Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area

Journal Pre-proofs Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area Juliana A. Galhardi, Bruno P. Leles, Jaime W.V. de Mello, Kevin J. Wilkinson PII: DOI: Reference:

S0048-9697(19)34475-4 https://doi.org/10.1016/j.scitotenv.2019.134484 STOTEN 134484

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Science of the Total Environment

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8 July 2019 2 September 2019 14 September 2019

Please cite this article as: J.A. Galhardi, B.P. Leles, J.W.V. de Mello, K.J. Wilkinson, Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134484

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Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area Juliana A. Galhardi1*, Bruno P. Leles2, Jaime W.V. de Mello3, Kevin J. Wilkinson1

1. Biophysical Environmental Chemistry Group, Department of Chemistry, University of Montreal, Montreal, Quebec, Canada. H3C 3J7. 2. Department of Ecology, São Paulo State University, Rio Claro - SP, Brazil. 3. Soil Chemistry and Environmental Geochemistry group, Department of Soil, Federal University of Viçosa, Viçosa, MG, Brazil.

* Corresponding author - [email protected] Abstract In order to assess the environmental risks related to mining activities in Southern Brazil, the transfer of trace metals and rare earth elements (REE) from soils to soybeans was evaluated in a U-rich area associated with coal mining. In some samples, As, Ba, Co, Cu and Ni were higher than the guidelines proposed by the Brazilian environmental agency. Soil, coal, ash, tailings and soybean were systematically sampled so that the chemical fractionation/speciation of the elements could be related to their bioavailability. In addition to total concentrations quantified by ICP-MS after microwave digestion, elemental measurements were made following different evaluations of the bioavailable metal, including chemical extractions (10 mM Ca(NO3)2 and 3-step sequential extraction), diffusive gradient in thin films technique (DGT) and chemical modeling (WHAM-free ion). Lower pH and higher clay and organic matter content were reflected by higher metal assimilation by the plants, especially by the roots and leaves. The bioaccumulation factor (BF) was generally higher for the leaves (e.g. Cu, Mn, Sr, Zn, Ba, REE with exception of Tm and Yb) and roots (e.g. Cd, Th and U). The results revealed that for Ba, Cd, Sr, Pb, U and most of the REE, the free ion concentration was strongly correlated with the metal content in the plants, especially for the grains. Values obtained by DGT were also correlated with the bioavailable portion of Ba, Mn, Sr, Zn, Pb, U and REE. Measurements obtained from Ca extractions correlated well with the bioavailable metals for Ba, Cd, Sr, Rb, Pb and Th. The free or extractable metal fractions gave much better correlations of the bioavailable fractions than did the total metal concentrations from the soils, especially for the REE. The paper validates some simplified means of estimating the risks associated with metals and REE in tropical soils affected by mining activities. Keywords: bioaccumulation; trace metals; rare earth metals; mining; tropical soils; metal mobility.

1. Introduction Mining and mineral processing generate large volumes of waste rock and tailings, which may act as sources of persistent toxic metals long after the mining operation ceases (Abraham et al., 2018). Metal mobility to the environment can be increased following their dissolution and leaching from tailings (Li et al., 2019), often due to acid effluents from the mines (Campaner et al., 2014; Prudêncio et al., 2015). As a consequence, the bioavailability of metals to plants; their subsequent transfer to the food chain; and their risks to human health can be increased (Zhuang et al., 2009, 2017; Adamu et al., 2015). In addition to the more commonly investigated metals such as Pb, Cd, Cu, Ni and Zn, rare earth elements (REE) are getting increased attention (Wang and Liang, 2014; Yang et al., 2017; Censi et al., 2017), largely due to their increased use in the high tech sectors (Gosen et al., 2014) and their applications in agriculture (Tyler, 2004; Carpenter et al., 2015). REE include the lanthanides, with atomic numbers from 57 to 71 (La to Lu), in addition to Sc and Y. REE have been shown to be enriched in coals and certain agricultural soils (Noack et al., 2014; Mayfield and Fairbrother, 2015), but their bioavailabilities are largely unknown (Zhao and Wilkinson, 2015; Rowell et al., 2018). Since the REE are known to enter the human body via ingestion (Carpenter et al., 2015; Amyot et al., 2017), it is important to investigate their concentration levels in crops, especially in areas strongly influenced by mining (Zhuang et al., 2017). In most countries, risk assessments and concentration threshold values are based on total metal concentrations (Alvarenga et al., 2014), in spite of the fact that they are generally a poor predictor of metal availability to plants. Indeed, soil-to-plant transfers of metals are strongly influenced by the chemical speciation of the metals and the physicochemical characteristics of the soil (Fernández-Ondoño et al., 2017). Unfortunately, it is generally experimentally difficult to unambiguously determine the bioavailable forms of metals in the complex soil matrix (Kim et al., 2015; Khan et al., 2017). Thermodynamic calculations (WHAM: Windermere Humic Aqueous Model; e.g. Hernandez-Soriano and Jimenez-Lopez 2012, NICA: Non ideal competitive adsorption-Donnan model; e.g. Kim et al., 2015),

chemical extractions (e.g. Vázquez et al., 2016) and analytical speciation measurements (DGT: diffusion gradient in thin films technique, e.g. Williams et al., 2012, ISE: ion selective electrodes; e.g. Benoit et al., 2013) have nonetheless been used to estimate metal bioavailability in agricultural soils. Further site-specific investigations are required in order to better understand the role of site characteristics and biogeochemical processes on the mobility of metals in soil-plant systems. Of particular interest are investigations performed in tropical zones (Xu et al., 2013; Alvarenga et al., 2014), which are large producers of food and ore minerals, but where climatic conditions, weathering processes, nutrient cycling, soil properties and metal biouptake can differ significantly from what is found in temperate zones (IAEA, 2010). For example, to date, no systematic investigation has been performed in cultivated tropical soils that are affected by mining activities, particularly in Brazil, despite that the country is the tenth largest vegetable producer and the second largest soybean producer in the world (FAO, 2012). Therefore, this paper aims to: 1) assess the transfer of trace and rare earth elements from soils to soybeans in an U-rich coal mining area in Southern Brazil; 2) compare the performance of different techniques (chemical extractions, DGT, chemical modeling) to effectively monitor the availability of trace and rare earth elements in tropical soils; 3) relate the speciation of the trace and rare earth elements in the soils to their biological availability; 4) investigate the relative importance of the chemical speciation of the metals and the physicochemical properties of soils on metal bioavailability; and 5) better understand the main factors controlling metal bioavailability in tropical agricultural soils. Our main working hypothesis was that measurements of free ion in the soils would generally provide the best indicator of metal concentrations in the plants.

2. Material and methods 2.1 Characteristics of the study area Coal is the most important source of non-renewable fuel used to generate electricity in Brazil. It is nonetheless known to be a source of radionuclides and trace elements, which can potentially be leached

into nearby soils and waters (Galhardi et al., 2017). Paraná State is the second largest producer of soybean and has the third largest coal reserves in the country. The coal mining area (Fig. 1) studied in this paper was centered at Figueira city, in the northern part of the state. The soils in the area are predominantly shallow, acidic, with poor drainage and a high saturation of exchangeable aluminum (Morrone and Daemon, 1985). The major crops are soybean and corn, which are mainly exported. Waters from the Laranjinha River (the main river in the region) and the tributary where the mining effluents used to be discharged (Pedras stream) are used for agricultural irrigation. Coal mining activities have taken place over the past several decades, in both open and underground pits, although presently only one underground mine is operational. The environmental impacts are mainly linked to the disposal of tailings (Galhardi and Bonotto, 2016), although metals and radionuclides are also emitted from the thermal, coal fired power plant in Figueira city (Flues et al., 2006).

