution, and (ii) to discuss its interest, especially with
 respect to the accuracy of the cokriging prediction. This on the scale of a region, the South-West of
France (SWF).

## 2. Materials and methods

### 2.1. Study region

The total area of the SWF is 90,293 km². This area includes the Nouvelle Aquitaine region, which is the largest agricultural region in France (39,000 km² of usable agricultural area), and the Gers department. The agricultural production of this area is varied (field crops, livestock, viticulture and arboriculture). The area also includes large forest areas (28,000 km²) with in particular the Landes massif located in the south-west of the region (Figure 1A). This area is inhabited by 6.1 million people, including 1.2 million within the urban area of Bordeaux (Figure 1A). The dominant soils of the study area and the associated parent materials have been described in a previous publication (Savignan et al. 2021).
## 2.2. Soil sampling

The RMQS network grid (16×16 km) in this area corresponds to 356 sampling sites. During the RMQS1 campaign, the locations of the sites were chosen if possible at the centre of each 16×16 km cell (Figure 1B), or otherwise within 1 km around (Jolivet et al., 2006). The types of land use are also listed as well as the technical itineraries on each parcel concerned by the sampling. The RMQS2 campaign is subject to an annualized spatio-temporal sampling strategy to detect changes as soon as possible compared to the previous campaign (Bourgeois et al., 2012). Following this strategy, soil sampling was carried out at 35 sites (Figure 1B). All of these samples were analyzed for this study.

For each site, soil samples were taken following the protocol of the RMQS network handbook (Jolivet et al., 2017, 2006): 25 individual cores in the topsoil (0-30 cm to ± 5 cm depending on the thickness of the first organo-mineral horizon) using a stratified random sampling plan in a 20×20 m area. During the RMQS1 campaign, the soils were sampled from plot no.1, while for the RMQS2 campaign they were sampled from plot no. 2 (Figure 1C). For each site, the 25 cores were mixed to obtain a composite soil sample. For the two campaigns (RMQS1 and 2), in addition to the 25 corings, a pit was dug close (5 m) to the sampling area in order to describe the soil profile, to sample the soil horizons and to perform specific measurements (apparent density, coarse elements, etc.) (Figure 1C). In addition, for sites with agricultural occupation, a survey was carried out on agricultural practices (rotation, fertilization, phytosanitary treatment, etc.) of the plot with the operator and/or owner. These surveys have been used to research the origins of Ag in soil.

## 2.3. Chemicals

Nitric acid (HNO₃) (70%), hydrochloric acid (HCl) (37%) and hydrofluoric acid (HF) (49%) (supplied by JT BAKER) of analytical purity were used for mineralization of soil samples. Elemental solutions of Ag, Cd, Cu and Pb at 1000 µg L⁻¹ were used for calibration. Indium and iridium at 1000 µg L⁻¹ were used as internal standards. Before analysis, the solutions resulting from soil mineralization (complete dissolution of the soil matrix) were diluted with ultrapure deionized water, produced by a Milli-Q® system (Merck KGaA, Darmstadt, Germany), having a resistivity of 18.2 MΩ cm and a total
135

organic carbon concentration ≤ 2 µg kg⁻¹. The JSd-2 sediment provided by the Geological Survey of Japan (Ibaraki, Japan) was used for elemental analysis validation.

136

137

## 2.4. Soil characterization

138

The physico-chemical characterization of composite soil samples from RMQS1 used in this study was described in a previous publication (Savignan et al. 2021). Soil samples taken from the pit (surface horizon) during RMQS2 were characterized by the analytical methods described below. Soil samples were air dried and then sieved at 2 mm. The particle size (clays, coarse and fine silts, coarse and fine sands) was determined without decarbonation using a Robinson pipette (AFNOR, 2003). Each soil was mixed with water (soil/water ratio 1/5, v/v), then the pH was measured in the water (AFNOR, 1994). The cation exchange capacity (CEC) was determined by extraction of cations (Ca²⁺, Mg²⁺, K⁺, Na⁺) with cobaltihexamine chloride (AFNOR, 1999). The total organic carbon and total nitrogen contents were determined by dry combustion after decarbonation (AFNOR, 1995). These analyses were carried out by the Regional Water Control Laboratory of the city of Limoges, accredited by the French authorities for soil and sludge analysis.

139

140

141

142

143

144

145

146

147

148

149

For elemental analyses, soil samples (100 mg) were mineralized by microwave (Milestone Ethos One) using an acid mixture of 1.5 mL HNO₃ (69%) / 4.5 mL HCl (37%) / 1 mL HF (49%) in PTFE vials. The microwave procedure, optimized in order to obtain residue-free mineralization, was as follows: 1) temperature ramp from 20°C to 200°C in 20 minutes, and 2) temperature stabilization at 200°C for 20 minutes. Total Ag, Cd, Cu and Pb concentrations were then determined by inductively coupled plasma-mass spectrometry (ICP-MS) equipped with an inert PFA sample introduction kit (Agilent 7900). The details of the ICP-MS parameters are described in Table S1. JSd-2 certified reference material (with certified concentrations of 1.04, 3.06, 1117 and 146 mg kg⁻¹ for Ag, Cd, Cu and Pb respectively) was used for validation. The recovery, calculated from 5 replicated analysis (whole protocol from mineralization to ICP-MS run), was (93±5), (99±5), (86±3) and (95±5)%, for Ag, Cd, Cu and Pb respectively. The associated quantification limits were 0.003, 0.007, 0.060 and 0.016 mg kg⁻¹ for Ag, Cd, Cu, and Pb, respectively.

150

151

152

153

154

155

156

157

158

159

160
## 2.5. Statistical analyses

The exploratory data analysis technique (Tukey, 1977) enabled the reference total elemental concentrations (median, minimum, maximum) to be calculated. This method is based on non-parametric statistics (i.e. statistics that do not depend on a law of probability) and makes it possible to obtain robust indicators (medians, quartiles, vibrissae) of the data set (Reimann et al., 2005). Cartograms of the study area were generated to visualize the distribution of the concentrations of studied elements relative to each sampling site, with a classification by quartiles. The dependence between the variables was sought by calculating the Spearman correlation coefficient ρ, in the form of a matrix. The significance of the association was checked by Fisher's transformation. Linear y = ax+b and exponential log y = log(a) x + log(b) regressions were used to deduce causal relationships on the origin of Ag in soils. The global significance of the models was verified by a Fisher test. Depending on the proximity of a potential source, the sites were divided into groups. A Wilcoxon-Mann-Whitney test was performed on these groups to compare their distributions and medians. Statistical analyses of the data were performed with R software version 4.0.3 (R Core Team, 2020).

## 2.6. Spatial and geostatistical analyses

To identify the origins of Ag in soils, different geographical data were used. We used the parent material information from the French soil geographical database (INRA, 2018) and its classification of dominant parent material in order to create groups of sites with similar lithology. Then we compared median of each group to decipher if different parent material is likely to give higher or lower Ag concentrations in derived soil. In addition, the geographical database SIG Mines France (BRGM, 2007) was used to collect information on the location and nature of (former) mines that exploited Ag and Pb and natural (unexploited) deposits of Ag in the study area. The distances between the mines and the sampling sites were calculated with ArcGIS 10.1. The distance-Ag concentration relationship was then modelled by the regressions described in section 2.5.

