Removal of Copper From the Vineyard Land of Pješivci (Montenegro) Using Amino Acids

Removal of Copper From the Vineyard Land of Pješivci (Montenegro) Using Amino Acids | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Removal of Copper From the Vineyard Land of Pješivci (Montenegro) Using Amino Acids Vlatko Kastratović, Bojana Knežević This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4263629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Sep, 2024 Read the published version in Environmental Earth Sciences → Version 1 posted 8 You are reading this latest preprint version Abstract Copper compounds in the form of fungicides are most commonly used as a measure of protection against vine diseases. Typically, around a dozen treatments are done throughout a year with a dispersion of approximately 5–10 kg of Cu per hectare. For many years, the remediation of heavy metals has often involved the use of ex-situ soil washing with chelating reagents. Amino acids have a lower metal chelation capability compared to EDTA and its derivatives, but they have numerous other advantages in comparison. The main goal of this research was to investigate the ability of 9 amino acids and one dipeptide to extract Cu from various vineyard soil samples and compare their chelating ability with other 'green' chelating agents. The average content of Cu extracted with amino acids is 34.7 ± 16.7 mg/kg or 30.3 ± 5.43 wt.% relative to the pseudo-total content. This is more than what was extracted with carboxylic acid salts (9.91 ± 7.49 mg/kg or 8.45 ± 5.56 wt.%) but less than with EDTA (98.5 ± 42.7 mg/kg or 79.9 ± 7.12 wt.%). The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His > Ser > Thr > Leu > Gly > Val > Phe > Gly-Gly ~ Ala > Arg. The results of this paper show that the amino acid structure is the most important factor for efficient Cu extraction, while the physicochemical properties of the vineyard soil have less impact. carboxylic acid chelating agents EDTA extraction fungicides remediation Figures Figure 1 Figure 2 Introduction The culture of growing vines and winemaking in Montenegro dates back to the pre-Roman period. Later, the ancient artisans and Romans further advanced Montenegro's viticulture. Viticulture continued to develop during the Middle Ages, especially along the shores of Lake Skadar. Today, the largest vineyards are owned by the company "13. Jul - Plantaže," with approximately 10 million vine plants on an area of 2,300 hectares. In addition to this company, there are 312 registered grape producers with vineyards covering around 2,850 hectares (Ministry of Agriculture of Montenegro, 2023). One of the main challenges vine growers face is how to protect the plant from numerous harmful organisms attacking it, thereby reducing yield and the quality of grapes. The most significant and common diseases vine is exposed to in Montenegro's climate are downy mildew, powdery mildew, and gray rot (mold). Copper compounds in the form of fungicides are most commonly used as a measure of protection against these vine diseases. The most common Cu compounds in fungicides are copper(II) sulfate pentahydrate, copper(II) oxide, copper hydroxide, and copper oxychloride. The number of phytosanitary treatments depends on several factors and, most of all, on weather conditions. Typically, around a dozen treatments are done throughout the year with a dispersion of approximately 5–10 kg of Cu per hectare (Ministry of Agriculture of Montenegro, 2023). Copper is an essential plant micronutrients, but it can be toxic at higher concentrations. Copper is a component of several complex proteins - enzymes and plays a significant role in several physiological processes in plants. The role of Cu in biochemical processes is influenced by its small ionic radius, oxidation state (Cu 2+ or Cu + ), and the formation of numerous complex chelate-type compounds (Kabata Pendias, 2010 ). Metals in the soil can be found in several forms: as dissolved free ions or dissolved complexes in pore water, as they are deposited as organic and inorganic compounds, or as they get adsorbed onto specific solid phase soil fractions. Metals in soil exhibit a specific mobility and bioavailability to plants. The origin of metals in soil is geochemical (natural) and anthropogenic. Metals of geochemical origin in soil are typically found in less available or entirely unavailable forms for plants. Soils have a pronounced ability to bind anthropogenic metals through ion exchange or adsorption, which have a significantly higher bioavailability (Gleyzes et al., 2002 ; Wise et al., 2000 ). Data on the total or "pseudo"-total metal content in the soil do not provide a clear picture of how they bind to the soil. Information about the mobility, bioavailability, and toxicity of metals from the soil is of much greater importance (Gao et al., 2010 ). The fact that metals are present in the soil does not imply that they are completely available for plant uptake (Pertsemli & Voutsa, 2007 ). The vine plant does not have the ability to absorb significant amounts of copper (Roviello et al., 2021 ). Liao et al. ( 2000 ) reported generally low uptake of Cu by plants and inefficient transport from roots to shoots in plantations using Cu fertilizers. For many years, the remediation of heavy metals has often involved the use of ex-situ soil washing with chelating reagents. The application of chelants to remove metals from contaminated soils is based on their efficiency, widespread application, selectivity, and reproducibility. (Chao et al., 1998 ; Dermont et al., 2008 ; Dolev et al., 2020 ; Ferraro et al., 2016 ; Kabala & Singh, 2001 ; Wuana & Okieimen, 2011 ). Synthetic metal chelating agents (such as EDTA), which are widely used in soil-washing processes are highly effective (Dermont et al., 2008 ; Dolev et al., 2020 ; Lestan, 2015 ). However, they have many disadvantages. They are non-biodegradable and can impact the biogeochemical soil reactions (Jelusic & Lestan, 2014 ; Lestan, 2015 ). Furthermore, they have a strong affinity for essential metals (e.g., Ca), thereby altering the soil structure (Tsang et al., 2007 ). Since considerable amounts of EDTA are used to chelate essential metals in the soil (Ca, Mg, Fe, Al, etc.), it makes the remediation process less efficient (Tandy et al., 2004 ; Manouchehri & Bermond, 2006 ). Protein and other natural amino acids also possess a pronounced ability to complex metals, but they have been much less investigated (Farkas & Sovago, 2012 ; Fleck & Petrosyan, 2014 ; Laudicina et al., 2016). Amino acids have a lower metal chelation ability compared to EDTA and its derivatives, but they have numerous other advantages in comparison: they are biodegradable and environmentally friendly; they serve as a source of nitrogen for plants; they help in the uptake of essential elements by plants; in some cases, are used in agriculture as fertilizers; they do not chelate essential soil metals (e.g., Ca), and thus, they do not alter soil structure; they are inexpensive (Dolev et al., 2020 ; Liu & Lee, 2013 ; Warren, 2014 ). The immediate objectives of this research are to: (1) investigate the ability of 9 amino acids and one dipeptide to extract Cu from different samples of vineyard soil, (2) consider whether soil Cu speciation affects the extraction efficiency, (3) compare the chelating ability of amino acids with other "green" chelators, and also the mutual ability of amino acids to extract Cu depending on the structure of the amino acid itself. Our intention is to have the results of this study assist in the optimization and selection of amino acids for the remediation of Cu-contaminated soil. Materials and methods Study area and sampling The selected soils are concentrated in the wine-growing sub-region of Pješivci (approximate coordinates: latitude 42° 24' and longitude 19° 03') at a distance of 30 km from Podgorica, Montenegro (Fig. 1 ). In terms of pedological characteristics, the explored site features vertic eutric brown soil on alluvial and colluvial deposits - a variety of vertic cambisol (Ministry of Sustainable Development and Tourism of Montenegro, 2019 ). The investigated wine-growing sub-region is primarily influenced by a slightly modified Mediterranean climate. The area is characterized by long, hot, and dry summers and relatively mild and rainy winters. In January, temperatures are lower than those in coastal areas at approximately the same latitude, while during the summer, temperatures tend to be somewhat higher (Ministry of Sustainable Development and Tourism of Montenegro, 2019 ). Sampling was conducted with nine vineyard soils using the Eijkelkamp soil sampling kit. The vineyards are located at a distance of 100–500 meters from each other. Composite samples were made by mixing cross-collected soil from 10 sampling sites from a 20 cm deep layer, with a distance of 2.5 meters between sampling sites. Sample preparation for analysis The soil samples were dried in the air and then in a drying oven at a temperature of 75 0 C for 48 hours. Dried soil