2.2 Sampling and analysis Soya (Glycine max) (n = 22) was collected from cultivated lands close to the mine. Leaves (SL1SL22), roots (SR1-SR22) and grains (SG1-SG22) were sampled during the harvest period. Two kg of soil (S1-S22) was collected at the same points as the plants, at a depth of 20 cm using a manual collector and then stored in plastic containers. All plants and soils were collected in triplicate within a 10 m diameter sampling zone. Coal (n = 2) was sampled from the active underground mine, as were tailings (n = 3) and ash tailings (n = 2) were collected from the filters of the thermal power plant. Finally, four of the main commercial fertilizers that are produced and applied to Brazilian soils (mainly for soybeans) were also sampled (FTL1-FTL4). Leaves and roots were rinsed with deionized water, oven-dried at 60 °C until a constant dry weight and then ground into a uniform powder. Soils, tailings, coal ash and coal were dried at 60 °C until constant weight. Soil samples were sieved through a 2 mm nylon mesh and ground to a fine powder. Soil properties were analyzed according to standard methods (pH - ISO 10390 (ISO, 2005); electrical

conductivity (EC) - ISO 11265 (ISO, 1994); cation exchange capacity (CEC) - ISO 23470 (ISO, 2018); organic carbon (OC) - ISO 10694 (ISO, 1995); nitrogen – Kjeldhal digestion method; phosphate and potassium – Mehlich extractor) in the Pedology Department at the University of Viçosa, Brazil. Trace elements and REE were measured by inductively coupled plasma mass spectrometry (ICP-MS, Perkin Nexion 300x). For the plants, approximately 0.25 g of each sub-sample (grain, leaf and root) was digested (DigiPrepMS digestor) in a 15-mL polyethylene tube containing 3.5 mL of concentrated ultrapure nitric acid (65 % v/v) and 0.5 mL of H2O2 (30% v/v) at 85 oC. Samples were digested overnight or until the solution became transparent and homogeneous and then diluted to 12 mL in Milli-Q water (R18 M cm; TOC2 g C L-1). Prior to their analysis by ICP-MS, solutions were diluted further (10x) with Milli-Q water in order to reach a HNO3 concentration of 1% v/v. Soils, tailings, coal ash, coal and fertilizers were digested in a microwave system according to the method EPA 3051A (USEPA, 2007), for which 1 g of the solid samples were digested in 9 mL of concentrated nitric acid (ultrapure, 65 % v/v) and 1 mL of concentrated (ultrapure, 37 % v/v) hydrochloric acid. After digestion, the solutions were diluted using Milli-Q water; ICP-MS analysis was performed on samples that were 1% v/v HNO3. For ICP-MS analysis, indium was used as the internal standard in order to compensate for signal drift. An external calibration was performed using standard solutions in the range of 0-500 μg L-1. A certified reference material, CSM-1 (Inorganic Ventures) and a quality control standard (Ctrl. Std. 4, SCP Science), were used to validate the measurements. Recoveries were consistently between 90% and 110%.

2.3 Metal fractionation Sequential extraction procedures, which involve exposing the soil to a series of chemical agents of increasing extraction strength, have been applied to gain insight into the chemical nature of sorption sites and the potential bioavailability of metals associated with the soils (Tang et al., 2018, Hasan et al., 2018). Metal fractionation was assessed using the modified BCR (Community Bureau of Reference) three-step

sequential extraction method, which has been extensively used for Cd, Cr, Cu, Ni, Pb and Zn (Ure et al., 1993). The typical aqua regia digestion, corresponding to a forth step (F4 – residual fraction), was not used. In summary, 1 g of the soils were first treated with 40 mL of 0.11 mol L-1 acetic acid (HAc) at room temperature for 16 h, under continuous agitation. Samples were then centrifuged at 3000 rpm (1882 x g) for 5 min. (Heraeus Multifuge 1 S-R, Kendro, Germany) and the supernatant was collected. This acid soluble fraction (F1) is most often attributed to the water-soluble, exchangeable and carbonate-bound metal fractions. Extraction of remaining solid phase with 40 mL of a 0.5 mol L-1 hydroxylamine hydrochloride solution at pH 1.5 (16 h, 22 oC) gives metals that are operationally defined as being bound to Fe and Mn oxides. Fraction F2 was collected after centrifugation (1882 x g, 5 min.) and the solid residue was treated with 10 mL of 30% H2O2 for 1 h at room temperature, under continuous agitation. Following a second addition of H2O2, the sample was heated to 85 °C where it was maintained until the solution was completely evaporated. The solid residue was treated with 50 mL of 1 mol L-1 ammonium acetate (pH 2) for 16 h at room temperature, under continuous agitation. Finally, the supernatant was carefully separated by centrifugation and analyzed in order to determine F3 (operationally defined as the metals bounded to sulfides and organic matter, i.e. metals released under strongly oxidizing conditions). In addition to the 3 step sequential extraction, a single step extraction was also performed using Ca(NO3)2 (e.g. Boshoff et al., 2014; Yao et al., 2017). Forty mL of 10 mM Ca(NO3)2 was added to 1 g of soil and the sample was shaken for 16 h. The solution was subsequently centrifuged for 5 min. at 3000 rpm (1882 x g). The supernatant was acidified with ultrapure HNO3 to give a final concentration of 1% v/v. Trace elements and REE were determined, as described above.

2.4 Metal speciation: DGT and WHAM Bioavailability may be best related to the flux of the kinetically labile metals from the soil to the plant (Hooda and Zhang, 2008), which can be evaluated using the diffusion gradient in thin films technique

(DGT) (Davison and Zhang, 1994). Similar to plant roots, the DGT probes cause a decrease of the local concentrations of bioavailable metal in the soil solution (Dočekalová et al., 2012). They are thought to provide a time-averaged concentration of metal species that contribute to the diffusive flux, which includes the free ion and labile complexes (Degryse et al., 2009). Although several research groups have demonstrated a good correlation between metal contents in plants and those found by DGT (e.g. Gramlich et al., 2017; Peng et al., 2017), its effectiveness for evaluating metal availability in agricultural soils is still under debate (Senila et al., 2012). DGT devices were cleaned with 2% HNO3 (24 h) and washed 7x with ultrapure water. The acetate membrane filters were washed in 2% HNO3 for 6 h and then rinsed with ultrapure water until they reached pH 6.0. Approximately 25 g of soil was added to a Petri dish and hydrated to maximum saturation (72 h, 20 °C). DGTs were deployed in the hydrated soils for 24 h, then removed, briefly rinsed with ultrapure water and dismantled. Metals in the DGT resins were extracted using 1.5 mL of 1 M HNO3 for 24 h under constant stirring. Solutions were diluted with ultrapure water to 1% HNO3 for analysis by ICP-MS. Following DGT retrieval, soil solutions were collected by centrifuging the soil paste (10 g) for 20 min. at 3000 rpm (1882 x g). The supernatant was filtered (0.45 μm), acidified with ultrapure HNO3 to 1% v/v and then analyzed by ICP-MS. A second aliquot of the porewater was used to determine the dissolved organic carbon (DOC) following acidification in 5% H3PO4. Free ion concentrations of the soil porewaters were calculated using WHAM/ Model VI version 7.0.4. Dissolved organic matter (DOM) was estimated based upon the DOC concentrations (Almås et al., 2006) and assumed to have the default ion-binding properties of a fulvic acid (i.e. 50% carbon by weight; Myrvang et al., 2016).

2.5 Evaluation of the impacts of the metals Soil-to-plant transfer was evaluated using the bioaccumulation factor (BF), which is obtained from the ratio of the metal content in the plant (on a dry weight basis) to that in the soil (both the total content

and the bioavailable or extractible metal concentrations) (e.g. Vandenhove et al., 2014; Zhang et al., 2018). Translocation factors (TF) in the soybean were calculated from the ratio of the metal concentration in the roots to that in the shoots (Mehes-Smith et al., 2014). Enrichment factors (EF) were used to distinguish between the natural and anthropogenic contributions by normalizing measured concentrations in the topsoil by the concentration of a reference element (Eq. 1, Hasan et al., 2018). In this paper, Al was used as the reference element since it is particularly stable and conservative in the soils and commonly found associated with clay minerals in the area (Galhardi et al., 2017). The EF is defined as:

(𝐴𝑙𝑀) 𝐸𝐹 = 𝑀 (𝐴𝑙)

Eq. 1

𝑖

𝑛

where (M/Al)i is the ratio of the mean concentration of a given metal and Al in the sample and (M/Al)n is its ratio in the upper continental earth’s crust (Wedepohl, 1995).

2.6 Statistical analysis Standard descriptive statistics (mean, maximum, minimum, standard deviation) were calculated for the concentrations of metals, soil properties and other parameters. Multivariate statistical approaches were used to reveal relationships in the soil-plant system, for example, between soil properties, bioavailable metal fractions and metal assimilation by the soybean. A Pearson’s correlation coefficient (p < 0.05) was first used to identify significant relationships after first excluding metal concentrations that were below detection limits. A Student t-test was used to establish significant differences between two groups of samples, for example, between the bioavailable content of the metals found by two different techniques or the bioavailable portion of a metal in the soil versus the metal content in the plants. Principal component analysis (PCA) was used to identify significant metal groupings. Statistical analysis was performed using XLSTAT (Microsoft Excel 10).

2.7 Quality control All of the chemicals were at least reagent grade. Milli-Q water had a resistivity of ≥18 M cm and a carbon content ≤2 g C L-1. Prior to use, polymerware and glassware were soaked in 2% HNO3 for at least 24 h then rinsed 7x with MilliQ-water. All samples were measured in triplicate. Blanks and standard reference materials were used for quality control during the entire analytical process. For example, NIST SRM 1515 and NIST SRM 1573a (National Institute of Standards and Technology) were used to validate the digestion of the plants, whereas NIST 2710, NIST 2711A and NIST 8704 (purchased from Sigma Aldrich) were used to validate the digestion/extraction of the soils.