The location data of the anti-hail ground generator network (based on silver iodide) of the national association for the study and fight against atmospheric plagues (Association Nationale
d'Etude et de Lutte contre les Fléaux Atmosphériques, ANELFA) were also used for searching correlation with the Ag concentrations (ANELFA, 2020).

The geostatistical analyses were carried out from the calculation of the semivariograms. The semivariograms enable the structure (organization and regularity of the variable in the space considered) and the spatial variability of the data to be described. Thus, the semivariance $\gamma(h)$ of a measured variable is estimated between two points that are h apart, which makes it possible to assess the importance of the link (i.e. the degree of continuity) between these points for this variable (autocorrelation). They indicate the degradation of information between two measurement points for this variable as the distance between them increases. The evolution of the information is therefore represented by an increasing function up to a level defined by the maximum interpolation distance. Beyond this distance, two consecutive values become independent. The Equation 1 represents the calculation of semivariogram:

$$ \text{Eq (1)} \quad \gamma(h) = \frac{1}{2N(h)} \sum_{i=1}^{N(h)} \{[Z(x_i + h) - Z(x_i)]^2\} $$

where $\gamma(h)$ is the semivariance, $N(h)$ the number of point pairs, $h$ the distance between 2 points, $Z(x_i)$ is the element concentration at location $x_i$. The distance $h$ used for the experimental semivariograms is 16 km, which corresponds to the size of the RMQS grid cell. They were then fitted empirically by a mathematical function comprising a plateau. The fitting enables information on the spatial structure of the variable to be obtained thanks to the following parameters: the nugget effect (value of the ordinate at the origin; represents the share of variability lower than the sampling step and the measurement errors), the range (distance for which the plateau is reached and beyond which there is no longer any autocorrelation) and the sill (plateau) which represents the sum of the total variance and the nugget effect. The contribution of analytical measurement errors was estimated from the RSD values of the analyses of the certified material. The semivariogram was fitted by an experimental empirical mathematical model, by iterative weighting of the least squares, by minimizing the following Equation 2:

$$ \text{Eq (2)} \quad \sum_{j=1}^{n} N(h)(\hat{\gamma}(\bar{h}_j) - \gamma(\bar{h}_j))^2 $$
Where $\hat{\gamma}(\bar{h}_j)$ is the experimental value of the semivariance, and $\gamma(\bar{h}_j)$ the modelled values of the semivariance. For the interpretation of the semivariograms, the nugget effect/total variance ratio was calculated. This ratio represents the part of variability lower than the sampling step and that due to the error of measurement of the concentration compared to its total variance. The correlated variables were jointly analysed using cross-variograms. This is to verify that these variables are not only spatially autocorrelated but also that their spatial variabilities are correlated with each other. The cross-semivariograms are defined by the following equation 3 for co-located variables:

$$ \text{Eq (3)} \quad \gamma_{l,k}(h) = \frac{1}{2N(h)} \sum_{i=1}^{N(h)} \{[Z_l(x_i) - Z_l(x_i + h)][Z_k(x_i) - Z_k(x_i + h)]\} $$

Where $l$ and $k$ represent the co-variables. From cross-semivariograms it is thus possible to construct a co-regionalization model, i.e. to model the spatial structure of dependent co-variables. The Linear Model of Co-Regionalization (LMCR) is fitted jointly with the simple variogram models, to ensure the positivity of the covariance, with the same range fixed for all the variograms (Goovaerts, 1997). The co-regionalization model is fitted by the ordinary least squares method (without statistical weight).

From the auto-semivariogram model and the co-regionalization model, ordinary kriging (OK) and ordinary cokriging (CK) were performed and compared to determine the spatial distribution of Ag in SWF soils. The ordinary kriging linear estimator (Equation 4) is (Goovaerts, 1997):

$$ \text{Eq (4)} \quad \hat{Z}_0 = \sum_{i=1}^{n} \lambda_i Z(x_i) $$

With $\hat{Z}_{x_0}$ the variable to be predicted at an unsampled point $x_0$, $Z(x_i)$ the measured value, and $\lambda_i$ the statistical weights to be determined. The linear cokriging estimator is expressed as follows (Equation 5) (Goovaerts, 1997):

$$ \text{Eq (5)} \quad \hat{Z}_l = \sum_{i=1}^{n} \lambda_i Z_l(x_i) + \sum_{j=1}^{p} \lambda_j Z_k(x_j) $$

With $\hat{Z}_l$ the variable (main) to be predicted, $Z_l$ the main variable measured, $Z_k$, the measured auxiliary variable, $x_i$ and $x_j$ the known locations, $\lambda_i$ and $\lambda_j$ the weights to be determined.
237

From the cross-validations, the following criteria were used to compare the interpolation
results: mean error (ME), root mean square error (RMSE) and ratio between residues and variance (i.e.
238
239 Mean Squared Deviation Ratio, MSDR). All geostatistical calculations were performed with the gstat
240
241 software suite.

238

239

240

241

242

3. Results

243

3.1. Statistical description and characterization of soils

244

The statistical characteristics of the physico-chemical variables of the soils sampled and characterized, and available in the RMQS2 database are presented in Table 1. The texture of the samples (taken from the pit) is highly variable and reflects the diversity of soils in the region. The soils are either predominantly sandy because they correspond to the sandy soils of the Landes, or with balanced textures between the 3 fractions, or predominantly clayey. Organic matter is in very variable quantity, the minimum values representing rather the soils of arable crops and the maximum values, the soils of forests. The pH of the 35 soils covers almost the whole usual range of soil pH, ranging from 4 to 8. Acidic soils correspond more to sandy forest soils and the most basic soils to chalky soils.

245

246

247

248

249

250

251

252

The median value of Ag is close to its abundance in the earth's crust (0.07 mg kg⁻¹) (Table 4).
However, 3 extreme values differ from the others, which indicate an unusual behaviour in the
distribution of the concentrations of Ag and suggest contaminations whose source is to be identified
(Figure 2A). The medians of Cd, Cu, Pb from RMQS2 are lower than those from RMQS1, this is due
to the low number of samples analysed (35 vs. 356). Nevertheless, similar upper whiskers for the two
data sets are found (Table 2). The results obtained for Cd, Cu, Pb for RMQS1 have been described and
discussed in a previous publication (Savignan et al. 2021).

253

254

255

256

257

258

259

Spearman's correlation matrix indicates that Ag concentration is significantly correlated with
silt and clay fractions in soils (Figure 3). However, it is anti-correlated with the coarse sand fraction,
which indicates that sandy soils are depleted in Ag, as can be seen on the distribution map by quartiles
(Figure 2). Moreover, the Ag concentration is positively correlated with the Cu and Cd concentrations.
It is even more strongly correlated with the Pb concentration (r > 0.9), which suggests a common

260

261

262

263
264

distribution and origin for these two elements. Pb would thus be a more suitable auxiliary variable than Cd and Cu to carry out a cokriging of Ag concentrations.

265

266

## 3.2. Origins of silver in soils

267

Ag concentrations were compared according to the parent materials of the sampling sites. Values were divided in seven groups of dominant parent materials following the classification of the BDGSF (alluvial deposits, calcareous rocks, clayey materials, detrital formations, sandy materials, crystalline rocks). The performed Kruskal Wallis test indicates that there is a significant discrepancy of the median between those groups (p = 0.002). However, the pairwise Wilcoxon test indicates that only the median of Ag from sandy materials is significantly lower than medians of other parent material group (Table 3). There is no significant difference of the median for other groups, such as between calcareous rocks and crystalline rocks (p = 0.81).