samples were crushed in an agate mortar and sieved through a series of sieves (< 2.0, 1.5, 1.0, and 0.5 mm). Approximately 0.5 g (± 0.0001 g) of the soil sample under pressure and high temperature was mineralized with a mixture of HCl (wt. % 36): HNO 3 (wt. % 65) (v/v 3:1) in a microwave oven, Milestone Microwave Ethos 1 (USEPA, 2007). After mineralization, the solutions were diluted with 2M HNO 3 to the final volume of 50.0 mL. Analysis of soil and extracted Cu In our study, Cu from vineyard soils was extracted with amino acids, Na-salts of carboxylic acids (acetate, oxalate, tartrate, citrate), and complexing reagent (EDTA). The following amino acids were used: glycine (Gly), alanine (Ala), valine (Val), leucine (Leu), phenylalanine (Phe), serine (Ser), threonine (Thr), histidine (His), arginine (Arg ) and the dipeptide glycyl-glycine (Gly-Gly). In order to determine the optimal Cu leaching conditions, the following were examined: soil particle size (0.5, 1.0, 1.5, and 2.0 mm); concentration of washing agent (0.05, 0.1 and 0.2 mol/L); contact time (0.5 h, 1.0 h and 2.0 h); soil/solution ratio, w/v, (1:25, 1:50 and 1:100). The pH of the extraction solution was not adjusted, and in all Cu extraction experiments the mixtures were buffered after a few moments to the pH of the tested soil samples. After preliminary tests, we decided on the following experimental conditions: soil particle size 0.5 mm, Cu extraction solution concentration 0.1 mol/L, extraction time 1 h, and soil/solution ratio 1 g/50.0 mL. In order to determine the distribution of Cu in the soil, we applied a modified BCR (the Community Bureau of Reference of the European Union) sequential extraction procedure of the sample sediment (Pueyo et al., 2003 ). The pH (in H 2 O and KCl) of the tested samples of vineyard soil was determined by potentiometry. The content of oxidizable carbon was determined by the WalkleyBlack method (Nelson & Sommers, 1982). Total organic carbon was obtained by multiplying the content of oxidizable carbon by 1.334. The content of organic matter was calculated based on the assumption that organic matter contains 5 % organic carbon. The percentage (wt. %) of CaCO 3 in soils was determined by the volumetric calcimeter method after the HCl attack. Determination of the concentration of extracted Cu in the samples of soils was conducted by inductively coupled plasma optical emission spectroscopy (ICP-OES) technique on a "Spectro Arcos" device. All samples of soils were prepared for three times, and their average value was analyzed. In each batch of ten samples, the blank solution was measured. The analytical accuracy of the results was checked using several reference soil samples from the interlaboratory calibration program (Houba et al., 1996 ). The reproducibility of the results was within 10% of the certified values. Statistical analysis Microsoft Excel 2000 was used to calculate the mean values and standard deviation. The significance of variations in the extracted Cu content among different amino acids was assessed using one-way analysis of variance (ANOVA I and II). A post-hoc test was applied when differences were significant (SPSS 2012). To compare the mean values among individual amino acids from all three locations, the Fisher's LSD test for the least significant difference was used. Results and discussion Physicochemical characteristics of the tested soils An overview of the mean values of the physicochemical characteristics of the tested soils, which are relevant to the behavior of metals, is given in Table 1 . Table 1 Physicochemical characteristics of the tested soils pH (H 2 O) pH (KCl) Oxidizable C (%) TOC (%) OM (%) Carbonates (%) Min - max Mean ± SD 5.98–7.50 6.65 ± 0.47 5.38–7.16 6.33 ± 0.59 0.49–1.71 1.12 ± 0.42 0.65–2.28 1.53 ± 0.58 1.12–3.92 2.64 ± 1.00 13.6–46.5 29.2 ± 11.1 The pH value of the tested vineyard soils ranges from 5.98 to 7.50. Strawn and Baker ( 2009 ) showed that within the pH range of 5.2 to 7.1, Cu is primarily complexed with soil organic matter in the form of cyclic five-membered ring chelates. Karlsson et al. ( 2006 ) demonstrated in their paper that at pH 4.8–6.3, Cu 2+ forms a five-membered chelate ring with glutamic acid. Manceau and Matynia ( 2010 ) showed that Cu 2+ at pH 4.5 and 5.5 builds five-membered chelates similar to Cu(malate) 2 at a Cu concentration of 100–300 ppm and six-membered ones similar to Cu(malonate) 1-2 at higher Cu concentrations. Due to copper's high affinity for soil organic matter and colloids, Cu is a poorly mobile element in almost neutral soils. Consequently, farmers have been able to apply significant amounts of Cu salts to organic soils over time without causing any crop toxicity (Brunetto et al., 2016 ; McBride, 1994 ). In alkaline soils, soluble Cu 2+ complexes (hydroxy-, carbonate-) can be formed, increasing the mobility of Cu in the soil (Brunetto et al., 2016 ; McBride, 1994 ). Several studies (Brunetto et al., 2016 ; Fernandez-Calvino et al., 2009 ; Khanlari & Jalali, 2008 ) have shown that in weekly alkaline soils, the largest Cu portion remains in the residual fraction after successive extractions. These considerations demonstrate that Cu is quite stable in the soil, and that its bioavailability depends on the nature of organic matter, organic matter minerals Fe, Al, Mn, its amount, as well as on carbonate, soil pH, and cation exchange capacity (Brunetto et al., 2016 ; Couto et al., 2016 ; Fernandez-Calvino et al., 2009 ). Copper adsorption in the soil primarily occurs on organic matter, while the remainder is bound to fractions with lower binding energy and greater mobility (Brunetto et al., 2016 ). Sequential soil analysis Table 2 shows the mean values of copper concentrations (mg/kg) and mass fraction (in percentages) resulting from sequential extraction. The results of the analysis from individual locations are given in the Supplementary file (Table S1 ). Table 2 Cu distribution by fractions of the investigated soils; minimal and maximal concentrations and mean ± standard deviation; n = 9 I - Exchangeable and easily accessible; II - Reducible; III - Oxidizable; IV – Residual Fractions ↓ mg/kg dry weight wt.% dry weight min.-max. Mean ± SD min.-max. Mean ± SD pseudo-total 56.1–230 127 ± 57.9 I 1.82–5.01 3.46 ± 1.09 1.94–3.72 2.87 ± 0.61 II 8.47–56.4 25.6 ± 15.4 13.7–24.5 19.2 ± 3.67 III 34.7–95.2 62.7 ± 26.0 39.4–61.8 50.6 ± 7.10 IV 11.0–78.0 35.7 ± 20.7 18.8–36.3 27.4 ± 6.43 The largest part of Cu is bound to humic acids and other organic matter in the soil. Copper follows the following trend of distribution per fraction: III > IV > II > I. As in this and many previous studies, different kinds of soils have shown that non-residual fractions of Cu are mainly associated with the oxidizable phase, occurring as organically complexed metal species. This is because Cu shows a high affinity with humic substances, which is a fraction of the natural organic matter chemically active in complexing such metals (Kastratović et al., 2016 ). Cu extraction by carboxylic acids and EDTA In our experiments, we extracted 98.5 ± 42.7 mg/kg of copper using EDTA from sample which, when calculated as a wt.%, amounts to 79.9 ± 7.12 relative to the pseudo-total Cu content (127 mg/kg). (The amount of Cu extracted by EDTA in mg/kg and wt.% from the individual tested sites are given in the Supplementary file, Table S2 ). This is several times more than Cu extracted by amino acids and carboxylic acid salts. The average value of Cu extracted with 10 amino acids is 34.7 ± 16.7 mg/kg, or 30.3 ± 5.46 wt.% (next chapter) relative to pseudo-total Cu content. The mean value of Cu extracted by four Na-salts of carboxylic acids (acetate, oxalate, tartrate, citrate) is 9.91 ± 7.49 mg/kg. Amino acids and carboxylate anions extracted 35.2% and 10.1% compared to EDTA, respectively (Fig. 2 ). Wuana et al. ( 2010 ) reported extraction yields of 70.30% for Cu using EDTA. EDTA leaching removes 96–100% of Cu from labile soil fractions and leads to the dissolution of minerals from the residual fraction. Their work demonstrated that the extraction efficiency decreases in the following order: EDTA > citric acid > tartaric acid. They further stated that among the metals they investigated (Cd, Cu, Ni, Pb, Zn), Cu exhibited the strongest chelating ability. Natural low-molecular-weight organic acids, including mono-, di-, and poly-carboxylic acids, result from the decomposition of plant and animal residues in the soil and are natural products of root exudates. Low-molecular-weight carboxylic acids are chelating reagents capable of extracting heavy metals from the soil by forming complexes. While carboxylic acids are less effective at removing heavy metals from the soil than aminopolycarboxylic acids (EDTA and its derivatives), they offer several significant advantages. At the same time, they are biodegradable and environmentally friendly. Numerous research studies are based on the use of various natural low-molecular-weight organic acids for metal extraction from the soil (ITRC, 2003; Wuana et al., 2010 ; Xiao et al., 2019 ; Zhang et al., 2022 ). The ability to remove metals using carboxylic acids depends on the dissociation constant (pKa), the ligand's shape, and the number of carboxyl groups (-COOH). They are effective only in removing exchangeable, carbonate, and reductive fractions of heavy metals (ITRC, 2003; Peters, 1999 ). In our study, we investigated the capacity of Cu removal from soil using Na-salts of acetic (monocarboxylic), oxalic (dicarboxylic), tartaric (hydroxy-dicarboxylic), and citric as tricarboxylic acid. The descending order of Cu removal was as follows: citrate (20.3 mg/kg) > oxalate (10.2) > tartrate (5.40) > acetate (3.82). (The amount of Cu extracted by carboxylic acid salts in mg/kg and wt.