3. Results and Discussion 3.1 Analytical merit Although we recognize that REE are metals, in the following discussion, we have used the terms REE to distinguish between elements with atomic masses from 57 to 71, and trace metals for the remaining elements, in order to avoid making the text unnecessarily difficult to follow. Limits of detection determined in the plants ranged from 0.021 (U) to 3.78 μg kg-1 (Zn) for the trace metals and 0.0062 (Tm) to 27.9 μg kg-1 (Ho) for the REE. For the soils, the limits of detection of the REE ranged from 0.0042 (Pr) to 22.3 μg kg-1 (Ho) and for the trace metals, they ranged from 0.014 (U) to 2.53 μg kg-1 (Zn). For the plants, recoveries ranged from 86% (U) to 107% (Cr) for the metals and 75% (Nd) to 89% (La) for the REE. For the soil reference materials, recoveries ranged from 82% (Ba) to 110% (Cd) for the metals and 79% (Sm) to 97% (Ce) for the REE. For REE that were not certified in the soils (Pr, Gd to Lu) or plants (Pr, Dy to Lu), the variability among the different recoveries (n = 10) was less than 12%, demonstrating a reproducible performance of the digestion procedures (Bosco-Santos et al., 2017). Although similar recoveries of the REE have been found by other researchers (e.g. Dołȩgowska and Migaszewski, 2013),

slightly higher recoveries have also been documented by researchers using stronger acid solutions (e.g. HF) for the digestions (e.g. Li et al., 2013; Wang and Liang, 2014).

3.2 Characteristics of the soils and metal accumulation Descriptive characteristics of the soils, including the concentrations of trace elements and REE are presented as mean values in Table 1 and as raw data in Tables S1-1 to S1-3. The mean pH of the soils was 5.5 ± 1 with more than 80% of the pH values below 7, indicating that they were predominantly acidic, likely due to the weathering of sulphide minerals (e.g. FeS2, FeAsS, CuFeS2, ZnS, PbS2) associated with the coal deposits (Abraham et al., 2018). The cation exchange capacity (CEC), as well as the N, P and organic carbon contents were consistent with the definition of a fertile soil. DOC concentrations in the soil porewaters were 9.3 ± 3.8 mg C L-1. The texture of the soil was mainly clay loam, followed by sandy clay loam and loam. Soil pH was negatively correlated with the DOC of the soil solutions, while the clay content was positively correlated with the DOC (n = 22, p < 0.05), i.e. high clay containing soils generally had more organic matter and lower pH (Fig. 2). In several samples, metal concentrations were higher than those proposed by guidelines established by CONAMA (2011) (Table 1), i.e. As (S14, S16), Ba (S12, S17), Co (S12, S17), Cu (S1) and Ni (S17). The concentrations of several of the metals were positively correlated, including Pb, Th, U, Zn, Cd, Rb, V and Co. Indeed, Cd concentrations correlated positively with all of the elements, except Sr. Other positive correlations among the metals, REE and the soil properties can be found in Table S2-1. The total metal content in the soils did not correlate with pH. The organic content of the soils exhibited a positive correlation with Sr, Rb and U, while the DOC of the porewater showed a positive correlation with Cu, Rb, Pb and Th. Nutrient concentrations were correlated with some of the metals, e.g. N with Sr, Rb, Pb, Th and U; K with Cd, Cr, V, Zn and Th. In addition, the CEC was correlated with Cd, Sr, Rb, Zn, Pb, Th and U. The positive correlation between Sr, Pb, Th and U and the soil properties (CEC, clay, N, OC) was strong, demonstrating the importance of the soil characteristics on the geochemistry of these metals (Gray

and Mclaren, 2006). Indeed, several elements (Cd, Cu, Rb, Zn, Pb, Th, U) were positively correlated with the clay fraction of the soil, which is logical, given its importance with respect to metal sorption (Yutong et al., 2016). For the REE, the total concentration in the topsoils averaged 52.8 mg kg-1, with values ranging from 18 mg kg-1 to 155 mg kg-1, in agreement with previous data for Brazilian soils found within the proximity of a uranium-phosphate deposit (139 mg kg-1; Cunha et al., 2018). REE concentrations in the soils generally decreased from the light to heavy elements (Table 1) in agreement with Wiche et al. (2017). Indeed, the LREE represented about 91% of the REE in the soils (LREE/HREE = 9.4) and some REE, such as La and Ce, were more abundant than many of the trace metals, including As, Cd, Co, Th and U. REE were positively correlated with several of the trace elements (Table S2-2), including Th, Pb, Zn, V, Rb, Ni, Mn, Co, Cd, Ba and Cr and Cu, providing some evidence that they had similar inputs or common geochemical characteristics (Soltani et al., 2014). REE (except Eu) were positively correlated with the clay content of the soils (Table S2-1). On the other hand, there was a relatively poor correlation between REE concentrations, pH and OC content. Cunha et al. (2018) have suggested that P and U can be used as proxies for REE concentrations in Brazilian soils, however, this relationship was not strong in the present study. Indeed, U was only significantly correlated with Ho and Lu. REE mobility in soils has previously been shown to depend on the clay mineral, organic matter and Fe oxide contents (Dołȩgowska and Migaszewski, 2013), however, low correlations were found here. Due to their low inherent solubility (Li et al., 2013), complexation with carbonates and humic substances is known to enhance REE mobility (Zhuang et al., 2017), whereas phosphate complexation leads to decreased REE solubility (Ding et al., 2005). Enrichment factors (EF) provide an indication of the severity of the contamination with values of 0.5 ≤ EF ≤ 1.5 indicating natural weathering processes and values EF>1.5 suggesting a significant anthropogenic contribution (Wang and Liang, 2014). Based upon the mean values of EF and consistent with prior results (Campaner et al., 2014), significant anthropogenic contributions of As, Cr, Co, Cu, Cd,

Mn, Ni, V, Zn, Pb, Th, U, Gd and Ho were observed for several of the topsoils (Table 2). Several of these elements, such as As, Cr, Cd, Pb and U, are of great concern to both the human and natural ecosystems, due largely to their inherent toxicities. In the context of climate change where alternating storm and flood events, dry seasons, crop and forest fires occur, the mobilization of those elements poses a real threat to the living organisms (Abraham et al., 2018). REE can accumulate in surficial soils via atmospheric deposition, mining activities and the application of fertilizers (Wang and Liang, 2014). Indeed, positive correlations relating K and N with Ce, Pr, Nd, Sm, Eu, Yb and Lu (Table S2-1) indicate a potential source from fertilizers. Nonetheless, with the exception of Ce, Gd and Ho, EF factors were smaller than 1.5, suggesting that this source was probably not of great importance. There is no reason to believe that the observed (high) Ho concentrations were due to an artifact. Ho measurements ranged from 0.74 mg kg-1 to 3.5 mg kg-1, averaging 1.7 mg kg-1, which was slightly higher than the North American Shale Composite (NASC) (1.2 mg kg-1), Post Archean Australian Shale (PAAS) (1.053 mg kg-1) and mean values of the upper continental crust (0.62 mg kg-1; Wedepohl, 1995). Variation of the Ho concentrations between sample replicates was less than 10% for all samples, and results acquired using a sector field ICP-MS (AttoM ES, Nu Instruments) gave statistically similar concentrations.

3.3 Metal speciation and fractionation Ba, Sr, and Zn were mainly found in the operationally defined exchangeable fraction (F1), whereas As, Co, Mn, Rb, Pb and U appeared to be more associated with the oxides (F2) (Table 3 with detailed information in Tables S1-4 to S1-9). Most of the Cd, Cr, Cu, Ni, V, Th and Pb Ba and Sr was extracted in the H2O2 fraction (F3). Zn was fairly equally distributed among the three extractable fractions, with a slightly higher value observed for the exchangeable fraction, consistent with previous results on agricultural soils (Vázquez et al., 2016). Pb was also equally found among F2 and F3.