268

269

270

271

272

273

274

275

The distribution of Ag concentrations was compared to the location of known Pb (and Ag) mines and geological deposits (Figure 2B). Visually, there is an association between Pb mines and deposits, and high Ag concentrations and anomalies in the soil. The exponential models calculated (log Ag as a function of the distance from the sites to the mines and deposits) to quantify this association are significant at the 1% threshold and confirm the observed trend (Figure 4). Thus, the distance between sites and Ag mines explains 39% of the total variance in Ag concentration, and the distance between sites and Ag deposits explains 40% of this variance. These results indicate that the distribution and origin of Ag in soils have a mixed origin that is both anthropogenic caused by pollution from mining activities, and natural dependent on the underlying source rock. Therefore, the choice of Pb as auxiliary variable to carry out a cokriging is confirmed to be relevant.

276

277

278

279

280

281

282

283

284

285

The distribution of Ag concentrations was compared to the location of AgI-based anti-hail ground generators (Figure 2C). Indeed, the south-west of France has many vineyards, orchards and market gardening which are particularly sensitive to hail. Thus, for several years, a network of anti-hail generators for crop protection has been developing in the study area. Despite the high density of this network (450 generators), only one extreme value in Ag appears near the network. This is the southernmost site in the study area, which is also under the influence of Ag-rich deposits. Moreover,

286

287

288

289

290
by selecting the 15 sites within 10 km of a generator (theoretical area of effect) and comparing them to
the other sites, the Wilcoxon-Mann-Whitney test does not show any significant difference between the
medians and distribution of the two groups (p = 0.25). Thus, given the low concentrations of Ag
found, this practice would not tend to increase the concentration of Ag in the soil.

The surveys carried out with farmers relate in particular to the history of the plot and the
technical itineraries implemented on the RMQS sites with agricultural occupation (crop succession
and meadows). Information on inputs, potentially sources of trace elements (mineral fertilizers,
phytosanitary products, organic inputs such as manure or sludge), was recorded for the 21 agricultural
plots sampled. It appears that 18 sites are subject to mineral fertilization, 8 to manure, 14 to
phytosanitary spreading, but none has received organic fertilization by sewage sludge. This source of
Ag contamination can therefore be excluded for the samples considered.

3.3. Spatial distribution of silver in soils

The strong correlation between Ag and Pb and the common origin of these elements makes it
possible to consider a cokriging using Pb as an auxiliary variable to establish the spatial distribution of
Ag in the soils of the SWF. Cokriging also requires covariates to be spatially autocorrelated
individually and crosswise. The auxiliary variable must therefore have a spatial structure similar to
that of Ag. The semivariograms of the elements from the RMQS2 show that Ag and Pb have a similar
spatial structure. This is not the case for Cd and Cu whose semivariograms have different models from
that of Ag (Figure S1). This is further confirmation that Pb is the most relevant variable to achieve
cokriging of Ag concentrations.

The RMQS1 data being complete on 356 sites, we studied the similarity of the Pb
concentrations measured during RMQS1 to those of RMQS2. The results of the linear regression show
a strong correlation (r = 0.95; Figure S2) between the Pb values of the two RMQS campaigns, despite
different sampling (pit vs. soil composite) and different analysis methods. Thus, the rest of the
geostatistical analyses were carried out with the lead data from the RMQS1. The concentrations were
transformed into log10 to obtain a data distribution, which approaches the normal law and more easily
comparable semivariance values.
318

319

320

321

322

323

324

325

326

327

328

For Ag, the modelled semivariogram follows a spherical model, without nugget effect, with a range of 141 km (Figure 5). The modelled lead variogram is also spherical, with a nugget effect and a range of 153 km. Their two similar ranges (8% difference) make it possible to calculate a cross-variogram and to establish a linear model of co-regionalization (Figure 6). The LMCR is fitted from a fixed range for all variograms. The lead variogram being more robust (higher number of point pairs), its range was assigned to the other variograms. The LMCR results give the variogram a nugget effect and a higher sill. Sill and nugget values also increase slightly for Pb (Figure 6). These nugget effects can be explained on the one hand by the measurement uncertainties (which represent 36% and 22% of the nugget effect for Ag and Pb, respectively); and on the other hand, for the most part, by the spatial variability lower than the sampling step. From these elements the interpolation of the Ag concentration by ordinary cokriging (CK) was calculated.

329

330

331

332

333

334

335

336

337

338

339

The quality of ordinary CK was then estimated by cross-validation and was compared, according to three criteria (ME, RMSE, MSDR), with that of ordinary kriging (OK) based on the values of the simple variogram of Ag (Figure 7). Two modalities were applied for the cross-validation of the CK: a first case where only the main variable (Ag) is removed, then a second case where the main and secondary variables (Ag and Pb) are removed. The ME of cokriging and kriging indicates an underestimation bias for CK and an overestimation bias for OK. For both cross-validation modalities, the RMSE of CK is closer to zero. The CK therefore gives better accuracy than the OK. Similarly, the MSDR of CK (without the main variable) is closer to 1 than that of OK. This indicates that the cross-validation residues are of the same order of magnitude as the prediction errors. Also, the range of variance is less with CK (Figure 7). These results show that the CK improves the quality of the interpolation in our study.

340

341

342

343

344

Figure 8 shows the spatial distribution map of Ag concentrations interpolated (and backtransformed) by cokriging. As expected, the highest concentrations are found in the northern zone, in the presence of mining activity or deposits containing Pb and Ag, in particular in the Deux-Sèvres department. Other areas also appear to be more concentrated in Ag, in particular near the city of Bordeaux and in the Gers department. This is due to the high concentrations of Pb of anthropogenic
345

origin (air pollution from lead gasoline) around the city of Bordeaux, which has a high population
density (Savignan et al., 2021).

346

347

4. Discussion

348

349

350

351

352

In this part, the concentrations of Ag found in soils are discussed with regard to the uses and origins of Ag. The spatial distribution of Ag determined by cokriging is then discussed: first, the spatial resolution of the information obtained with regard to the spatial resolution of the data used; then the interest of a preliminary distribution in comparison with a spatial distribution established in fine from a complete set of data.

353

4.1. Silver concentration levels in soils and its origin

354

355

356

357

358

359

360

361

362

The median concentration of Ag measured in our study (0.07 mg kg⁻¹) is close to that found in the European project GEMAS (0.04 mg kg⁻¹ in cultivated soils) but lower than that obtained by the European project FOREGS (0.27 mg kg⁻¹), including for its French part (0.3 mg kg⁻¹) (De Vos and Tarvainen, 2005; Reimann et al., 2018; Salpeteur and Maldan, 2011). This difference can be explained by the difference in sampling density of the networks and the low number of RMQS2 samples analysed. According to Figure 2A, there are still several sites to be sampled near mining areas or Pb deposits, which would be enriched in Ag. Thus, as the RMQS2 campaign progresses (expected end 2027) and chemical analyses, the distribution of Ag concentrations could change, and its median could increase.