% from the individual tested sites are given in the Supplementary file, Table S3 and S4). When calculated relative to the pseudo-total, the range of extracted Cu varied from 2.99 wt.% for acetate to 15.9 wt.% for citrate. These results are comparable to the amino acid arginine (4.28 wt.%). Compared to other examined amino acids (25.1 wt.% − 40.2 wt.%), carboxylates leached significantly less Cu from the soil. Figure 2 shows a comparison of the mean values of Cu extracted by the chelators used in relation to the content in the sediment by fraction. In the study by Wuana et al. ( 2010 ), it was reported that citric acid mainly removes Cu from the acid-exchangeable and reducible soil fractions. They observed that after leaching the soil with citric acid, only a small portion of Cu (8%) was removed from the residual fraction. Ke et al. ( 2006 ) demonstrated that tartaric acid is effective in removing Cu from the easily-exchangeable carbonate fractions of contaminated soil. Around 13% of Cu remained unextracted from the exchangeable fraction even after leaching the soil with tartaric acid (Wuana et al., 2010 ). Cu extraction by amino acids The results of the extracted amounts of Cu, minimal and maximal concentrations and mean ± standard deviation, with the tested amino acids from nine locations are presented in Table 3 . The results of the analysis from individual locations are given in the Supplementary file (Table S5 and S6). Table 3 Cu amounts extracted by amino acids in mg/kg and wt.% relative to pseudo-total content, minimal and maximal concentrations and mean ± standard deviation; n = 9 mg/kg dry weight wt.% relative to pseudo-total min.-max. Mean ± SD min.-max. Mean ± SD Gly 18.7–60.5 36.8 ± 15.7 (bc) 25.1–33.7 29.5 ± 3.42 (bc) Ala 17.4–50.6 31.9 ± 12.9 (c) 21.0–31.0 25.9 ± 3.76 (c) Val 17.7–58.7 35.4 ± 15.2 (bc) 24.2–32.5 28.3 ± 3.05 (bc) Leu 19.2–64.8 38.5 ± 17.0 (bc) 25.3–35.6 30.6 ± 3.63 (bc) Phe 17.9–55.1 34.2 ± 14.4 (bc) 23.1–31.9 27.5 ± 3.38 (bc) Gly-gly 14.9–58.2 32.8 ± 15.3 (c) 21.8–29.3 25.8 ± 2.59 (c) Ser 20.5–64.7 41.6 ± 17.7 (b) 26.8–38.1 33.5 ± 4.57 (ab) Thr 19.8–62.5 39.4 ± 16.8 (bc) 26.4–36.7 31.5 ± 4.02 (bc) His 22.8–88.5 51.0 ± 22.4 (a) 36.9–44.2 40.3 ± 2.45 (a) Arg 2.39–11.2 5.46 ± 2.72 (d) 3.43–5.06 4.28 ± 0.53 (d) * Copper content values with the same letter(s) are not significantly different at p = 0.05 in the column By applying the analysis of variance (ANOVA) to the results of extracted Cu content expressed in mg/kg using different amino acids, F = 4.927 was calculated. The tabulated F-value for a significance level of 0.05 and the number of degrees of freedom φ1 = 9 and φ2 = 80 is 2.01. To determine which mean values significantly differ from each other, we applied the Tukey-Snedecor test. The tabulated value for Q (10,80) = 4.62, so the maximum allowed difference between the mean values D = 24.254. The same calculation was applied to Cu contents expressed in wt.% relative to the total content in the soil. There is a significant difference between individual mean values of Cu (wt.%) extracted with different amino acids (F cal =70.625, F critical =2.01). The calculated maximum allowed difference between the values was D = 5.102. The results of the statistical analysis of mean values by individual locations are shown in the Supplementary file (Table S5 and S6). The least significant difference for p = 0.05, based on Fisher's test between the mean values of Cu (mg/kg) washed with amino acids for all locations, is LSD = 8.694. For results expressed in wt.%, LSD = 7.219. Of the tested amino acids, histidine extracted the highest Cu amount. Through its free electron pair, the imidazole nitrogen participates in the creation of a coordinative covalent bond with Cu. The lowest amount was extracted with arginine. After His, the largest amounts of Cu were extracted with the polar amino acids serine (Ser) and threonine (Thr) and the hydrophobic leucine (Leu). It is likely that the hydroxyl group of the side chain of Ser and Thr can contribute to the copper chelation process. This analysis shows that there is no statistically significant difference between the mean values for Ser, Thr, Leu, Gly, Val and Phe. Alanine and the dipeptide gly-gly had similar mean values of extracted Cu. These results also show a advantage of Ala and Gly-gly over Arg (Table 3 ). The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His > Ser > Thr > Leu > Gly > Val > Phe > Gly-Gly ~ Ala > Arg. Interestingly, His has the highest stability constant with Cu (18.1), while arginine has the lowest (13.9). Other amino acids have similar stability constants with Cu. Such data suggest that the stability constants of the complex depend very little on the steric factor and hydrophilicity of amino acids. Similar to our results, Doleva et al. (2020) found that the extraction of Cu for most of the tested amino acids was around 30–35%. They further report that the level of Cu removal by threonine, aspartic acid and histidine is comparable to that by EDTA, while amino acids containing a non-polar side chain consistently showed lower extraction efficiency. Cysteine containing a thiol group was found to be ineffective for extraction as it forms a precipitate with Cu 2+ . It is important to note that most amino acids extracted less than 5% of Ca (except for acidic Asp), compared to ~ 40% Ca extracted by EDTA, suggesting that they are potentially very suitable reagents for soil. Karczewska and Milko ( 2010 ) tested histidine (His) for the extraction of Cu, Pb, and Zn and found it to be competitive with EDDS (an EDTA derivative). The results of the studies mentioned above show that hydrophobic, nucleophilic, and steric properties are not decisive factors that determine the efficiency of metal extraction using amino acids. The functional groups of the side chain play a much more important role in metal extraction. Yamauchi et al. ( 2002 ) demonstrated that His, which is a potentially tridentate ligand, is capable of strongly coordinating metal cations via imidazole nitrogen. By applying the analysis of variance (ANOVA) to the mean values of extracted Cu expressed in mg/kg in relation to 9 different locations, F = 13.536 > F critical,9,81 =2.21, was calculated. The maximum allowed difference between mean values is D = 19,326. The results of the statistical analysis of the differences between the mean values of individual locations are shown in the Supplementary file (Table S5 and S6). Mean values from locations S1, S5 and S9 are significantly higher than the others. The lowest amounts of Cu were extracted from locations S2, S3, S4. The difference in mean values between locations S5, S7 and S9 as well as S6, S7, S8 and S9 is insignificant. The least significant difference for p = 0.05, based on Fisher's test between the mean values of Cu (mg/kg) washed with amino acids for all locations, is LSD = 16.6. The descending order of locations by extracted Cu (wt.%) is: S1 > S5 > S9 > S7 > S6 > S8 > S3 > S4 > S2. This order of locations is the same as the order of the increasing amount of pseudo-total Cu content. The content in the residual (immobilized) fraction is also important for the amount of Cu extracted from certain locations. The selected locations significantly differ in terms of organic matter and carbonate content (Table 1 ). There is a statistically insignificant difference between the results of the mean values of washed Cu (expressed in wt.%) from individual locations, with the tested amino acids (F cal =0.920, F critical =2.21). The calculated maximum allowed difference between the values was D = 14.317. The least significant difference for p = 0.05, based on Fisher's test between the mean values of Cu (wt.%) is LSD = 16.6. The permissible difference between the results (expressed in wt.%) from different locations indicates that the physicochemical characteristics of the soil do not have a significant effect on Cu extraction. The structure of amino acids has a much greater impact. Conclusions The largest part of Cu (50.6% ± 7.10 wt.%) in the investigated vineyard soils is bound to humic acids and other organic substances in the soil. The copper content in the exchangeable and easily accessible fraction, reducible, and residual fractions is in the following order: 2.87 ± 0.61 wt.%, 19.2 ± 3,67 wt.%, and 27.4 ± 6.43 wt.