Correlations among the extracted elements and correlations between the metals and soil properties have been presented in Tables S2-3 to S2-5. For example, several elements showed a positive correlation with U, including As and Th. In the exchangeable metal fraction (F1), metal concentrations were strongly (p0.05) correlated with pH, DOC and clay content: pH was negatively correlated with extracted Th; DOC exhibited a positive correlation with Zn, Pb and Th and the clay content was positively correlated with Cd, Cu, Pb and Th. These data demonstrated that numerous metals, but especially Th, were more available in the acidic, clayey and DOC rich soils. These findings are consistent with the overall correlations observed among pH, clay and DOC content in the soils (Fig. 2, above). Th was also strongly correlated with Pb. Similar relationships were found for fraction 2. For example, pH correlated negatively with Th; positive correlations were found between Cu, Rb and Th and DOC; and Cu, Rb, Pb and Th and the clay content. For fraction 3, Cu was positively correlated with DOC; U and Rb were positively correlated with OC; and Cu, Rb, Zn, Pb and U were positively correlated with the clay content. The strong correlations with pH and DOC content will have implications on the mobility of the metals in the soils by influencing adsorption and complexation (Kim et al., 2015). While pH, clay and organic matter content were strongly linked to the fractionation of numerous metals in the soils, there was generally a negative correlation between the sand content and trace metal concentrations, including for Ba, Co, Cr, Cu, Mn, Sr, Rb, Zn, Pb and U. Among the REE, La, Pr, Sm and Eu were mostly associated with the operationally defined oxide fraction (F2), while Ce, Nd, Eu, Gd, Dy, Ho, Er, Tm, Yb, Lu were mainly found in the oxidizable fraction (F3). Eu was equally found among F2 and F3. Ho, which exhibited overall higher than expected concentrations (with respect to the other REE), was also strongly associated with the oxidizable fraction (F3) although a substantial fraction was found in the exchangeable fraction, F1 (Table 3). REE concentrations extracted in F1 were generally positively correlated with each other (Table S2-6), however, extracted Ce, Dy, Er, Tm and Yb concentrations correlated negatively with pH while Tm and

Yb concentrations correlated positively with soil clay content (Table S2-3), indicative of an increased mobility in acidic, clayey soils. The total REE content was positively correlated with F2 (with exception of La and Ho) (Table S2-4) and F3 (except Pr). In F3, the REE concentrations (except Pr and Nd) were negatively correlated with the sand concentration and the clay content was positively correlated with extracted La, Pr, Sm and Eu (Table S2-5).

3.4 Metals and REE in the tailings, coal, coal ash and fertilizers High concentrations of metals and REE in the coal, coal ash, tailings and fertilizers are of high environmental concern, since they can be an important source of contamination to the surrounding environment. The concentrations of metals and REE are given in Table 4 for the ashes of a thermal power plant, for tailings and coal sampled at a local mine and for four commonly used fertilizers. Of particular interest, very high concentrations of arsenic, Mn, Zn and Pb were found in the coal and coal ash. The coal generally had higher concentrations of metals than the ash, with the notable exceptions of Cd, Cr and V. In the tailings, mean concentrations of As, Ba, Mn and U were higher than those found either in the coal or coal ash. Finally, mean Cd concentrations in the coal ash and tailings were higher than what was found in the coal, representing an important potential source of contamination to the soils and natural waters of the area. LREE concentrations were also generally higher in the coal as compared to the coal ash, however, the opposite was observed for the HREE (with exception of Ho). LREE represented a majority of REE in the ashes (72%), coal (81%) and tailings (75%), corresponding to an enrichment (LREE/HREE) of 3.0. LREE enrichment levels in the soils most closely agreed with those observed in the coal, potentially indicating that the soils may have been influenced by mining activities (Kolker et al., 2017). The four fertilizers showed fairly different ranges of concentrations for each of the metals, with generally higher concentrations of toxic metals in the first product (i.e. As, Cd, Cr, Rb, V, U), higher concentrations of micronutrients in FTL-2 (Ba, Co, Cu, Mn, Ni, Zn, (Pb)) and higher concentrations of Th

in FTL-3. Three of the fertilizers (FTL-1, FTL-2, FTL-3) had higher concentrations of U and Th than were found in the tailings, coal and coal ash. The presence of REE is well documented in fertilizers (Pang et al. 2001) and indeed, three of the four fertilizers (FTL-2, FTL-3 and FTL-4) had higher concentrations of REE than was found in the coal, coal ashes or tailing samples. The mean concentration of LREE in the fertilizers was 1160 mg kg-1 (96% of the REE, mainly due to the high amounts of La and Ce), 47.8 mg kg1 of

HREE were measured (with a preponderant contribution of Ho), indicating a much higher enrichment

of LREE than in the other analyzed matrices (LREE/HREE = 24.3).

3.5 Trace and rare earth elements in the soya Mean values for the concentrations of the metals in the soya are presented as in Fig. 3 with raw data given in Tables S1-10 to S1-15. Grains showed the highest concentrations of Ni and Rb; leaves showed the highest concentrations of Cr, Mn, Sr, Zn, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er and Lu; while the roots presented the highest content of As, Ba, Cd, Co, Cu, V, Pb, Th, U, Tm and Yb. REE were preferentially concentrated in the leaves, with the exception of Tm and Yb, which were more concentrated in the roots. REE averaged 0.76 mg kg-1 in the grains, 7.24 mg kg-1 in the leaves and 4.48 mg kg-1 in the roots (Fig. 3). Interestingly, the leaves (80%) and roots (79%) accumulated LREE to a greater extent than HREE, whereas HREE accounted for 96% of the REE in the grains (Fig. 4), although this was largely due to the very high Ho concentrations in that fraction (Fig. 3). Mean concentrations of LREE in the grains, leaves and roots were 3.4 mg kg-1, 74.4 mg kg-1 and 74 mg kg-1, respectively, with LREE/HREE ratios of 0.04, 3.77 and 3.42. Preferential interactions with phosphates in the rhizosphere may explain the higher REE accumulation in the roots (Censi et al., 2017; Ding et al., 2005). Finally, there was a strong codependency observed for the REE. For example, with exception of Lu and Ho, REE were positively correlated with each other and with U, Th, Pb, Zn, V, Ni, Mn, Cu, Cr, Co, Cd and As in the roots, indicating a common input source and common geochemical characteristics of these elements, which is reflected in their assimilation by the plants (Xinde et al., 2000; Khan et al., 2017). Numerous other correlations can be observed in Tables S2-7 to S2-9.

The Cu and Zn contents in the plants were generally greater than those leached from the soil, which could be attributed to the use of mineral fertilizers (François et al., 2009). There were no significant differences in the accumulation of Cu in the grains, leaves and roots (t-test, p > 0.05), nor were there significant differences in the accumulation of As, Ba, Cd, Co, Cr and Ni in the roots and leaves (t-test, p  0.05). Similarly, for the REE, with exception of La and Ho, no significant differences were observed between the accumulation of REE in the leaves and roots (t-test, p = 0.05). Principal Component Analysis (PCA) was performed for each of the plant organs (Fig. 5). For roots, leaves and grains, U, Th and Pb were associated, while As, U, Th and Pb were associated in the grains and leaves. These associations reflect the presence of the U-rich coal, As-rich pyrite and Pb in the geological strata of the region (Campaner et al., 2014). REE were grouped together in leaves and grains, indicating that they were accumulated in the leaves and grains following their assimilation by the roots (Brioschi et al., 2013; Carpenter et al., 2015). The release of U, Th, As, Pb and REE to the environment and their subsequent assimilation by organisms can be intensified by mining activities (Galhardi et al., 2017). In the roots, the concentrations of As, Ba, Cu and U were positively correlated with their concentrations in the soil. A similar result was noted for As, Ba, Cd, Mn and Zn in the leaves and Cd in the grains (Tables S2-10 to S2-12). The adsorption and accumulation of the metals by the plants depends upon many factors including temperature, moisture, organic matter, pH, and nutrient availability (Stojanovic et al., 2012; Liu et al., 2015). Indeed, strong correlations were found between the soil properties and the metal concentrations in the plants (Tables S2-13 to S2-15). Of interest, lower values of pH and higher values of clay content, organic matter content and CEC were reflected by higher metal contents in the plant organs, especially the roots and leaves. OM was positively correlated with Zn, Pb, Th, La and Ce and DOC was positively correlated with Cd, Co, Cu, Mn, Ni, Rb, Zn, Pb, Th, La, Ce and Ho. In the leaves, positive correlations were found for: (i) CEC and LREE; (ii) clay content and Pb, Cd, Ni, Th and U; (iii) OC and Cr, Ni, Pb and U and (iv) DOC and Mn, Ni, Rb, Pb and U. A negative correlation was found between pH and Cd, Cu, Sr and Zn. In the roots, a positive correlation was found