363

364

365

366

367

368

369

370

371

The origin of Ag linked to that of lead, highlighted in our study, is known and documented. Silver-bearing galena (PbS) of hydrothermal origin is a historical source of Pb and Ag (Klein et al., 1985). In our study region, hydrothermal processes occurred in the northeast part notably in crystalline and metamorphic rocks (Figure 1B). This is associated with an area rich in metals and high mining activities, including Ag and Pb (Bossy, 2010; de Gramont and Braux, 1990). In the south part of the region soils derived from geological fold of the pyrenes orogenesis (Figure 1B). This region is also associated with historical mining activities of many metals (Corella et al., 2021) explaining the higher Ag interpolated concentration. Furthermore, the area most concentrated in Ag is linked to a historic mining dating from the Middle Ages, in Deux-Sèvres department, around the town of Melle.
372

Archaeological and geological work has shown that the exploitation was located on an extensive
deposit of 100 km², shallow, composed of siliceous limestone (Jurassic) with inclusions of silver-
bearing galena (Bailly-Maître and Benoît, 1997). The historical mining origins of Ag in soils have also
been highlighted in other regions of France (Vosges) and Europe (Krakow, Poland; Halkyn, Wales)
(Jones et al., 1983; Kowalska et al., 2016; Mariet et al., 2016). On the other hand, low values of Ag
identified in soils from sandy materials corresponds to sandy soils from the Landes de Gascogne
which are known to be generally poor in elements (Savignan et al., 2021).

373

374

375

376

377

378

379

The cloud seeding does not seem to increase the amount of Ag in soils. Similar results were
obtained in Greece where the supply of AgI was carried out by plane (Tsiouris et al., 2002). The
absence of visible contamination by this practice in our study area can be explained by the low
quantity of Ag used by the generators. The amount of Ag used by a generator is 9 g h⁻¹, for a
theoretical range of 10 km and an operating time that varies from 15 to 30 days per year depending on
the weather (Dessens et al., 2016). Considering these conditions and the following (overestimating)
hypothesis: if 50% of emitted Ag would reach the soil, over the surface covered by the 450 generators
of the region (4 million ha), then the deposition of Ag from cloud seeding would be between 0.18 and
0.36 g ha⁻¹ yr⁻¹. Nevertheless, more analyses on the other RMQS sites, and above all a long-term
monitoring of the dispersion of Ag by this process are necessary to confirm this result and also to
evaluate a possible effect of accumulation over time.

380

381

382

383

384

385

386

387

388

389

390

4.2. Spatial distribution by (co)kriging

391

Kriging combined with an RMQS sampling plan based on a 16×16 km grid (with a high nugget effect/sill ratio) give better estimates for low and medium concentration values (Roth, 1998).
Indeed, the sampling scheme used is not aimed at detecting localized point pollution (hotspots) but to give a regional view of the elemental distributions. We therefore expect not to detect high levels of Ag or Pb in our study, with the possible consequence of an underestimation of the concentrations measured and estimated by interpolation. If the origins of mainly geogenic and mining Ag in connection with Pb are confirmed by future sampling, it is expected that the main characteristics of the spatial structure will change little (spherical model and almost similar range). However, it is also

392

393

394

395

396

397

398
possible, for the next sampled sites, that new sources appear, for example contributions due to fertilization by sewage sludge or diffuse pollution of urban origin. (Lottermoser, 2012; Moeckel et al., 2020; Zuzolo et al., 2018). Therefore, the Ag content could be less strongly correlated with the Pb distribution, with a possibly different spatial structure. Such an evolution would then confirm the presence of other sources of contamination.

It is important to keep in mind that (i) the cokriging performed is based solely on the RMQS sampling sites; (ii) Pb results from RMQS1 were used as auxiliary variables to complete the RMQS2 sites not yet sampled and analysed, with the objective of obtaining the primary variable of Ag concentration. Our results show that CK improved the quality of the interpolation and illustrates the interest of its application to obtain an early spatial distribution of Ag in the soils of the SWF. Moreover, cokriging can remain interesting even once the RMQS2 campaign is completed, if it is confirmed that the 2 variables, Ag and Pb concentrations, vary spatially in the same way. Indeed in this case, cokriging, by taking into account a double data set, leads to an estimate of the spatial distribution a little more precise than the ordinary kriging (Helterbrand and Cressie, 1994).

## 5. Conclusion

The sampling campaigns of the national Soil Quality Measurement Network RMQS1 and RMQS2, allowed us to obtain the first information concerning Ag in the soils of southwestern France. From 35 RMQS2 samples already collected, the Ag concentrations were found to be close to the reference values of the earth's crust. Areas more concentrated in Ag were detected; they are and linked to natural deposits and historical mining of silver-bearing galena (PbS). No correlation could be demonstrated between the concentration of Ag and the high density of anti-hail ground generator. Using the strong dependence between the Pb and Ag concentrations, and all the data collected during the RMQS1, a model of co-regionalization was established from the cross-semivariograms of Ag and Pb. Then by cokriging, a spatial distribution of the Ag concentration was obtained, with improved precision compared to kriging. This study thus provides a first estimate of the concentration of Ag in soils, which can be useful for regional territorial decisions on environmental protection and food safety. Subsequently, this work can be supplemented by new samples from the RMQS2, not only in
the region studied but also at the national level. Since the RMQS also has the objective of preserving
state of the soil, analyses of the RMQS1 samples could be carried out in order to compare them and
highlight a possible accumulation of Ag over time. In addition, given the new uses of Ag in the form
of nanoparticles and the importance of soils in the life cycle of these materials, specific
characterization analyses of Ag-NPs could be carried out on the RMQS samples with the aim of
providing some answers to this emerging environmental issue.

## 432 Acknowledgments

Olivier Atteia is thanked for his advice and comments on the geostatistical analyses. Thanks to Claudy
Jolivet, Céline Ratié and Line Boulonne of unit INRAE US1106 InfoSol. We acknowledge the two
anonymous reviewers for their insightful comments on the manuscript. The RMQS program is funded
by the scientific interest group "GIS Sol", which brings together the Ministry of Ecological Transition
(MTES), the Ministry of Agriculture and Food (MAA), the National Research Institute for
Agriculture, Food and the Environment (INRAE), the Ecological Transition Agency (ADEME), the
Research Institute for Development (IRD), the National Geographic Institute (IGN), the French Office
of Biodiversity (OFB) and the Bureau of Geological and Mining Research (BRGM).

441
442 References

443 AFNOR, 2003. NF X31-107. Qualité du sol. Détermination de la distribution granulométrique des

444 particules du sol. Méthode à la pipette.

445 AFNOR, 1999. NF X31-130. Qualité des sols. Méthodes chimiques. Détermination de la capacité

446 d'échange cationique (CEC) et des cations extractibles.

447 AFNOR, 1995. NF ISO 10694. Qualité du sol. Dosage du carbone organique et du carbone total après

448 combustion sèche (analyse élémentaire).

449 AFNOR, 1994. ISO 10390. Qualité du sol. détermination du pH.

450 ANELFA, 2020. Anelfa : association nationale d'études et de lutte contre les fléaux atmosphériques -

451 Etudes et prévention grêle [WWW Document]. URL http://www.anelfa.asso.fr/ (accessed 5.11.20).

452 Arrouays, D., Jolivet, C., Boulonne, L., Bodineau, G., Ratié, C., Saby, N., Grolleau, E., 2003. Le

453 réseau de mesures de la qualité des sols (RMQS) de France. Etude Gest. Sols 10, 241-250.