%. The average content of Cu extracted with amino acids is 34.7 ± 16.7 mg/kg or 30.3 ± 5.46 wt.% relative to the pseudo-total content. This is more than what was extracted with carboxylic acid salts (8.45 wt.%) but less than with EDTA – 79.9 wt.%. However, amino acids have several very favorable properties, recommending them for the extraction of Cu and other heavy metals from soil. The descending order of Cu removal with carboxylic acid salts was: citrate (20.3 mg/kg) > oxalate (10.2) > tartrate (5.40) > acetate (3.82). When calculated relative to the pseudo-total, the range of extracted Cu varied from 2.99 wt.% for acetate to 15.9 wt.% for citrate. The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His > Ser > Thr > Leu > Gly > Val > Phe > Gly-Gly ~ Ala > Arg. The results of this paper show that the amino acid structure is the most important factor for efficient Cu extraction, while the physicochemical properties of the vineyard soil have less influence. Declarations Competing Interests: The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics approval All authors have read, have understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Consent for publication The authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. Data availability All data generated or analyzed during this study are included in this published article and the supplementary material. Authors’ contribution Bojana Knežević carried out the sampling and analysis of the samples. Vlatko Kastratović wrote the main manuscript, pictures/illustrations and preparation of the tables. All authors contributed to the structure of the manuscript and were involved in the review and editing of the manuscript. References Brunetto, G., Bastos de Melo, G.W., Terzano, R., Del Buono, D., Astolfi, S., Tomasi, N., Pii, Y., Mimmo, T., & Cesco, S. (2016). Copper accumulation in vineyard soils: Rhizosphere processes and agronomic practices to limit its toxicity. Chemosphere, 162, 293–307. https://doi.org/10.1016/j.chemosphere.2016.07.104 . Chao, J.C., Hong, A., Okey, R.W., & Peters, R.W. (1998). Selection of chelating agents for remediation of radionuclide-contaminated soil. Proceedings of the 1998 Conference on Hazardous Waste Research, 142–155. Couto, R.R., Lazzari, C.J.R., Trapp, T., De Conti, L., Comin, J.J., Martins, S.R., Filho, P.B., & Brunetto, G. (2016). 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Fernandez-Calvino, D., Novoa-Munoz, J.C., Díaz-Ravina, M., & Arias-Estevez, M. (2009). Copper accumulation and fractionation in vineyard soils from temperate humid zone (NW Iberian Peninsula). Geoderma, 153, 119–129. https://doi.org/10.1016/j.geoderma.2009.07.024 . Ferraro, A., Fabbricino, M., van Hullebusch, E.D., Esposito, G., & Pirozzi, F. (2016). Effect of soil/contamination characteristics and process operational conditions on aminopolycarboxylates enhanced soil washing for heavy metals removal: a review. Reviews in Environmental Science and Biotechnology, 15, 111–145. https://doi.org/10.1007/s11157-015-9378-2 . Fleck, M., & Petrosyan, A.M. (2014). Salts of Amino Acids: Crystallization, Structure and Properties. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-319-06299-0 . Gao, X.L., Chen, C.A., Wang, G., Xue, & Q.Z. (2010). Environmental status of Daya Bay surface sediments inferred from a sequential extraction technique. Estuarine, Coastal and Shelf Science, 86, 369–378. https://doi.org/10.1016/j.ecss.2009.10.012 . Gleyzes, C., Tellier, S., & Astruc, M. (2002). Fractionation studies of trace elements in contaminated soils and sediments: a review of sequential extraction procedures. TrAC Trends in Analytical Chemisty, 21, 451–467. https://doi.org/10.1016/S0165-9936(02)00603-9 . Houba, V.J.G., Uittenbogaard, J., & Pellen, P. (1996). Wageningen evaluating programmes for analytical laboratories (WEPAL) organization and purpose. Communications in Soil Science and Plant Analysis, 27(3–4), 421–431. https://doi.org/10.1080/00103629609369565 . Interstate Technology and Regulatory Council (2003). Characterization and Remediation of Soils at Closed Small Arms Firing Ranges. Washington, DC: Technical/Regulatory Guidelines; 2003. Jelusic, M., & Lestan, D. (2014). Effffect of EDTA washing of metal polluted garden soils. Part I: toxicity hazards and impact on soil properties. Science of the Total Environment, 475, 132–141. https://doi.org/10.1016/j.scitotenv.2013.11.049 . Kabala, C., & Singh, B.R. (2001). Fractionation and mobility of copper, lead and zinc in soil profiles in the vicinity of a copper smelter. Journal of Environmental Quality, 30(2):485–492. https://doi.org/10.2134/jeq2001.302485x . Kabata Pendias, A. (2010). Trace Elements in Soils and Plants. 4th Edition, Boca Raton, CRC Press, Pages 548. https://doi.org/10.1201/b10158 . Karczewska, A., & Milko, K. (2010). Effects of chelating agents on copper, lead an zinc solubility in polluted soils and tailings produced by copper industry. Ecological Chemisty and Engineering S, 17(4–5), 395–403. Karlsson, T., Persson, P., & Skyllberg, U. (2006). Complexation of Copper(II) in organic soils and in dissolved organic matter EXAFS evidence for chelate ring structures. Environmental Science & Technology, 40, 2623–2628. https://doi.org/10.1021/es052211f . Kastratović, V., Jaćimović, Ž., Bigović, M., Đurović, D., & Krivokapić, S. (2016). Environmental Status and geochemical assessment Sediments of Lake Skadar, Montenegro. Environmental Monitoring and Assessment, 188(8), 449. https://doi.org/10.1007/s10661-016-5459-0 . Ke, X., Li, P., Zhou, Q., Zhang, Y., & Sun, T. (2006). Removal of heavy metals from a contaminated soil using tartaric acid. Journal of Environmental Sciences, 18(4), 727–733. https://doi.org/10.1007/BF03326158 Khanlari, Z.V., & Jalali, M. (2008). Concentrations and chemical speciation of five heavy metals (Zn, Cd, Ni, Cu, and Pb) in selected agricultural calcareous soils of Hamadan Province, western Iran. Archives of Agronomy and Soil Science, 54, 19–32. https://doi.org/10.1080/03650340701697317 . Laudicina, V.A., Palazzolo, E., & Badalucco, L. (2013). Natural organic compounds in soil solution: potential role as soil quality indicators. Current Organic Chemisty, 17, 2991–2997. https://doi.org/10.2174/13852728113179990120 . Lestan, D. (2015). Remediation of toxic metal-contaminated soil using EDTA soil washing. In: Varma, A., Sherameti, I. (Eds.), Heavy Metal Contamination of Soils. Springer International Publishing Switzerland, pp. 395–429. https://doi.org/10.1007/978-3-319-14526-6 . Liao, M.T., Hedley, M.J., Woolley, D.J., Brooks, R.R., & Nichols, M.A. (2000). Copper uptake and translocation in chicory ( Cichorium intybus L. cv Grasslands Puna) and tomato ( Lycopersicon esculentum Mill. cv Rondy) plants grown in NFT system. II. The role of nicotianamine and histidine in xylem sap copper transport. Plant Soil, 223, 245–254. Liu, X.Q., & Lee, K.S. (2013). Effect of mixed amino acids on crop growth. In: Aflakpui, D.G. (Ed.), Agriculture Science. InTech, pp. 119–158. https://doi.org/10.5772/37461 . Manceau, A., & Matynia, A. (2010). The nature of Cu bonding to natural organic matter. Geochimica et Cosmochimica Acta, 74, 2556–2580. https://doi.org/10.1016/j.gca.2010.01.027 . Manouchehri, N., & Bermond, A. (2006). Study of trace metal partitioning between soil – EDTA extracts and Chelex-100 resin. Analytica Chimica Acta, 557, 337–343. https://doi.org/10.1016/j.aca.2005.10.038 . McBride, M. (1994). Trace and toxic elements in soils. In: McBride, M. (Ed.), Environmental Chemistry of Soils. Oxford Univ. Press, New York; 308–341. Ministry of Agriculture of Montenegro, Action Plan for 2023, Vitticulture and wine production. https://www.gov.me/en/ Ministry of Sustainable Development and Tourism of Montenegro, Report on the strategic assessment of the impact on the environment of the Spatial - urban plan of the Municipality of Danilovgrad in the part of the general urban development "Spuž", Podgorica; 2019. (in Montenegrin) Nelson, D.W., & Sommers, L.E. (1982). Total carbon, organic carbon and organic matter. p. 539–579. In: A. L. Page et al. (ed.) Methods of soil analysis: Part 2. Chemical and microbiological properties. ASA Monograph Number 9. Pertsemli, E., & Voutsa, D. (2007). Distribution of heavy metals in Lakes Doirani and Kerkini, Northern Greece. Journal of Hazardous Materials, 148(3), 529–537. https://doi.org/10.1016/j.jhazmat.2007.03.019 . Peters, R.W. (1999). Chelant Extraction of Heavy Metals from Contaminated Soils. Journal of Hazardous Materials, 66, 151–210. https://doi.org/10.1016/s0304-3894(99)00010-2 . Pueyo, M., Sastre, J., Hernandez, E., Vidal, M., Lopez-Sanchez, J.F., & Rauret, G. (2003). Prediction of trace element mobility in contaminated soils by sequential extraction. Journal of Environmental Quality, 32(6), 2054–2066. https://doi.org/10.2134/jeq2003.2054 . Roviello, V., Caruso, U., Dal Poggetto, G., & Naviglio, D. (2021). Assessment of Copper and Heavy Metals in Family-Run Vineyard Soils and Wines of Campania Region, South Italy. International Journal of Environmental Research and Public Health, 18(16), 8465. https://doi.org/10.3390/ijerph18168465 . Strawn, D.G., & Baker, L.L. (2009). Molecular characterization of copper in soils using X-ray absorption spectroscopy. Environmental Pollution, 157(10), 2813–2821. https://doi.org/10.1016/j.envpol.2009.04.018 . Tandy, S., Bossart, K., Mueller, R., Ritschel, J., Hauser, L., Schulin, R., & Nowack, B. (2004). Extraction of heavy metals from soils using biodegradable chelating agents. Environmental Science & Technology, 38, 937–944. https://doi.org/10.1021/es0348750 . Tsang, D.C.W., Zhang, W., & Lo, I.M.C. (2007). Copper extraction effectiveness and soil dissolution issues of EDTA-flushing of artificially contaminated soils. Chemosphere, 68, 234–243. https://doi.org/10.1016/j.chemosphere.2007.01.022 . USEPA Method 3051a (2007). Microwave assisted acid digestion of sediments, sludges, soils and oils, Revision 1. Warren, C.R. (2014). Organic N molecules in the soil solution: what is known, what is unknown and the path forwards. Plant Soil 375, 1–19. https://doi.org/10.1007/s11104-013-1939-y . Wise, D.L., Trantolo, D.J., Cichon, E.J., Inyang, H.I., & Stottmeiste, U. (Eds.), Remediation Engineering of Contaminated Soils, Marcel Decker, NY, USA; 2000. Wuana, R.A., & Okieimen, F.E. (2011). Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecology 1–20. https://doi.org/10.5402/2011/402647 . Wuana, R.A., Okieimen, F.E., & Imborvungu, J.A. (2010). Removal of Heavy Metals from a Contaminated Soil Using Organic Chelating Acids. International Journal of Environmental Science and Technology, 7(3), 485–496. https://doi.org/10.1007/bf03326158 . Xiao, R., Ali, A., Wang, P., Li, R., Tian, X., & Zhang, Z. (2019). Comparison of the Feasibility of Different Washing Solutions for Combined Soil Washing and Phytoremediation for the Detoxification of Cadmium (Cd) and Zinc (Zn) in Contaminated Soil. Chemosphere, 230, 510–518. https://doi.org/10.1016/j.chemosphere.2019.05.121 . Yamauchi, O., Odani, A., & Takani, M. (2002). Metal–amino acid chemistry. Weak interactions and related functions of side chain groups. Dalton Transactions, 18:3411–3421. https://doi.org/DOI:10.1039/b202385g . Zhang, H., Xu, Y., Kanyerere, T., Wang, Y-s., & Sun, M. (2022). Washing Reagents for Remediating Heavy-Metal-Contaminated Soil: A Review. Frontiers in Earth Science, 10:901570. https://doi.org/10.3389/feart.2022.901570 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4263629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294624968,"identity":"f56754f3-cde8-41fd-b7dc-9e2ada2cc6a3","order_by":0,"name":"Vlatko Kastratović","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACdgYGxgaGBDkStDAzMDYCtRiTriWxgWgdBoeZnz+cUZGWvuH48QsMP2oYovkJa2EzbNxwJicXiAsYe44x5M4kZJ9kM4Nh48O2itwNN3gSGHgbGHI3HCCohf0jSEu6AVAL41+glv2EtPAz8xg2bmzLSTC4wX6AGWwLIb8AtRTOnHEmzXDmmRyGwzLHJHJnELKFjb19w8eeimR5vuPHHz58U2OT299AyBoE4DEAmi9BvHogYH9AkvJRMApGwSgYOQAAr9dDxwUU1hAAAAAASUVORK5CYII=","orcid":"","institution":"Faculty of Natural Sciences and Mathematics, Universtity of Montenegro","correspondingAuthor":true,"prefix":"","firstName":"Vlatko","middleName":"","lastName":"Kastratović","suffix":""},{"id":294624978,"identity":"2747e58a-06a6-468a-825d-d6f21f231a83","order_by":1,"name":"Bojana Knežević","email":"","orcid":"","institution":"Faculty of Natural Sciences and Mathematics, Universtity of Montenegro","correspondingAuthor":false,"prefix":"","firstName":"Bojana","middleName":"","lastName":"Knežević","suffix":""}],"badges":[],"createdAt":"2024-04-14 04:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4263629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4263629/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-024-11875-w","type":"published","date":"2024-09-24T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55228691,"identity":"f9defbc8-bc6f-4efd-8845-dfeffba24372","added_by":"auto","created_at":"2024-04-24 11:49:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1309848,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the sampling stations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4263629/v1/5316e718fbbdfa13a7bb80ff.png"},{"id":55228689,"identity":"dbe685a3-e3a5-4bf5-9802-b81b97d42222","added_by":"auto","created_at":"2024-04-24 11:49:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30638,"visible":true,"origin":"","legend":"\u003cp\u003eAmounts of Cu extracted by EDTA, amino acids and carboxylic acid salts in relation to the amount in the sediment\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4263629/v1/8b834e4ce31b70985fc928eb.png"},{"id":65627347,"identity":"ebc46add-9ca3-4443-9a86-ed53775e7bea","added_by":"auto","created_at":"2024-09-30 16:15:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1651556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4263629/v1/39aa3a79-f9cd-4510-8a0f-ad9dc8d0bf1d.pdf"},{"id":55228693,"identity":"25598639-e172-4715-914a-517d1610c22b","added_by":"auto","created_at":"2024-04-24 11:49:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27148,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4263629/v1/d32154785a3881bfbe8ab4af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRemoval of Copper From the Vineyard Land of Pješivci (Montenegro) Using Amino Acids\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe culture of growing vines and winemaking in Montenegro dates back to the pre-Roman period. Later, the ancient artisans and Romans further advanced Montenegro's viticulture. Viticulture continued to develop during the Middle Ages, especially along the shores of Lake Skadar. Today, the largest vineyards are owned by the company \"13. Jul - Plantaže,\" with approximately 10\u0026nbsp;million vine plants on an area of 2,300 hectares. In addition to this company, there are 312 registered grape producers with vineyards covering around 2,850 hectares (Ministry of Agriculture of Montenegro, 2023).\u003c/p\u003e \u003cp\u003eOne of the main challenges vine growers face is how to protect the plant from numerous harmful organisms attacking it, thereby reducing yield and the quality of grapes. The most significant and common diseases vine is exposed to in Montenegro's climate are downy mildew, powdery mildew, and gray rot (mold). Copper compounds in the form of fungicides are most commonly used as a measure of protection against these vine diseases. The most common Cu compounds in fungicides are copper(II) sulfate pentahydrate, copper(II) oxide, copper hydroxide, and copper oxychloride. The number of phytosanitary treatments depends on several factors and, most of all, on weather conditions. Typically, around a dozen treatments are done throughout the year with a dispersion of approximately 5\u0026ndash;10 kg of Cu per hectare (Ministry of Agriculture of Montenegro, 2023).\u003c/p\u003e \u003cp\u003eCopper is an essential plant micronutrients, but it can be toxic at higher concentrations. Copper is a component of several complex proteins - enzymes and plays a significant role in several physiological processes in plants. The role of Cu in biochemical processes is influenced by its small ionic radius, oxidation state (Cu\u003csup\u003e2+\u003c/sup\u003e or Cu\u003csup\u003e+\u003c/sup\u003e), and the formation of numerous complex chelate-type compounds (Kabata Pendias, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetals in the soil can be found in several forms: as dissolved free ions or dissolved complexes in pore water, as they are deposited as organic and inorganic compounds, or as they get adsorbed onto specific solid phase soil fractions. Metals in soil exhibit a specific mobility and bioavailability to plants.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe origin of metals in soil is geochemical (natural) and anthropogenic. Metals of geochemical origin in soil are typically found in less available or entirely unavailable forms for plants. Soils have a pronounced ability to bind anthropogenic metals through ion exchange or adsorption, which have a significantly higher bioavailability (Gleyzes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wise et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Data on the total or \"pseudo\"-total metal content in the soil do not provide a clear picture of how they bind to the soil. Information about the mobility, bioavailability, and toxicity of metals from the soil is of much greater importance (Gao et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The fact that metals are present in the soil does not imply that they are completely available for plant uptake (Pertsemli \u0026amp; Voutsa, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The vine plant does not have the ability to absorb significant amounts of copper (Roviello et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Liao et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) reported generally low uptake of Cu by plants and inefficient transport from roots to shoots in plantations using Cu fertilizers.