between: (i) clay content and Ba, Cd, Cu, Ni, Zn, Pb, Th, U, REE and (ii) CEC and several REE including La, Ce, Pr, Nd, Sm, Eu and Gd. Weaker correlations were observed when relating metal contents in the grains with the soil properties (Tables S2-13 to S2-15). Mean values of the bioconcentration factor (n = 22) were presented in Fig. 6 (detailed information is given in Table S1-16). BF were generally higher for the leaves (e.g. Cu, Mn, Sr, Zn, Ba, REE with the exception of Tm and Yb) and roots (e.g. Cd, Th and U). As for mobility, the bioavailability of the metals is largely controlled by their adsorption to the soils, which in turn is related to the properties of the soil, including pH, CEC, organic matter, clay mineral and oxide content (James et al., 2004; Gao et al., 2016; Antoniadis et al., 2017). Indeed, pH is known to have a major influence on metal bioavailability, due to its combined effects on the solubility and speciation of most metals (Gray and Mclaren, 2006; Muhammad et al., 2012; Reijonen et al., 2016). A negative correlation between soil pH and metal mobility and bioavailability in plants has been documented previously (Carpenter et al., 2015). Similarly, dissolved organic matter in soils can increase both the mobility and uptake of metals by plant roots (e.g. HernandezSoriano and Jimenez-Lopez, 2012), including Cd and Pb (Reijonen et al., 2016). Indeed, in this study, metal assimilation by the plants increased with the clay and organic matter contents of the soil and with decreasing pH, similar to previously observed correlations (Fig. 2). For example, in the grains, clay content was positively correlated with the BF for Mn, Ni, Th and U; pH was negatively correlated with the BF for Ba, Cd, Ni, Sr, Pb, LREE, Gd, Dy, Ho and Yb; OC showed a positive correlation with BF for Mn, Ni and Th and DOC was positively correlated with BF for Cr, Mn, Ni, V, Pb, Th, U and the REE (with exception of Sm, Ho, Er, Tm and Lu). In the roots, OC was positively correlated with the BF for Ni, Pb, La, Ce, Pr and LREE, while DOC was correlated with the BF for Co, Mn, Ni, Pb, Ce, Gd and the LREE (Tables S2-16 to S2-18). pH was negatively correlated with Ni and Sr. Finally, for the leaves, OC was positively correlated with BF for Cr, Ni and Mn. In this region, U mineralization has been associated with the presence of coal (Medeiros and Thomaz Filho, 1973). Previous studies have shown that in the nearby vicinity of the mine and thermal power plant,

U was assimilated by the crops (Galhardi et al., 2017). In the present study, a strong correlation between the concentration of U and the total organic carbon has been observed, similar to previous studies (Regenspurg et al., 2010). Indeed, Boghi et al. (2018) found that complexation with organic acids could increase the solubility of U in the soil and, as a consequence, its assimilation by plants. Translocation has also been enhanced, by the exudation of citrate, which has been shown to increase U uptake and increase translocation from the roots to the shoots and leaves (Laurette et al., 2012; Henner et al., 2018). In the rhizosphere, the release of organic acids from the roots and from natural organic matter can decrease the pH (~ 5.5) and increase metal complexation, potentially increasing the metal bioavailability in the studied soils. Nutrient contents also appeared to affect the metal bioavailability. For example, in the grains, phosphate showed a negative correlation with BF for As, Cu, Zn, Eu, Er, Lu and the HREE. Phosphate also showed a negative correlation with Cu and Zn in the leaves and Cu, Sr and Rb in the roots. Phosphate can reduce metal mobility and bioavailability in soils through the formation of insoluble precipitates under a wide range of environmental conditions (Song et al., 2009). The REE are especially sensitive to phosphate such that the observed decrease in phosphate concentrations from the roots to the shoots might be sufficient to explain the smaller values of REE observed in the grains (Ding et al., 2005). Finally, based on the TF calculated for soya (transfer from roots to leaves, roots to grains or leaves to grains), most of the metals were preferentially translocated and concentrated in the aerial tissues with a preferential path from the roots to the leaves (Fig. 7, detailed data in Table S1-17).

3.6 Bioavailability of the metals and the REE The bioavailability of metals depends on the specific physicochemical properties of both the metal and the soil (Fernández-Ondoño et al., 2017; Peijnenburg et al., 2007) in addition to the biological characteristics of the organism(s) being tested (Degryse et al., 2009; Liang et al., 2013). In this paper, the bioavailable fraction of the metals was evaluated using five different techniques: (i) total concentration in

soils; (ii) sequential extraction; (iii) Ca(NO3)2 extraction; (iv) DGT measurements and (v) thermodynamic modelling. Few studies have related metal speciation or fractionation to their bioavailability in tropical soils, where the accumulation of OC is generally low due to high weathering rates. Statistical analyses of the soil properties and measurements of the metal availability assessed using the different extraction techniques indicated that the physicochemical characteristics of the soil, especially the OC and DOC, influenced the performance of the different tested method (Tables S2-19 – S2-21). The comparison between the total content of the elements in the soils and their called bioavailable portions indicate similar trends among the different techniques (Tables S2-22 to S2-25; Pearson’s correlation matrix, p < 0.05). For example, the free concentrations of Ba, Co, Cu and Mn, DGT measurements of Ba, Co, Cu, Mn, Nd, Eu, Gd, Dy, Er, Tm and Lu and the Ca-extractable fractions of As, Ba, Co, Cu, Mn, Sr, Rb and Zn were all positively correlated with the total metal content in the soils. Table 5 presents Pearson’s correlation coefficients that relate the bioavailable metal fractions determined with the different techniques to the metal content in the plant parts, with significant correlations (p < 0.05) being indicated in bold. The results revealed that for Ba, Cd, Sr, Pb, U and most of the REE, the free ion concentration was strongly correlated with the metal content in the grains, with significant correlations also observed for the leaves (Ba, Cd, Mn, Zn, Pb) and the roots (Ba, Cd, Cu, Mn, Ni, Zn, Pb, Ce and Gd). In general, values obtained by DGT were also correlated with the bioavailable portions for the REE, Ba, Mn, Sr, Ni, Zn, Pb and U. Similarly, the metals extracted using Ca were well correlated with the bioavailable fraction of Ba, Cd, Sr, Rb, Pb and Th that was measured for the leaves, roots and grains. They also showed strong correlations for the REE found in the leaves and for the HREE measured in the roots. When compared with total metal concentrations from the soils, increased numbers of strong correlations were found between results from the speciation/fraction techniques and metal concentrations in the plants (grains: Cd, leaves: As, Ba, Cd, Mn and Zn, roots: As, Ba, Cu and U). Based upon the average value of the Pearson’s correlation coefficient (Table 5), DGT determined concentrations were the most strongly correlated with concentrations from the grains, roots and leaves, while the Ca extractions gave excellent correlations the

metal concentrations in the roots. In all cases, the free or extractable metal fractions gave much better correlations of the bioavailable metal fractions than did the total metal concentrations from the soils, especially for the REE, where total soil metal content showed no significant correlations with the plant parts. The bioavailability results are consistent with previous work (e.g. Sun et al., 2014; Vázquez et al., 2016; Fernández-Ondoño et al., 2017) that indicated strong correlations between the metals in plants and those measured using chemical extractions and the DGT.

4. Conclusions and Environmental Implications It is important to understand how trace elements are accumulated by edible plants, especially those in high weathered soils, as were examined here. In that context, this is the first study evaluating the uptake of REE by vegetables cultivated in agricultural soils that have been affected by coal mining in Brazil and, indeed, to our knowledge, the first which examines coal mining areas in South America. For tropical soils, few BF measurements are available and indeed, data are rare for the rare earths and actinides. For the available data on REE uptake by plants, little attention has been paid to the fractionation of the REE during the bioaccumulation process or during REE migration from the roots to aerial portions of the plants. In this study, high concentrations of REE and metals, in particular As, Mn, Zn and Pb, were measured in the coal, coal ash, tailings and fertilizers. The LREE represented a majority of the REE in the ashes, coal and tailings, which was reflected by enrichment levels detected in the soils, leaves and roots. These observations are suggestive of potential impacts of mining activities on the agricultural fields. Potential environmental and health risks were identified for Zn and Mn, based upon the large calculated value of their translocation factor in combination with their high observed mobility and high enrichment factor. The soil properties appeared to strongly influence the bioavailability of the metals and REE. In general, higher pH, clay content, organic matter content and CEC were reflected by higher metal concentrations in the plant, especially in the roots and leaves. Nonetheless, for many elements, total metal

concentrations in the soils were correlated with levels in the plants, e.g., As, Ba, Cu and U in the case of the roots; As, Ba, Cd, Mn and Zn for the leaves and Cd for the grains. If the study was intended to identify a ‘best’ technique for determining bioavailable metal, comparison among the different fractionation/speciation techniques showed fairly similar trends and strength of correlation. Nonetheless, when normalizing the BF by the metal fractions obtained by the chemical extraction techniques (rather than total metal concentrations) a greater number of elements showed significant correlations with the plant parts. This was especially the case for the DGT, where significant improvements were shown. For both the trace and rare earth elements, concentrations obtained from DGT, chemical extractions and chemical modeling were positively correlated with the bioavailable metal content of the plants, due to their transfer from the contaminated soils to the edible portion of soybean. These techniques could thus provide a simplified means of determining the bioavailable exposure levels of the contaminants, which could be used to estimate the environmental risks and risks to human health of these metals. The results nonetheless reinforce the need to perform more research associated with metals in soils affected by mining activities in order to better investigate the mechanisms that influence the bioavailability of metals during their transfer from tropical soils to crops. Further research could be also be performed in order to characterize the mineralogy of the soils in order to better elucidate whether the observed variability in metals’ concentrations is caused by natural anthropogenic factors. Different plant (and animal) species could also be tested with the goal of better regulating metal contamination in tropical agricultural areas affected by industrial and mining activities.