454 Bailly-Maître, M.-C., Benoît, P., 1997. Les mines d'argent de la France médiévale. Actes Congrès

455 Société Hist. Médiév. Enseign. Supér. Public 28, 17-45. https://doi.org/10.3406/shmes.1997.1715

456 Bell, R.A., Kramer, J.R., 1999. Structural chemistry and geochemistry of silver-sulfur compounds:

457 Critical review. Environ. Toxicol. Chem. 18, 9-22. https://doi.org/10.1002/etc.5620180103

458 Bossy, A., 2010. Origines de l'arsenic dans les eaux, sols et sédiments du district aurifère de St-

459 Yrieix-la-Perche (Limousin, France) : contribution du lessivage des phases porteuses d'arsenic.

460 Université de Limoges, Limoges.

461 Bourgeois, A., Allard, D., Saby, N., Martin, M., Arrouays, D., 2012. Optimisation d'un

462 échantillonnage annualisé d'un réseau national de surveillance de la qualité des sols: Application aux

463 teneurs en carbone du RMQS, in: Le Sol Face Aux Changements Globaux. Presented at the 11e

464 Journée d'Etude des Sols, Versailles, pp. 222-223. https://doi.org/hal-02748472

465 BRGM, 2007. SIG Mines France [WWW Document]. URL http://sigminesfrance.brgm.fr/index.asp

466 (accessed 4.24.18).

467 Chen, J., Wei, F., Zheng, C., Wu, Y., Adriano, D.C., 1991. Background concentrations of elements in

468 soils of China. Water. Air. Soil Pollut. 57-58, 699-712. https://doi.org/10.1007/BF00282934

469 Chen, T., Chang, Q., Liu, J., Clevers, J.G.P.W., Kooistra, L., 2016. Identification of soil heavy metal

470 sources and improvement in spatial mapping based on soil spectral information: A case study in

471 northwest China. Sci. Total Environ. 565, 155-164. https://doi.org/10.1016/j.scitotenv.2016.04.163

472 Corella, J.P., Sierra, M.J., Garralón, A., Millán, R., Rodríguez-Alonso, J., Mata, M.P., de Vera, A.V.,

473 Moreno, A., González-Sampériz, P., Duval, B., Amouroux, D., Vivez, P., Cuevas, C.A., Adame, J.A.,

474 Wilhelm, B., Saiz-Lopez, A., Valero-Garcés, B.L., 2021. Recent and historical pollution legacy in

475 high altitude Lake Marboré (Central Pyrenees): A record of mining and smelting since pre-Roman

476 times in the Iberian Peninsula. Sci. Total Environ. 751, 141557.

477 https://doi.org/10.1016/j.scitotenv.2020.141557

478 Cornelis, G., Doolette, C., McLaughlin, M.J., Kirby, J.K., Beak, D.G., Chittleborough, D., 2012.

479 Retention and Dissolution of Engineered Silver Nanoparticles in Natural Soils. Soil Sci. Soc. Am. J.

480 76, 891. https://doi.org/10.2136/sssaj2011.0360

481 Courtois, P., Rorat, A., Lemiere, S., Guyoneaud, R., Attard, E., Levard, C., Vandenbulcke, F., 2019.

482 Ecotoxicology of silver nanoparticles and their derivatives introduced in soil with or without sewage

483 sludge: A review of effects on microorganisms, plants and animals. Environ. Pollut. 253, 578-598.

484 https://doi.org/10.1016/j.envpol.2019.07.053

485 de Gramont, X., Braux, C., 1990. Synthèse Sud Limousin (No. R 31814 DEX-DAM-90), Inventaire

486 des ressources minières du territoire métropolitain. BRGM, Aubière.
487 De Vos, W., Tarvainen, T. (Eds.), 2005. Geochemical atlas of Europe. Part 2. Interpretation of geochemical maps, additional tables, figures, maps, and related publications. Geological Survey of Finland, Espoo.

490 Dessens, J., Sánchez, J.L., Berthet, C., Hermida, L., Merino, A., 2016. Hail prevention by ground-based silver iodide generators: Results of historical and modern field projects. Atmospheric Res. 170, 98–111. https://doi.org/10.1016/j.atmosres.2015.11.008

493 Douay, F., Pruvot, C., Roussel, H., Ciesielski, H., Fourrier, H., Proix, N., Waterlot, C., 2008. Contamination of Urban Soils in an Area of Northern France Polluted by Dust Emissions of Two Smelters. Water. Air. Soil Pollut. 188, 247–260. https://doi.org/10.1007/s11270-007-9541-7

496 Eckelman, M.J., Graedel, T.E., 2007. Silver Emissions and their Environmental Impacts: A Multilevel Assessment. Environ. Sci. Technol. 41, 6283–6289. https://doi.org/10.1021/es062970d

498 Fernández-Caliani, J.C., Giráldez, M.I., Rivera, M.B., 2019. Source and geochemical partitioning of silver in a naturally-enriched soil. Appl. Geochem. 103, 85–96.

500 https://doi.org/10.1016/j.apgeochem.2019.02.010

501 Fromm, K.M., 2011. Give silver a shine. Nat. Chem. 3, 178–178. https://doi.org/10.1038/nchem.970

502 Garcia-Velasco, N., Gandariasbeitia, M., Irizar, A., Soto, M., 2016. Uptake route and resulting toxicity of silver nanoparticles in Eisenia fetida earthworm exposed through Standard OECD Tests.

504 Ecotoxicology 25, 1543–1555. https://doi.org/10.1007/s10646-016-1710-2

505 Geisler-Lee, J., Brooks, M., Gerfen, J.R., Wang, Q., Fotis, C., Sparer, A., Ma, X., Berg, R.H., Geisler, M., 2014. Reproductive Toxicity and Life History Study of Silver Nanoparticle Effect, Uptake and Transport in Arabidopsis thaliana. Nanomaterials 4, 301–318. https://doi.org/10.3390/nano4020301

507 Transport in Arabidopsis thaliana. Nanomaterials 4, 301–318. https://doi.org/10.3390/nano4020301

508 Golden, N., Zhang, C., Potito, A., Gibson, P.J., Bargary, N., Morrison, L., 2020. Use of ordinary cokriging with magnetic susceptibility for mapping lead concentrations in soils of an urban contaminated site. J. Soils Sediments 20, 1357–1370. https://doi.org/10.1007/s11368-019-02537-7

509 cokriging with magnetic susceptibility for mapping lead concentrations in soils of an urban contaminated site. J. Soils Sediments 20, 1357–1370. https://doi.org/10.1007/s11368-019-02537-7

511 Goovaerts, P., 1997. Geostatistics for Natural Recources Evaluation, Applied Geostatistics series.

512 Oxford University Press, New York.

513 Gottschalk, F., Sonderer, T., Scholz, R.W., Nowack, B., 2009. Modeled environmental concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CNT, fullerenes) for different regions. Environ. Sci.

514 Technol. 43, 9216–9222.

516 Helterbrand, J.D., Cressie, N., 1994. Universal cokriging under intrinsic coregionalization. Math.

517 Geol. 26, 205–226. https://doi.org/10.1007/BF02082764

518 Hooda, P.S. (Ed.), 2010. Trace elements in soils. Wiley, Chichester, West Sussex.

519 INRA, 2018. Base de Données Géographique des Sols de France à 1/1 000 000 version 3.2.8.0,

520 10/09/1998. https://doi.org/10.15454/BPN57S

521 Jolivet, C., Berché, P., Boulonne, L., Fontaine, M., Gouny, L., Lehman, S., Maître, B., Ratié, C.,

522 Schellenberger, E., Soler-Dominguez, N., 2017. Manuel du Réseau de mesure de la qualité des sols.

523 RMQS2 : deuxième campagne 2016-2027, Version 2. INRA, US 1106 InfoSol, Orléans.

524 Jolivet, C., Boulonne, L., Ratié, C., 2006. Manuel du Réseau de mesures de la qualité des sols, édition 2006. INRA, US 1106 InfoSol, Orléans.