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor many years, the remediation of heavy metals has often involved the use of ex-situ soil washing with chelating reagents. The application of chelants to remove metals from contaminated soils is based on their efficiency, widespread application, selectivity, and reproducibility. (Chao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dermont et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dolev et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ferraro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kabala \u0026amp; Singh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wuana \u0026amp; Okieimen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Synthetic metal chelating agents (such as EDTA), which are widely used in soil-washing processes are highly effective (Dermont et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dolev et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lestan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, they have many disadvantages. They are non-biodegradable and can impact the biogeochemical soil reactions (Jelusic \u0026amp; Lestan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lestan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, they have a strong affinity for essential metals (e.g., Ca), thereby altering the soil structure (Tsang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Since considerable amounts of EDTA are used to chelate essential metals in the soil (Ca, Mg, Fe, Al, etc.), it makes the remediation process less efficient (Tandy et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Manouchehri \u0026amp; Bermond, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProtein and other natural amino acids also possess a pronounced ability to complex metals, but they have been much less investigated (Farkas \u0026amp; Sovago, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fleck \u0026amp; Petrosyan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Laudicina et al., 2016). Amino acids have a lower metal chelation ability compared to EDTA and its derivatives, but they have numerous other advantages in comparison: they are biodegradable and environmentally friendly; they serve as a source of nitrogen for plants; they help in the uptake of essential elements by plants; in some cases, are used in agriculture as fertilizers; they do not chelate essential soil metals (e.g., Ca), and thus, they do not alter soil structure; they are inexpensive (Dolev et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu \u0026amp; Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Warren, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe immediate objectives of this research are to: (1) investigate the ability of 9 amino acids and one dipeptide to extract Cu from different samples of vineyard soil, (2) consider whether soil Cu speciation affects the extraction efficiency, (3) compare the chelating ability of amino acids with other \"green\" chelators, and also the mutual ability of amino acids to extract Cu depending on the structure of the amino acid itself. Our intention is to have the results of this study assist in the optimization and selection of amino acids for the remediation of Cu-contaminated soil.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area and sampling\u003c/p\u003e \u003cp\u003eThe selected soils are concentrated in the wine-growing sub-region of Pješivci (approximate coordinates: latitude 42\u0026deg; 24' and longitude 19\u0026deg; 03') at a distance of 30 km from Podgorica, Montenegro (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of pedological characteristics, the explored site features vertic eutric brown soil on alluvial and colluvial deposits - a variety of vertic cambisol (Ministry of Sustainable Development and Tourism of Montenegro, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The investigated wine-growing sub-region is primarily influenced by a slightly modified Mediterranean climate. The area is characterized by long, hot, and dry summers and relatively mild and rainy winters. In January, temperatures are lower than those in coastal areas at approximately the same latitude, while during the summer, temperatures tend to be somewhat higher (Ministry of Sustainable Development and Tourism of Montenegro, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSampling was conducted with nine vineyard soils using the Eijkelkamp soil sampling kit. The vineyards are located at a distance of 100\u0026ndash;500 meters from each other. Composite samples were made by mixing cross-collected soil from 10 sampling sites from a 20 cm deep layer, with a distance of 2.5 meters between sampling sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSample preparation for analysis\u003c/p\u003e \u003cp\u003eThe soil samples were dried in the air and then in a drying oven at a temperature of 75 \u003csup\u003e0\u003c/sup\u003eC for 48 hours. Dried soil samples were crushed in an agate mortar and sieved through a series of sieves (\u0026lt;\u0026thinsp;2.0, 1.5, 1.0, and 0.5 mm). Approximately 0.5 g (\u0026plusmn;\u0026thinsp;0.0001 g) of the soil sample under pressure and high temperature was mineralized with a mixture of HCl (wt. % 36): HNO\u003csub\u003e3\u003c/sub\u003e (wt. % 65) (v/v 3:1) in a microwave oven, Milestone Microwave Ethos 1 (USEPA, 2007). After mineralization, the solutions were diluted with 2M HNO\u003csub\u003e3\u003c/sub\u003e to the final volume of 50.0 mL.\u003c/p\u003e \u003cp\u003eAnalysis of soil and extracted Cu\u003c/p\u003e \u003cp\u003eIn our study, Cu from vineyard soils was extracted with amino acids, Na-salts of carboxylic acids (acetate, oxalate, tartrate, citrate), and complexing reagent (EDTA). The following amino acids were used: glycine (Gly), alanine (Ala), valine (Val), leucine (Leu), phenylalanine (Phe), serine (Ser), threonine (Thr), histidine (His), arginine (Arg ) and the dipeptide glycyl-glycine (Gly-Gly).\u003c/p\u003e \u003cp\u003eIn order to determine the optimal Cu leaching conditions, the following were examined: soil particle size (0.5, 1.0, 1.5, and 2.0 mm); concentration of washing agent (0.05, 0.1 and 0.2 mol/L); contact time (0.5 h, 1.0 h and 2.0 h); soil/solution ratio, w/v, (1:25, 1:50 and 1:100). The pH of the extraction solution was not adjusted, and in all Cu extraction experiments the mixtures were buffered after a few moments to the pH of the tested soil samples. After preliminary tests, we decided on the following experimental conditions: soil particle size 0.5 mm, Cu extraction solution concentration 0.1 mol/L, extraction time 1 h, and soil/solution ratio 1 g/50.0 mL.\u003c/p\u003e \u003cp\u003eIn order to determine the distribution of Cu in the soil, we applied a modified BCR (the Community Bureau of Reference of the European Union) sequential extraction procedure of the sample sediment (Pueyo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pH (in H\u003csub\u003e2\u003c/sub\u003eO and KCl) of the tested samples of vineyard soil was determined by potentiometry. The content of oxidizable carbon was determined by the WalkleyBlack method (Nelson \u0026amp; Sommers, 1982). Total organic carbon was obtained by multiplying the content of oxidizable carbon by 1.334. The content of organic matter was calculated based on the assumption that organic matter contains 5 % organic carbon. The percentage (wt. %) of CaCO\u003csub\u003e3\u003c/sub\u003e in soils was determined by the volumetric calcimeter method after the HCl attack.\u003c/p\u003e \u003cp\u003eDetermination of the concentration of extracted Cu in the samples of soils was conducted by inductively coupled plasma optical emission spectroscopy (ICP-OES) technique on a \"Spectro Arcos\" device. All samples of soils were prepared for three times, and their average value was analyzed. In each batch of ten samples, the blank solution was measured. The analytical accuracy of the results was checked using several reference soil samples from the interlaboratory calibration program (Houba et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The reproducibility of the results was within 10% of the certified values.