Acknowledgements Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche du Québec - Nature et Technologies (FRQNT), Brazilian National Council for Scientific and Technological Development (CNPq), Mitacs - Canada and Vale Ltd. (Brazil).

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Bioavailability of trace metals and rare earth elements (REE) from the tropical soils of a coal mining area Juliana A. Galhardi1*, Bruno P. Leles2, Jaime W.V. de Mello3, Kevin J. Wilkinson1

1. Biophysical Environmental Chemistry Group, Department of Chemistry, University of Montreal, Montreal, Quebec, Canada. H3C 3J7. 2. Department of Ecology, São Paulo State University, Rio Claro - SP, Brazil. 3. Soil Chemistry and Environmental Geochemistry group, Department of Soil, Federal University of Viçosa, Viçosa, MG, Brazil.

* Corresponding author - [email protected]

Credit Author Statement

Juliana A. Galhardi – Conceptualization and design of the study, sample collection and treatment, acquisition of data, formal analysis, data treatment, analysis and interpretation of data, writing the manuscript. Bruno P. Leles – Assistance for the collection and organization of the samples. Jaime W.V. de Mello – Acquisition of data, formal analysis. Kevin J. Wilkinson - Conception and design of study, project supervision, resources, revising the manuscript critically for important intellectual content.

Figures Figure 1: 1.5-column fitting image. Figure 2: 2-column fitting image. Figure 3: 1.5-column fitting image. Figure 4: 2-column fitting image. Figure 5: single fitting image. Figure 6: 2-column fitting image. Figure 7: 1.5-column fitting image.

Fig. 1 – Map and photos of the coal mining area, showing some of the acid drainage that was observed in the ponds and tailings and that could reach the natural waters, soil and crops.

Figure 2 – Relationship between soil pH, clay content and DOC.

Figure 3 – Mean concentrations (mg kg-1) of trace and rare earth elements in grains, leaves and roots for soybeans sampled from cultivated lands close to the studied coal mine area in Southern Brazil (n = 22). Note the differences in the scaling of the y-axis.

100

%

80 60 40 20 0

80.2

78.8

Leaves

Roots

3.8 Grains LREE

HREE

Figure 4 – LREE and HREE content in the plants.

Figure 5 – Principal component analyses (PCA) for trace and rare earth elements in the plants roots (A), leaves (B) and grains (C).

Figure 6 – Mean BF for trace and rare earth elements in the grains, leaves and roots of the soybean (n = 22).

Figure 7 – Translocation factors (mean, n = 22) of the trace and rare earth elements in the plant system.

Tables Table 1 – Mean values for the soil properties and concentrations of trace and rare earth elements (n = 22). EC= electrical conductivity; OC=organic carbon, DOC=dissolved organic carbon, CEC= cation exchange capacity, LREE= light rare earth elements (atomic numbers 57 to 63, i.e. La to Eu); HREE=heavy rare earth elements (atomic masses 64 to 71, i.e. Gd to Lu).

1.

Sample Mean SD Maximum Minimum Regulatory1 pH 5.5 1.3 7.7 4.1 EC (µS cm-1) 192 100 434 48 OC (mg kg-1) 40000 16000 71000 19000 DOC (mg L-1) 9.3 3.8 19.7 4.0 Ntotal (mg kg-1) 17 6 27 7 P (mg kg-1) 39 28 103 8.20 K (mg kg-1) 121 52 229 23.00 CEC (mmol kg-1) 120 31 163 52 Sand (%) 41.6 15.9 68.2 16.8 Silt (%) 30.6 9.1 50.9 17.7 Clay (%) 27.8 8.8 46 10 Al (mg kg-1) 18247 7009 32340 4163 Fe (mg kg-1) 15717 7799 43304 4466 Ca (mg kg-1) 1890 2060 8105 245 Mg (mg kg-1) 1018 753 2991 165 As (mg kg-1) 6.8 4.5 20.2 1.7 15 Ba (mg kg-1) 67 122 585 14.5 150 Cd (mg kg-1) 0.37 0.24 1.01 0.02 1.3 Co (mg kg-1) 5.9 11.8 43 0.62 25 Cr (mg kg-1) 32.5 12.6 71.8 22.1 75 Cu (mg kg-1) 14.2 13.7 65 4.08 60 Mn (mg kg-1) 492 1141 5425 66.90 Ni (mg kg-1) 11.6 6.2 32.2 6.3 30 Sr (mg kg-1) 15.5 10.4 40.6 4.7 Rb (mg kg-1) 23.4 11.7 44.6 5.8 V (mg kg-1) 47.8 29.9 171 17.6 Zn (mg kg-1) 33.2 21.9 102 7.60 300 Pb (mg kg-1) 8.8 5.2 27.2 2.7 72 Th (mg kg-1) 5.7 1.8 8.6 1.9 U (mg kg-1) 2.0 0.72 3.44 0.55 La (mg kg-1) 9.9 5.4 28.9 3.5 Ce (mg kg-1) 25.0 13.8 69.2 8.3 Pr (mg kg-1) 2.3 1.4 7.0 0.77 Nd (mg kg-1) 8.6 5.3 27.3 2.8 Sm (mg kg-1) 1.6 1.1 5.4 0.50 Eu (mg kg-1) 0.27 0.24 1.2 0.06 Gd (mg kg-1) 1.6 1.1 5.7 0.48 Dy (mg kg-1) 0.80 0.66 3.28 0.21 Ho (mg kg-1) 1.7 0.69 3.5 0.74 Er (mg kg-1) 0.48 0.40 2.0 0.12 Tm (mg kg-1) 0.06 0.05 0.24 0.01 Yb (mg kg-1) 0.36 0.29 1.45 0.08 Lu (mg kg-1) 0.07 0.05 0.25 0.00 47.7 27.0 139 16.1 LREE (mg kg-1) 5.1 3.1 16.3 1.7 HREE (mg kg-1) 52.8 30.1 155 18 REE (mg kg-1) Prevention Values published by the Brazilian National Council for the Environment (CONAMA, 2011), which indicate concentration thresholds for Brazilian agricultural soils.

Table 2 – Enrichment factors of the trace metals, including rare earth elements in the topsoils, obtained by normalizing with Al concentrations (Equation 1). Standard deviations are given in parentheses (n = 22). Element

EF

Element

EF

Ba As Cr Co Cu Cd Mn Sr Rb V Zn Pb Th

0.41 (0.6) 13.8 (6.9) 3.78 (2.6) 2.06 (3.6) 4.03 (2.4) 14.8 (7.6) 3.80 (8.1) 0.20 (0.1) 0.86 (0.3) 3.67 (1.9) 2.60 (1.3) 2.11 (0.9) 2.24 (0.5)

La Ce Pr Nd Sm Eu Gd Dy Ho Er Tm Yb Lu

1.25 (0.6) 1.55 (0.7) 1.49 (0.8) 1.36 (0.7) 1.37 (0.8) 1.14 (0.8) 2.33 (1.4) 1.13 (0.8) 11.20 (4.4) 0.94 (0.6) 0.80 (0.5) 0.98 (0.6) 1.02 (0.6)

U

3.22 (0.6)

Table 3 – Mean values of the elemental fractionation in the soils (mg kg-1, n = 22) with their respective standard deviations. Element As Ba Cd Co Cr Cu Mn Ni Sr Rb V Zn Pb Th U

F1 0.17 (0.21) 8.6 (8.7) 0.08 (0.05) 0.35 (0.37) 1.5 (0.1) 0.65 (1.9) 78 (58) 1.2 (0.89) 8.2 (8.1) 0.40 (0.16) 0.09 (0.07) 6.8 (4.8) 0.23 (0.18) 0.54 (0.49) 0.67 (0.57)

F2 0.3 (0.26) 4.2 (0.15) 0.09 (0.08) 2.9 (7.9) 0.77 (0.26) 1.2 (2.6) 94 (229) 1.4 (2.4) 3.8 (3.5) 1.2 (0.52) 6.1 (1.8) 6.1 (6.5) 3.1 (4.0) 0.36 (0.3) 1.3 (1.0)

F3 1.9 (3.4) 5.4 (8.3) 0.12 (0.2) 0.34 (0.64) 10.9 (1.1) 5.2 (10.1) 41 (74) 1.8 (1.8) 1.4 (1.1) 0.79 (0.42) 15.6 (27) 5.7 (4.7) 3.1 (2.6) 0.95 (1.7) 0.33 (0.14)

Element La Ce Pr Nd Sm Eu Gd Dy Ho Er Tm Yb Lu LREE HREE

F1 0.11 (0.16) 0.20 (0.15) 0.02 (0.03) 0.07 (0.08) 0.012 (0.015) 0.05 (0.04) 0.014 (0.02) 0.11 (0.13) 1.05 (0.8) 0.04 (0.05) 0.005 (0.006) 0.03 (0.03) 0.011 (0.005) 0.46 (0.41) 1.16 (0.79)