525 2006. INRA, US 1106 InfoSol, Orléans.

526 Jones, K.C., Peterson, P.J., Davies, B.E., 1983. Silver concentrations in Welsh soils and their dispersal

527 from derelict mine sites. Miner. Environ. 5, 122–127. https://doi.org/10.1007/BF02093333

528 Klein, C., Hurlbut, C.S. (Cornelius S., Dana, J.D., 1985. Manual of mineralogy : (after James D.

529 Dana). New York : Wiley.

530 Kleja, D., Nakata, S., Persson, I., Gustafsson, J.P., 2016. Silver(I) Binding Properties of Organic Soil

531 Materials Are Different from Those of Isolated Humic Substances. Environ. Sci. Technol. 50, 7453–

532 7460. https://doi.org/10.1021/acs.est.6b00970
533

Kowalska, J., Mazurek, R., Gąsiorek, M., Setlak, M., Zaleski, T., Waroszewski, J., 2016. Soil

534

pollution indices conditioned by medieval metallurgical activity – A case study from Krakow

535

(Poland). Environ. Pollut. 218, 1023–1036. https://doi.org/10.1016/j.envpol.2016.08.053

536

Lorenz, C., Windler, L., von Goetz, N., Lehmann, R.P., Schuppler, M., Hungerbühler, K., Heuberger,

537

M., Nowack, B., 2012. Characterization of silver release from commercially available functional

538

(nano)textiles. Chemosphere 89, 817–824. https://doi.org/10.1016/j.chemosphere.2012.04.063

539

Lottermoser, B.G., 2012. Effect of long-term irrigation with sewage effluent on the metal content of

540

soils, Berlin, Germany. Environ. Geochem. Health 34, 67–76. https://doi.org/10.1007/s10653-011-

541

9391-5

542

Marchant, B.P., Saby, N.P.A., Arrouays, D., 2017. A survey of topsoil arsenic and mercury

543

concentrations across France. Chemosphere 181, 635–644.

544

https://doi.org/10.1016/j.chemosphere.2017.04.106

545

Marchant, B.P., Saby, N.P.A., Lark, R.M., Bellamy, P.H., Jolivet, C.C., Arrouays, D., 2010. Robust

546

analysis of soil properties at the national scale: cadmium content of French soils. Eur. J. Soil Sci. 61,

547

144–152. https://doi.org/10.1111/j.1365-2389.2009.01212.x

548

Mariet, A.-L., de Vaufleury, A., Bégeot, C., Walter-Simonnet, A.-V., Gimbert, F., 2016. Palaeo-

549

pollution from mining activities in the Vosges Mountains: 1000 years and still bioavailable. Environ.

550

Pollut. 214, 575–584. https://doi.org/10.1016/j.envpol.2016.04.073

551

Moeckel, C., Breivik, K., Nøst, T.H., Sankoh, A., Jones, K.C., Sweetman, A., 2020. Soil pollution at a

552

major West African E-waste recycling site: Contamination pathways and implications for potential

553

mitigation strategies. Environ. Int. 137, 105563. https://doi.org/10.1016/j.envint.2020.105563

554

Morvan, X., Saby, N.P.A., Arrouays, D., Le Bas, C., Jones, R.J.A., Verheijen, F.G.A., Bellamy, P.H.,

555

Stephens, M., Kibblewhite, M.G., 2008. Soil monitoring in Europe: A review of existing systems and

556

requirements for harmonisation. Sci. Total Environ. 391, 1–12.

557

https://doi.org/10.1016/j.scitotenv.2007.10.046

558

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691.

559

https://doi.org/10.1016/j.cageo.2004.03.012

560

Peters, R.J.B., Bouwmeester, H., Gottardo, S., Amenta, V., Arena, M., Brandhoff, P., Marvin, H.J.P.,

561

Mech, A., Moniz, F.B., Pesudo, L.Q., Rauscher, H., Schoonjans, R., Undas, A.K., Vettori, M.V.,

562

Weigel, S., Aschberger, K., 2016. Nanomaterials for products and application in agriculture, feed and

563

food. Trends Food Sci. Technol. 54, 155–164. https://doi.org/10.1016/j.tifs.2016.06.008

564

R Core Team, 2020. R: A language and environment for statistical computing.

565

Ratte, H.T., 1999. Bioaccumulation and toxicity of silver compounds: a review. Environ. Toxicol.

566

Chem. 18, 89–108.

567

Reimann, C., de Caritat, P., 2017. Establishing geochemical background variation and threshold

568

values for 59 elements in Australian surface soil. Sci. Total Environ. 578, 633–648.

569

https://doi.org/10.1016/j.scitotenv.2016.11.010

570

Reimann, C., Fabian, K., Birke, M., Filzmoser, P., Demetriades, A., Négrel, P., Oorts, K., Matschullat,

571

J., de Caritat, P., Albanese, S., Anderson, M., Baritz, R., Batista, M.J., Bel-Ian, A., Cicchella, D., De

572

Vivo, B., De Vos, W., Dinelli, E., Ďuriš, M., Dusza-Dobek, A., Eggen, O.A., Eklund, M., Ernsten, V.,

573

Flight, D.M.A., Forrester, S., Fügedi, U., Gilucis, A., Gosar, M., Gregorauskiene, V., De Groot, W.,

574

Gulan, A., Halamić, J., Haslinger, E., Hayoz, P., Hoogewerff, J., Hrvatovic, H., Husnjak, S., Jähne-

575

Klingberg, F., Janik, L., Jordan, G., Kaminari, M., Kirby, J., Klos, V., Kwećko, P., Kuti, L.,

576

Ladenberger, A., Lima, A., Locutura, J., Lucivjansky, P., Mann, A., Mackovych, D., McLaughlin, M.,

577

Malyuk, B.I., Maquil, R., Meuli, R.G., Mol, G., O’Connor, P., Ottesen, R.T., Pasnieczna, A., Petersell,

578

V., Pfleiderer, S., Poňavič, M., Prazeres, C., Radusinović, S., Rauch, U., Salpeteur, I., Scanlon, R.,

579

Schedl, A., Scheib, A., Schoeters, I., Šefčik, P., Sellersjö, E., Slaninka, I., Soriano-Disla, J.M., Šorša,

580

A., Svrkota, R., Stafilov, T., Tarvainen, T., Tendavilov, V., Valera, P., Verougstraete, V., Vidojević,

581

D., Zissimos, A., Zomeni, Z., Sadeghi, M., 2018. GEMAS: Establishing geochemical background and
582 threshold for 53 chemical elements in European agricultural soil. Appl. Geochem., SI: ISEG 2016 88,
583 302–318. https://doi.org/10.1016/j.apgeochem.2017.01.021

584 Reimann, C., Filzmoser, P., Garrett, R.G., 2005. Background and threshold: critical comparison of
585 methods of determination. Sci. Total Environ. 346, 1–16.
586 https://doi.org/10.1016/j.scitotenv.2004.11.023