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMicrosoft Excel 2000 was used to calculate the mean values and standard deviation. The significance of variations in the extracted Cu content among different amino acids was assessed using one-way analysis of variance (ANOVA I and II). A post-hoc test was applied when differences were significant (SPSS 2012). To compare the mean values among individual amino acids from all three locations, the Fisher's LSD test for the least significant difference was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003ePhysicochemical characteristics of the tested soils\u003c/p\u003e \u003cp\u003eAn overview of the mean values of the physicochemical characteristics of the tested soils, which are relevant to the behavior of metals, is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical characteristics of the tested soils\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH (H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH (KCl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOxidizable C (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTOC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCarbonates (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin - max\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.98\u0026ndash;7.50\u003c/p\u003e \u003cp\u003e6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.38\u0026ndash;7.16\u003c/p\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u0026ndash;1.71\u003c/p\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u0026ndash;2.28\u003c/p\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026ndash;3.92\u003c/p\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6\u0026ndash;46.5\u003c/p\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe pH value of the tested vineyard soils ranges from 5.98 to 7.50. Strawn and Baker (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) showed that within the pH range of 5.2 to 7.1, Cu is primarily complexed with soil organic matter in the form of cyclic five-membered ring chelates. Karlsson et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) demonstrated in their paper that at pH 4.8\u0026ndash;6.3, Cu\u003csup\u003e2+\u003c/sup\u003e forms a five-membered chelate ring with glutamic acid. Manceau and Matynia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) showed that Cu\u003csup\u003e2+\u003c/sup\u003e at pH 4.5 and 5.5 builds five-membered chelates similar to Cu(malate)\u003csub\u003e2\u003c/sub\u003e at a Cu concentration of 100\u0026ndash;300 ppm and six-membered ones similar to Cu(malonate)\u003csub\u003e1-2\u003c/sub\u003e at higher Cu concentrations. Due to copper's high affinity for soil organic matter and colloids, Cu is a poorly mobile element in almost neutral soils. Consequently, farmers have been able to apply significant amounts of Cu salts to organic soils over time without causing any crop toxicity (Brunetto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McBride, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn alkaline soils, soluble Cu\u003csup\u003e2+\u003c/sup\u003e complexes (hydroxy-, carbonate-) can be formed, increasing the mobility of Cu in the soil (Brunetto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McBride, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Several studies (Brunetto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fernandez-Calvino et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Khanlari \u0026amp; Jalali, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) have shown that in weekly alkaline soils, the largest Cu portion remains in the residual fraction after successive extractions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese considerations demonstrate that Cu is quite stable in the soil, and that its bioavailability depends on the nature of organic matter, organic matter minerals Fe, Al, Mn, its amount, as well as on carbonate, soil pH, and cation exchange capacity (Brunetto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Couto et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fernandez-Calvino et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Copper adsorption in the soil primarily occurs on organic matter, while the remainder is bound to fractions with lower binding energy and greater mobility (Brunetto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSequential soil analysis\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the mean values of copper concentrations (mg/kg) and mass fraction (in percentages) resulting from sequential extraction. The results of the analysis from individual locations are given in the Supplementary file (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCu distribution by fractions of the investigated soils; minimal and maximal concentrations and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; n\u0026thinsp;=\u0026thinsp;9 I - Exchangeable and easily accessible; II - Reducible; III - Oxidizable; IV \u0026ndash; Residual\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFractions \u0026darr;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emg/kg dry weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ewt.% dry weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin.-max.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin.-max.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epseudo-total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.1\u0026ndash;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u0026thinsp;\u0026plusmn;\u0026thinsp;57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82\u0026ndash;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94\u0026ndash;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.47\u0026ndash;56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7\u0026ndash;24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.7\u0026ndash;95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.7\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u0026ndash;61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0\u0026ndash;78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.8\u0026ndash;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe largest part of Cu is bound to humic acids and other organic matter in the soil. Copper follows the following trend of distribution per fraction: III\u0026thinsp;\u0026gt;\u0026thinsp;IV\u0026thinsp;\u0026gt;\u0026thinsp;II\u0026thinsp;\u0026gt;\u0026thinsp;I. As in this and many previous studies, different kinds of soils have shown that non-residual fractions of Cu are mainly associated with the oxidizable phase, occurring as organically complexed metal species. This is because Cu shows a high affinity with humic substances, which is a fraction of the natural organic matter chemically active in complexing such metals (Kastratović et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCu extraction by carboxylic acids and EDTA\u003c/p\u003e \u003cp\u003eIn our experiments, we extracted 98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;42.7 mg/kg of copper using EDTA from sample which, when calculated as a wt.%, amounts to 79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12 relative to the pseudo-total Cu content (127 mg/kg). (The amount of Cu extracted by EDTA in mg/kg and wt.% from the individual tested sites are given in the Supplementary file, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This is several times more than Cu extracted by amino acids and carboxylic acid salts. The average value of Cu extracted with 10 amino acids is 34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7 mg/kg, or 30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46 wt.% (next chapter) relative to pseudo-total Cu content. The mean value of Cu extracted by four Na-salts of carboxylic acids (acetate, oxalate, tartrate, citrate) is 9.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49 mg/kg. Amino acids and carboxylate anions extracted 35.2% and 10.1% compared to EDTA, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWuana et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported extraction yields of 70.30% for Cu using EDTA. EDTA leaching removes 96\u0026ndash;100% of Cu from labile soil fractions and leads to the dissolution of minerals from the residual fraction. Their work demonstrated that the extraction efficiency decreases in the following order: EDTA\u0026thinsp;\u0026gt;\u0026thinsp;citric acid\u0026thinsp;\u0026gt;\u0026thinsp;tartaric acid. They further stated that among the metals they investigated (Cd, Cu, Ni, Pb, Zn), Cu exhibited the strongest chelating ability.\u003c/p\u003e \u003cp\u003eNatural low-molecular-weight organic acids, including mono-, di-, and poly-carboxylic acids, result from the decomposition of plant and animal residues in the soil and are natural products of root exudates. Low-molecular-weight carboxylic acids are chelating reagents capable of extracting heavy metals from the soil by forming complexes. While carboxylic acids are less effective at removing heavy metals from the soil than aminopolycarboxylic acids (EDTA and its derivatives), they offer several significant advantages. At the same time, they are biodegradable and environmentally friendly. Numerous research studies are based on the use of various natural low-molecular-weight organic acids for metal extraction from the soil (ITRC, 2003; Wuana et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ability to remove metals using carboxylic acids depends on the dissociation constant (pKa), the ligand's shape, and the number of carboxyl groups (-COOH). They are effective only in removing exchangeable, carbonate, and reductive fractions of heavy metals (ITRC, 2003; Peters, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, we investigated the capacity of Cu removal from soil using Na-salts of acetic (monocarboxylic), oxalic (dicarboxylic), tartaric (hydroxy-dicarboxylic), and citric as tricarboxylic acid. The descending order of Cu removal was as follows: citrate (20.3 mg/kg)\u0026thinsp;\u0026gt;\u0026thinsp;oxalate (10.2)\u0026thinsp;\u0026gt;\u0026thinsp;tartrate (5.40)\u0026thinsp;\u0026gt;\u0026thinsp;acetate (3.82). (The amount of Cu extracted by carboxylic acid salts in mg/kg and wt.