F2 8.7 (7.7) 0.90 (0.66) 2.35 (1.76) 0.15 (0.14) 0.54 (0.55) 0.09 (0.1) 0.02 (0.03) 0.12 (0.13) 0.04 (0.05) 0.41 (0.51) 0.05 (0.06) 0.26 (0.32) 0.04 (0.05) 12.7 (9.5) 0.96 (1.15)

F3 1.58 (1.29) 4.9 (4.2) 0.87 (1.3) 1.58 (1.6) 0.45 (0.4) 0.09 (0.1) 0.35 (0.39) 0.21 (0.24) 3.5 (4.7) 0.5 (0.7) 0.07 (0.09) 0.37 (0.5) 0.06 (0.08) 9.51 (7.5) 5 (6.7)

Table 4 –Trace elements and REE (mg kg-1) in the coal ash, coal, tailings and four fertilizers (FTL). Coal ash

Coal

Tailing

FTL-1

FTL-2

FTL-3

FTL-4

Element

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

As

190

2.1

142

1.6

201

44.7

7.4

0.1

4.3

0.1

3.0

0.03

1.4

0.02

Ba

34

0.4

32

0.36

81

27.2

3.7

0.04

503

5.5

3.6

0.04

231

2.5

Cd

3.5

0.02

0.4

0.001

0.48

0.28

2.3

0.03

1.5

0.01

0.2

0.001

0.1

0.001

Co

3.2

0.04

6.4

0.07

5.4

2.2

2.1

0.02

9.4

0.1

5.0

0.1

0.9

0.01

Cr

17

0.19

11.6

0.13

11.3

0.8

51.8

0.57

14.1

0.16

2.7

0.03

1.0

0.01

Cu

13

0.14

21

0.23

18.6

5.5

6.5

0.07

222

2.4

11.4

0.13

17.2

0.19

Mn

111

1.2

210

2.3

382

234

223

2.5

951

10.5

269

3.0

116

1.3

Ni

10

0.11

15

0.16

11

5.2

10.8

0.12

23.4

0.26

7.2

0.08

2.0

0.02

Sr

32

0.35

96

1.1

71

22.9

53

0.58

2106

23.2

74

0.8

6596

72.6

Rb

6.9

0.08

16.6

0.18

18.5

2.9

29.5

0.33

8.8

0.1

10.1

0.11

12.4

0.14

V

58

0.63

35

0.38

39.3

18.2

91

1.0

20

0.22

35

0.38

22.8

0.25

Zn

275

3

381

4.2

122

54.2

59

0.65

1250

13.7

24

0.27

17.3

0.19

Pb

50

0.6

748

8.2

104

27.4

2.3

0.03

24.5

0.27

0.6

0.01

2.2

0.02

Th

5.1

0.1

8.6

0.09

4.1

1.1

10.9

0.12

34.2

0.38

82

0.9

5.7

0.06

U

15

0.2

16

0.18

16.4

2.3

184

2.0

57.7

0.6

27.5

0.3

1.0

0.01

La

11.1

0.12

11.4

0.13

8.9

1.5

6.3

0.07

467

5.1

112

1.2

679

7.5

Ce

21.3

0.23

28

0.31

22

4.2

8.0

0.09

975

10.73

239

2.6

983

10.8

Pr

2.7

0.03

3.8

0.04

2.7

0.54

1.08

0.01

106

1.17

28.8

0.32

94

1.04

Nd

11

0.12

15.6

0.17

11.2

2.1

4.45

0.05

379

4.17

112

1.23

306

3.4

Sm

2.81

0.03

3.2

0.04

2.3

0.41

1.14

0.01

49

0.54

21.3

0.23

41

0.45

Eu

0.62

0.01

0.6

0.01

0.46

0.08

0.31

0.1

11.8

0.13

6.4

0.07

10.4

0.12

Gd

4.14

0.05

3.6

0.04

3.2

0.67

2.1

0.02

39.7

0.44

24.8

0.27

36

0.4

Dy

4.97

0.05

2.7

0.03

2.7

1.1

2.9

0.03

10.2

0.11

12.9

0.14

12.7

0.14

Ho

3.66

0.04

5.9

0.07

7.0

0.2

3.2

0.04



4.6

0.05



Er

2.99

0.03

1.5

0.02

1.4

0.6

3.1

0.04

3.44

0.04

5.7

0.06

4.9

0.05

Tm

0.4

0.005

0.19

0.003

0.17

0.07

0.59

0.01

0.24

0.1

0.67

0.01

0.44

0.01

Yb

2.47

0.03

1.21

0.01

1.03

0.4

5.1

0.06

1.37

0.02

4.05

0.05

2.5

0.03

Lu

0.33

0.004

0.1

0.001

0.22

0.06

0.99

0.01

0.16

0.002

0.58

0.01

0.3

0.1

LREE

49.5

0.54

63

0.69

48

8.7

21.3

0.23

1988

21.9

519

5.7

2113

23.2

HREE

18.9

0.21

15

0.17

16

2.6

18.2

0.2

55

0.6

53

0.59

57

0.56


Table 5 – Correlation between trace elements and REE in plants versus the total concentration and the bioavailable fraction determined thought the sequential extraction method (F1 – F3), single extraction (Ca(NO3)2), DGT and chemical modeling (WHAM – free ion). F1

F2

F3

Ca(NO3)2

DGT

Total concentration (acid digestion)