587 Rosenbaum, M.S., Söderström, M., 1996. Cokriging of Heavy Metals as an Aid to Biogeochemical
588 Mapping. Acta Agric. Scand. Sect. B - Soil Plant Sci. 46, 1–8.
589 https://doi.org/10.1080/09064719609410940

590 Roth, C., 1998. Is Lognormal Kriging Suitable for Local Estimation? Math. Geol. 30, 999–1009.
591 https://doi.org/10.1023/A:1021733609645

592 Saby, N., Arrouays, D., Boulonne, L., Jolivet, C., Pochot, A., 2006. Geostatistical assessment of Pb in
593 soil around Paris, France. Sci. Total Environ. 367, 212–221.
594 https://doi.org/10.1016/j.scitotenv.2005.11.028

595 Salpeteur, I., Maldan, F., 2011. French geochemical baseline data for trace elements in top- and
596 bottom-soils and overbanks of the shield areas and sediment covers: A low density survey in the
597 FOREGS Geochemical Atlas of Europe (II). Environ. Risques Santé 299–315.
598 https://doi.org/10.1684/ers.2011.0479

599 Savignan, L., Lee, A., Coynel, A., Jalabert, S., Faucher, S., Lespes, G., Chéry, P., 2021. Spatial
600 distribution of trace elements in the soils of south-western France and identification of natural and
601 anthropogenic sources. CATENA 205, 105446. https://doi.org/10.1016/j.catena.2021.105446

602 Schlich, K., Hoppe, M., Kraas, M., Schubert, J., Chanana, M., Hund-Rinke, K., 2018. Long-term
603 effects of three different silver sulfide nanomaterials, silver nitrate and bulk silver sulfide on soil
604 microorganisms and plants. Environ. Pollut. 242, 1850–1859.
605 https://doi.org/10.1016/j.envpol.2018.07.082

606 Sikora, F.J., Stevenson, F.J., 1988. Silver complexation by humic substances: Conditional stability
607 constants and nature of reactive sites. Geoderma 42, 353–363. https://doi.org/10.1016/0016-
608 7061(88)90011-0

609 Smith, D.B., Cannon, W.F., Woodruff, L.G., Solano, F., Ellefsen, K.J., 2014. Geochemical and
610 mineralogical maps for soils of the conterminous United States (Report No. 2014–1082), Open-File
611 Report. Reston, VA. https://doi.org/10.3133/ofr20141082

612 The Silver Institute, 2019. World Silver Survey 2019. The Silver Institute, Washington, D.C.

613 Tsiouris, S.E., Aravanopoulos, F.A., Papadoyannis, I.N., Sofoniou, M.K., Polyzopoulos, N.,
614 Christodoulou, M., Samanidou, V.F., Zachariadis, G.A., Constantinidou, H.-I.A., 2002. Soil silver
615 content of agricultural areas subjected to cloud seeding with silver iodide. Fresenius Environ. Bull. 11,
616 697–702.

617 Tukey, J.W., 1977. Exploratory data analysis, Addison-Wesley series in behavioral science :
618 quantitative methods. Addison-Wesley, Reading.

619 Wedepohl, H., 1995. The composition of the continental crust. Geochim. Cosmochim. Acta 59, 1217–
620 1232. https://doi.org/10.1016/0016-7037(95)00038-2

621 Wu, J., Norvell, W.A., Welch, R.M., 2006. Kriging on highly skewed data for DTPA-extractable soil
622 Zn with auxiliary information for pH and organic carbon. Geoderma 134, 187–199.
623 https://doi.org/10.1016/j.geoderma.2005.11.002

624 Zuzolo, D., Cicchella, D., Doherty, A.L., Albanese, S., Lima, A., De Vivo, B., 2018. The distribution
625 of precious metals (Au, Ag, Pt, and Pd) in the soils of the Campania Region (Italy). J. Geochem.
626 Explor. 192, 33–44. https://doi.org/10.1016/j.gexplo.2018.03.009

627
Caption of Figures

Figure 1:

A) Administrative map of the study area B) RMQS network grid over the study area. Red dots represent the 35 samples from RMQS2 campaign used for Ag determination. RMQS1 sites were sampled on all dots (356), dominant parental materials are represented in background, extracted from Soil Geographical Data Base of France (BDGSF) at scale 1:1000000 (INRA, 2018). C) Representation of the sampling scheme on site. Grey dots correspond to the sub-square sampled during the RMQS1 campaign. Green dots correspond to the sub-square sampled during the RMQS2 campaign. Other sub-squares (3,4) are designed for further sampling campaign. Sketch modified from Jolivet et al. (2017).

Figure 2:

Maps of point data distribution from RMQS2 samples. A) Ag concentration classified by quartiles. B) Ag concentration overlapped with localisation of mines and mineral deposits (citation). C) Ag concentration overlapped with localisation of AgI anti-hail ground generator for cloud seeding provided by the ANELFA (ANELFA, 2020).

Figure 3:

Matrix of correlations with pedological variables and metal variables (RMQS2 samples). Positive correlations are shaded in blue, negative correlations are shaded in red. Small cross indicates that the correlation is not significant (Fisher test; *p* > 0.05).

Figure 4:

Regression analysis (RMQS2 samples) between A) Ag concentration (log transformed) and distance from Pb mines, B) Ag concentration (log transformed) and Pb deposits. Above figures of regression, Fisher test (F value) for statistical significance and coefficient of determination (R²) for the quality of the fit are described.

Figure 5:

Experimental and modelled auto-semivariograms of Ag (RMQS2) and Pb (RMQS1) with concentration values transformed by log10 function.

Figure 6:

Cross-semivariograms and auto-semivariograms of Ag (RMQS2), Pb (RMQS1) with fitted model of co-regionalisation. Dashed line represents the total variance, dotted line symbolises the measurement error variance. Semivariance values are expressed in log10 transformation of Ag or Pb concentration in mg² kg⁻².

Figure 7:

Results of cross-validation of kriging techniques with variance maps. Ordinary kriging results are based on RMQS2 samples while cokriging results are based on RMQS1 and RMQS2 samples. ME is the Mean Error, RMSE the Root Mean Squared, and MSDR the Mean Squared Deviation.

Figure 8:

Spatial distribution of Ag in soils of SWF, computed by cokriging with Pb (RMQS1) as co-variable. Values are backtransformed from log10 transformation.
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

C

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

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /></figure>

Legend

Concentration Ag (quartiles)

mg/kg

● [Minimum (0.015) - Quartile 1 (0.038)]

● [Quartile 1 - Median (0.081)]

● [Median - Quartile 3 (0.096)]

● [Quartile 3 - Upper whisker (0.182)]

● > Upper whisker (0.222)

Mines

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

Cloud seeding

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

Others

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

Administrative boundaries
<figure><img src="image_10.png" /></figure>
A

<table><thead><tr><td>Degrees of freedom</td><td>F value</td><td>p-value</td><td>R<sup>2</sup></td><td>r</td></tr></thead><tbody><tr><td>33</td><td>20.63</td><td>7e-04</td><td>0.385</td><td>0.62</td></tr></tbody></table>

B

<table><thead><tr><td>Degrees of freedom</td><td>F value</td><td>p-value</td><td>R<sup>2</sup></td><td>r</td></tr></thead><tbody><tr><td>33</td><td>20.63</td><td>7e-04</td><td>0.385</td><td>0.62</td></tr></tbody></table>

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

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

<table><thead><tr><td>Model</td><td>Nugget</td><td>Sill</td><td>Range (m)</td></tr></thead><tbody><tr><td>Spherical</td><td>0</td><td>0.11</td><td>141719</td></tr></tbody></table>

Pb

<table><thead><tr><td>Model</td><td>Nugget</td><td>Sill</td><td>Range (m)</td></tr></thead><tbody><tr><td>Spherical</td><td>0.037</td><td>0.092</td><td>153361</td></tr></tbody></table>

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

<figure><img src="image_14.png" /></figure>
<figure><img src="image_15.png" /></figure>

<table><thead><tr><th></th><th>Ag</th><th>Pb</th><th>Ag.Pb</th></tr></thead><tbody><tr><td>Model</td><td>Spherical</td><td>Spherical</td><td>Spherical</td></tr><tr><td>Nugget</td><td>0.015</td><td>0.042</td><td>0.025</td></tr><tr><td>Sill</td><td>0.13</td><td>0.1</td><td>0.09</td></tr><tr><td>Range (m)</td><td>153361</td><td>153361</td><td>153361</td></tr><tr><td>Nugget/var(%)</td><td>14</td><td>28</td><td>-</td></tr><tr><td>Measurement error/nugget (%)</td><td>36</td><td>22</td><td>-</td></tr></tbody></table>

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

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

Ag variable
removed

All variables
removed

ME

-0.0043

-0.00438

RMSE

0.13

0.19

MSDR

0.96

0.82

<figure><img src="image_18.png" /></figure>

Ag variance (CK)

Max : 0.0034

Min : 1.1e-5

Ordinary Kriging

ME

0.0038

RMSE

0.25

MSDR

1.40

<figure><img src="image_19.png" /></figure>

Ag variance (OK)

Max : 0.0055

Min : 3.3e-7
<figure><img src="image_20.png" /></figure>

Cokriged Ag
concentration
mg/kg
<table><caption>Table 1: Descriptive statistics of granulometry, pH, carbon, nitrogen, carbonate and CEC of the 35 RMQS2 sampled soils (from the pit).</caption><thead><tr><th></th><th>Clay (%)</th><th>Fine silt (%)</th><th>Coarse silt (%)</th><th>Fine sand (%)</th><th>Coarse sand (%)</th><th>Organic matter (%)</th><th>Organic Carbon (%)</th><th>Total nitrogen (%)</th><th>C/N</th><th>pH water</th><th>Carbonates (%)</th><th>CEC (cmol<sub>c</sub> kg<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Min</td><td>0.6</td><td>0.1</td><td>0.4</td><td>1.5</td><td>1.5</td><td>0.3</td><td>0.17</td><td>0.1</td><td>7.3</td><td>3.9</td><td>0</td><td>2.4</td></tr><tr><td>1st Quartile</td><td>9.2</td><td>4.7</td><td>4.2</td><td>8.2</td><td>9.7</td><td>2</td><td>1.16</td><td>0.9</td><td>9</td><td>4.3</td><td>0</td><td>6.3</td></tr><tr><td>Median</td><td>16.2</td><td>19.4</td><td>11.7</td><td>12.9</td><td>28.4</td><td>3.2</td><td>1.86</td><td>1.67</td><td>10.3</td><td>5.6</td><td>0.1</td><td>11.2</td></tr><tr><td>3rd Quartile</td><td>24.6</td><td>27.6</td><td>19</td><td>15.6</td><td>68.7</td><td>5.2</td><td>3.02</td><td>2.6</td><td>17.4</td><td>6.8</td><td>0.4</td><td>22</td></tr><tr><td>Max</td><td>53.5</td><td>42</td><td>28.7</td><td>34.1</td><td>95.9</td><td>18.5</td><td>10.73</td><td>5.3</td><td>46.4</td><td>8.3</td><td>75.5</td><td>123.2</td></tr></tbody></table>
<table><caption>Table 1: Descriptive statistics of Ag, Cd, Cu and Pb total concentrations in soils (mg kg⁻¹) and assessment of anomalous values. Cd, Cu, Pb (RMQS1) values were previously published in Savignan et al. (2021).</caption><thead><tr><th rowspan="2">Campaign</th><th colspan="4">RMQS2</th><th colspan="3">RMQS1</th></tr><tr><th>Ag</th><th>Cd</th><th>Cu</th><th>Pb</th><th>Cd</th><th>Cu</th><th>Pb</th></tr></thead><tbody><tr><td>Number of values</td><td>35</td><td>35</td><td>35</td><td>35</td><td>356</td><td>356</td><td>356</td></tr><tr><td>Min</td><td>0.015</td><td>0.004</td><td>0.20</td><td>3.0</td><td>0.01</td><td>0.5</td><td>3.1</td></tr><tr><td>1st Quartile</td><td>0.038</td><td>0.039</td><td>3.61</td><td>13.1</td><td>0.06</td><td>6.0</td><td>19.0</td></tr><tr><td>Median</td><td>0.081</td><td>0.122</td><td>11.05</td><td>23.2</td><td>0.15</td><td>13.1</td><td>27.1</td></tr><tr><td>3rd Quartile</td><td>0.096</td><td>0.219</td><td>22.48</td><td>33.9</td><td>0.28</td><td>22.9</td><td>37.9</td></tr><tr><td>Upper whisker</td><td>0.182</td><td>0.490</td><td>50.79</td><td>65.0</td><td>0.60</td><td>48.3</td><td>66.3</td></tr><tr><td>Max</td><td>0.222</td><td>1.044</td><td>111.81</td><td>93.5</td><td>2.1</td><td>491</td><td>261.0</td></tr><tr><td>Number of values > upper whisker</td><td>3</td><td>4</td><td>3</td><td>3</td><td>35</td><td>42</td><td>23</td></tr></tbody></table>
<table><caption>Table 1: Results of Wilcoxon pairwise tests, performed to compare medians according to the group of parent material. Letter *a* indicates a significant difference (p < 0.05) between the median of Ag of the groups. Abbreviation n.s. means non-significant.</caption><thead><tr><th>Parent material</th><th>Crystalline rocks</th><th>Alluvial deposits</th><th>Calcareous rocks</th><th>Clayey materials</th><th>Detrital formations</th><th>Sandy materials</th></tr></thead><tbody><tr><td>n</td><td>6</td><td>6</td><td>4</td><td>6</td><td>4</td><td>9</td></tr><tr><td>Median (mg/kg)</td><td>0.090</td><td>0.090</td><td>0.065</td><td>0.092</td><td>0.093</td><td>0.024</td></tr><tr><th>Pairwise Wilcoxon test</th><th>Crystalline rocks</th><th>Alluvial deposits</th><th>Calcareous rocks</th><th>Clayey materials</th><th>Detrital formations</th><th></th></tr><tr><td>Alluvial deposits</td><td>n.s.</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Calcareous rocks</td><td>n.s.</td><td>n.s.</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Clayey materials</td><td>n.s.</td><td>n.s.</td><td>n.s.</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Detrital formations</td><td>n.s.</td><td>n.s.</td><td>n.s.</td><td>n.s.</td><td>—</td><td>—</td></tr><tr><td>Sandy materials</td><td>a</td><td>a</td><td>n.s.</td><td>a</td><td>a</td><td>a</td></tr></tbody></table>