WHAM

Ele men t

Gra ins

Lea ves

Ro ots

Soi l

Gra ins

Lea ves

Ro ots

So il

Gra ins

Lea ves

Ro ots

Soi l

Gra ins

Lea ves

Ro ots

Soi l

Gra ins

Lea ves

Ro ots

Soi l

Gra ins

Lea ves

Ro ots

Soi l

Grain s

Leave s

Root s

As

0.3 14

0.2 21

0.8 3

0.8 37

0.3 74

0.7 58

0.9 08

0.1 77

0.5 56

0.4 29

0.2 16

0.2 83

0.0 59

0.8 01

0.9 07

-

-

-

-

-

-

-

-

0.381

0.481

0.81 3

Ba

0.9 53

0.8 54

0.7 43

0.6 99

0.1 83 0.1 51

0.1 2

0.3 02

0.3 99

0.6 89

0.8 87

0.8 83

0.9 77

0.9 34

0.9 37

0.9 42

0.8 64

0.8 77

0.9 26

0.9 33

0.8 47

0.6 78

0.6 44

0.8 48

0.5 54

0.61

0.833

0.80 1

Cd

0.4 95

0.9 27

0.7 41

0.7 99

0.2 54

0.3 19

0.0 98

0.7 19

0.1 75

0.1 31

0.1 56

0.5 1

0.8 58

0.4 65

0.5 63

0.2 56

0.8 56

0.3 45

0.2 62

0.5 33

0.6 2

0.5 29

0.1 67

0.962

0.662

0.32 1

0.3 44

0.2 71

0.8 05

0.4 24

0.2 03

0.9 86

0.1 94

0.9 63

0

0.1 73

0.0 5

0.8 67

0.1 59

0.0 33

0.9 78

0.0 65

0.1 06

0.7 8

0.281

-0.09

0.13 3

0.0 94

0.1 34

0.3 02

0.3 23

0.0 34

0.1 02

0.0 05

0.7 03

0.6 55

0.3 99

0.4 55

0.3 03

0.3 01 0.8 72

0.5 29

0.2 74

0.0 01

0.2 13

0.1 16

0.2 61

0.254

0.234

0.16 2

0.2 21

0.9 51

0.8 06

0.372

0.083

0.83 4

Co Cr

0.0 77 0.2 09

0.1 15 0.2 01

0.1 85 0.0 89 0.4 24

0.0 62 0.2 36

0.2 95 0.2 28 0.3 25

Cu

0.8 65

0.0 86

0.9 64

0.9 65

0.6 8

0.0 83

0.9 08

0.9 53

0.2 61

0.0 49

0.8 6

0.9 41

0.9 22

0.0 12

0.9 58

0.9 73

0.3 81

0.1 86

0.1 28

0.9 05

Mn

0.0 87

0.7 16

0.4 96

0.7 7

0.1 16

0.0 55

0.0 5

0.6 61

0.3

0.7 13

0.4 09

0.7 06

0.0 98

0.7 94

0.5 13

0.7 69

0.7 94

0.9 19

0.8 13

0.8 83

0.1 94

0.7 34

0.4 89

0.5 99

0.424

0.521

0.26 2

Ni

0.4 22

0.0 92

0.1 27

0.2 7

0.2 82

0.0 76

0.0 47

0.8 55

0.0 69

0.1 73

0.3 38

0.3 2

0.0 91

0.5 46

0.5 89

0.4 33

0.2 32

0.8 97

0.6 18

0.3 88

0.3 58

0.7 87

0.1 13

0.384

0.25

0.31 1

Sr

0.4 47

0.4 84

0.9 45

0.5 05

0.5 71

0.2 23

0.8 68

0.1 06

0.2 85

0.3 62

0.4 66

0.6 04

0.4 22

0.8 54

0.5 7

0.6 16

0.6 71

0.3 76

0.5 63

0.0 23

0.2 16

0.2 21

0.363

0.371

0.22 3

Rb

0.0 19

0.7 7 0.4 26

0.4 04

0.7 76

0.4 56

0.2 89

0.7 54

-

-

-

-

-

-

-

-

0.141

0.391

0.31 1

0.2 63

0.5 57

0.4 76

0.3 66

0.7 71

0.1 15

0.5 18 0.0 84

0.7 31

0.1 06

0.8 84 0.0 61

0.7 66

0.1 29

0.5 8 0.5 06

0.6 76

V

0.5 07 0.1 44

0.0 22 0.1 8 0.0 7

0.3 81

0.6 6

0.2 39

0.0 01

0.2 78

-

-

-

-

0.631

0.072

0.15 2

Zn

0.0 71

0.8 85

0.5 57

0.9

0.4 68

0.4 19

0.0 53

0.8 79

0.8 09

0.3 55

0.8 29

0.0 26

0.8 82

0.5 89

0.7 02

0.4 73

0.8 69

0.7 61

0.5 64

0.0 89

0.6 82

0.8 14

0.2 64

0.262

0.712

0.26 1

Pb

0.2 01

0.5 12

0.8 48

0.1 51

0.0 71

0.0 81

0.3 45

0.7 17

0.0 32

0.0 2

0.7 78

0.6 88

0.9 13

0.8 83

0.0 34

0.6 64

0.4 41

0.7 47

0.4 64

0.8 44

0.4 58

0.7 14

0.3 46

0.181

0.221

0.28 3

0.1 68 0.1 8 0.1 56

0.4 6

0.5 61

0.3 25

0.5 15

0.5 33

0.3 67

0.3 43

0.4 75

0.2 21

0.8 56

0.4 73

0.6 32

0.4 81

-

-

-

-

0.8 39

0.2 35

0.32 4

0.6 19

0.2 12

0.3 16

0.7 08

0.1 86

0.5 01

0.5 48

0.9 15

0.2 33

0.3 83

0.4 49

0.3 26

0.7 22

0.3 38

0.8 28

0.5 13

0.8 07

0.2 13

0.2 18 0.0 07

0.354

0.3 51

0.3 03 0.0 53

0.162

0.2 33

0.043

0.431

0.42 4

0.0 18

0.1 9

0.4

0.0 68 0.0 21 0.2 63

0.0 12 0.6 37

0.2 74

0.3 89

0.1 69

0.0 7

0.4 35

0.8 35

0.3 85

0.7 79

0.1 91

0.2 06

0.9 33

0.6 72

0.7 94

0.6 89

0.6 43

0.1 98

0.1 59

0.0 43

0.022

0.021

0.23 2

0.1 49

0.3 37

0.3 64

0.3 16

0.1 11

0.8 14

0.1 04

0.0 25

0.8 74

0.0 2

0.7 63

0.3 57

0.0 81

0.7 43

0.6 33

0.7 18

0.5 69

0.4 35

0.2 98

0.5 26

0.0 78

0.211

0.061

0.13 1

0.1 69 0.1 94 0.3 15 0.2 15 0.2 95 0.2 92

0.0 66 0.0 62 0.0 4

0.0 51 0.0 1

0.1 46

0.4 15

0.6 85

0.2 8

0.5 61

0.2 03

0.1 22

0.7 63

0.2 01

0.1 18

0.9 29

0.8 8

0.8 68

0.7 45

0.4 55

0.2 22

0.2 93

0.0 53

0.152

0.043

0.27 2

0.1 58

0.4 83

0.6 49

0.0 21

0.2 69

0.8 53

0.7 5

0.2 12

0.1 03

0.8 35

0.9 15

0.8 68

0.7 56

0.3 96

0.2 22

0.3 26

0.0 45

-0.21

0.042

0.28 1

0.1 4

0.5 33

0.2 21

0.5 3

0.6 25

0.1 65 0.0 61 0.0 93 0.1 22

0.2 5

0.6 04

0.6 9

0.7 15

0.2 5

0.0 79

0.7 33

0.9 16

0.8 93

0.7 26

0.5 18

0.2 87

0.3 51

0.0 17

-0.27

0.051

0.31 3

0.1 17

0.4 47

0.5 67

0.2 91

0.6 16

0.5 64

0.2 08

0.2 29

0.5 78

0.7 85

0.7 52

0.6 09

0.3 4

0.2 8

0.3 77

0.9 3

0.9 17

0.8 19

0.1 9

0.3 07

0.3 29

0.0 4

0.071

0.011

0.41 2

0.0 32

0.1 53

0.5 71

0.0 13

0.1 39

0.4 5

0.7 53

0.0 14

0.0 28

0.2 31

0.9 02

0.1 31

0.7 41

0.3 03

0.0 72

0.9 39

0.8 75

0.8 86

0.7 73

0.5 41

0.2 79

0.4 6

0.0 43

0.193

0.034

0.29 2

0.0 71

0.5 51

0.5 12

0.0 48

0.2 87

0.5 67

0.6 52

0.0 05

0.0 88

0.4 35

0.9 38

0.2 58

0.6 68

0.3 37

0.0 84

0.6 5

0.9 15

0.8 94

0.8 08

0.7 23

0.3 04

0.3 71

0.332

0.013

0.35 1

Ho

0.7 13

0.4 88

0.2 43

0.3 38

0.5 16

0.0 87

0.2 03

0.1 92

0.2 04

0.2 23

0.2 49

0.6 72

0.0 52

0.1 03

0.0 08

0.4 92

0.2 48

0.1 16

0.2 28

0.4 33

0.1 6

0.3 97

0.0 53

0.363

0.411

0.29 1

Er

0.1 75

0.0 14

0.6 12

0.5 21

0.1 95

0.2 08

0.5 93

0.7 34

0.1 43

0.1 57

0.1 44

0.8 49

0.2 66

0.6 02

0.4 69

0.0 96

0.0 07

0.9 35

0.9 03

0.7 89

0.4 51

0.2 71

0.3 93

0.532

0.022

0.36 2

Tm

0.0

0.0

0.6

0.5

0.1

0.2

0.6

0.7

-

-

0.1

0.7

0.3

0.5

0.5

0.0

0.2

0.9

0.8

0.7

0.6

0.2

0.3

-

-

0.36

Th U La Ce Pr Nd Sm Eu Gd Dy

0.5 12 0.5 51

0.2 9 0.0 33 0.0 34 0.0 36 0.2 76

0.1 34 0.0 24

0.1 27 0.2 02 0.4 11 0.1 75 0.1 44

0.0 09 0.0 11

0.0 13 0.2 46 0.0 2 -

37

64

32

71

14

97

37

08

23

Yb

0.2 12

0.0 52

0.6 84

0.5 27

0.0 99

0.2 81

0.6 01

0.7 05

Lu

0.7 07

0.2 05

0.0 66

0.4 97

0.0 28

0.6 88

0.1 66

0.3 49 0.3 48

0.4 68 0.3 74

0.1 51 0.2 99

0.3 47 0.2 56

0.6 65 0.2 13

0.0 62 0.2 56

Aver age SD

0.1 25 0.3 67

0.0 23 0.2 42 0.3 26

0.2 02 0.1 68 0.2 24

0.0 57 0.2 28

0.1 73 0.1 62 0.1 92 0.1 86 0.3 09

63

57

92

14

68

93

81

46

8

92

18

92

91

0.1 33

0.8 47

0.1 23

0.5 06

0.5 96

0.0 97

0.2 84

0.9 55

0.9 09

0.7 38

0.7 37

0.2 8

0.3 81

0.4 33 0.2 90 0.3 92

0.2 39 0.5 25 0.3 23

0.6 93 0.4 03 0.3 38

0.6 4

0.7 55

0.8 8

0.0 85

0.6 37 0.3 57

0.6 59 0.3 45

0.5 76 0.3 85

0.3 82 0.3 64

0.3 11 0.3 17 0.2 60

0.8 12 0.6 77 0.2 96

0.3 04 0.3 87 0.3 44

0.4 15 0.4 10 0.4 90

0.3 83 0.2 66 0.2 65

0.0 08 0.3 85 0.2 83

0.0 25 0.0 28 0.0 61 0.1 28 0.2 88

0.381

0.011

4

0.291

0.001

0.36 3

0.373

-0.18

0.002

0.178

0.378

0.289

0.27 1 0.32 2 0.21 7

38

39

Highlights 1) Trace elements and REE are accumulated by soybean in an area linked to coal mining 2) Acidic pH and high clay and DOC content enhanced REE uptake by plants 3) Chemical extractions correlated well with metal bioavailability in tropical soils 4) DGT and speciation modelling correlated well with metal bioavailability

40