% from the individual tested sites are given in the Supplementary file, Table S3 and S4). When calculated relative to the pseudo-total, the range of extracted Cu varied from 2.99 wt.% for acetate to 15.9 wt.% for citrate. These results are comparable to the amino acid arginine (4.28 wt.%). Compared to other examined amino acids (25.1 wt.% \u0026minus;\u0026thinsp;40.2 wt.%), carboxylates leached significantly less Cu from the soil.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a comparison of the mean values of Cu extracted by the chelators used in relation to the content in the sediment by fraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the study by Wuana et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), it was reported that citric acid mainly removes Cu from the acid-exchangeable and reducible soil fractions. They observed that after leaching the soil with citric acid, only a small portion of Cu (8%) was removed from the residual fraction. Ke et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) demonstrated that tartaric acid is effective in removing Cu from the easily-exchangeable carbonate fractions of contaminated soil. Around 13% of Cu remained unextracted from the exchangeable fraction even after leaching the soil with tartaric acid (Wuana et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCu extraction by amino acids\u003c/p\u003e \u003cp\u003eThe results of the extracted amounts of Cu, minimal and maximal concentrations and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with the tested amino acids from nine locations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of the analysis from individual locations are given in the Supplementary file (Table S5 and S6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCu amounts extracted by amino acids in mg/kg and wt.% relative to pseudo-total content, minimal and maximal concentrations and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; n\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emg/kg dry weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ewt.% relative to pseudo-total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin.-max.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin.-max.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.7\u0026ndash;60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.1\u0026ndash;33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.4\u0026ndash;50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 \u003cb\u003e(c)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0\u0026ndash;31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76 \u003cb\u003e(c)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.7\u0026ndash;58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.2\u0026ndash;32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026ndash;64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026ndash;35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.9\u0026ndash;55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.1\u0026ndash;31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGly-gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.9\u0026ndash;58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 \u003cb\u003e(c)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u0026ndash;29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59 \u003cb\u003e(c)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.5\u0026ndash;64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7 \u003cb\u003e(b)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.8\u0026ndash;38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57 \u003cb\u003e(ab)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.8\u0026ndash;62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.4\u0026ndash;36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02 \u003cb\u003e(bc)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u0026ndash;88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4 \u003cb\u003e(a)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.9\u0026ndash;44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45 \u003cb\u003e(a)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.39\u0026ndash;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72 \u003cb\u003e(d)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.43\u0026ndash;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 \u003cb\u003e(d)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Copper content values with the same letter(s) are not significantly different at p\u0026thinsp;=\u0026thinsp;0.05 in the column\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBy applying the analysis of variance (ANOVA) to the results of extracted Cu content expressed in mg/kg using different amino acids, F\u0026thinsp;=\u0026thinsp;4.927 was calculated. The tabulated F-value for a significance level of 0.05 and the number of degrees of freedom φ1\u0026thinsp;=\u0026thinsp;9 and φ2\u0026thinsp;=\u0026thinsp;80 is 2.01. To determine which mean values significantly differ from each other, we applied the Tukey-Snedecor test. The tabulated value for Q\u003csub\u003e(10,80)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.62, so the maximum allowed difference between the mean values D\u0026thinsp;=\u0026thinsp;24.254. The same calculation was applied to Cu contents expressed in wt.% relative to the total content in the soil. There is a significant difference between individual mean values of Cu (wt.%) extracted with different amino acids (F\u003csub\u003ecal\u003c/sub\u003e=70.625, F\u003csub\u003ecritical\u003c/sub\u003e=2.01). The calculated maximum allowed difference between the values was D\u0026thinsp;=\u0026thinsp;5.102. The results of the statistical analysis of mean values by individual locations are shown in the Supplementary file (Table S5 and S6).\u003c/p\u003e \u003cp\u003eThe least significant difference for p\u0026thinsp;=\u0026thinsp;0.05, based on Fisher's test between the mean values of Cu (mg/kg) washed with amino acids for all locations, is LSD\u0026thinsp;=\u0026thinsp;8.694. For results expressed in wt.%, LSD\u0026thinsp;=\u0026thinsp;7.219.\u003c/p\u003e \u003cp\u003eOf the tested amino acids, histidine extracted the highest Cu amount. Through its free electron pair, the imidazole nitrogen participates in the creation of a coordinative covalent bond with Cu. The lowest amount was extracted with arginine. After His, the largest amounts of Cu were extracted with the polar amino acids serine (Ser) and threonine (Thr) and the hydrophobic leucine (Leu). It is likely that the hydroxyl group of the side chain of Ser and Thr can contribute to the copper chelation process. This analysis shows that there is no statistically significant difference between the mean values for Ser, Thr, Leu, Gly, Val and Phe. Alanine and the dipeptide gly-gly had similar mean values of extracted Cu. These results also show a advantage of Ala and Gly-gly over Arg (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His\u0026thinsp;\u0026gt;\u0026thinsp;Ser\u0026thinsp;\u0026gt;\u0026thinsp;Thr\u0026thinsp;\u0026gt;\u0026thinsp;Leu\u0026thinsp;\u0026gt;\u0026thinsp;Gly\u0026thinsp;\u0026gt;\u0026thinsp;Val\u0026thinsp;\u0026gt;\u0026thinsp;Phe\u0026thinsp;\u0026gt;\u0026thinsp;Gly-Gly\u0026thinsp;~\u0026thinsp;Ala\u0026thinsp;\u0026gt;\u0026thinsp;Arg.\u003c/p\u003e \u003cp\u003eInterestingly, His has the highest stability constant with Cu (18.1), while arginine has the lowest (13.9). Other amino acids have similar stability constants with Cu. Such data suggest that the stability constants of the complex depend very little on the steric factor and hydrophilicity of amino acids.\u003c/p\u003e \u003cp\u003eSimilar to our results, Doleva et al. (2020) found that the extraction of Cu for most of the tested amino acids was around 30\u0026ndash;35%. They further report that the level of Cu removal by threonine, aspartic acid and histidine is comparable to that by EDTA, while amino acids containing a non-polar side chain consistently showed lower extraction efficiency. Cysteine containing a thiol group was found to be ineffective for extraction as it forms a precipitate with Cu\u003csup\u003e2+\u003c/sup\u003e. It is important to note that most amino acids extracted less than 5% of Ca (except for acidic Asp), compared to ~\u0026thinsp;40% Ca extracted by EDTA, suggesting that they are potentially very suitable reagents for soil. Karczewska and Milko (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) tested histidine (His) for the extraction of Cu, Pb, and Zn and found it to be competitive with EDDS (an EDTA derivative). The results of the studies mentioned above show that hydrophobic, nucleophilic, and steric properties are not decisive factors that determine the efficiency of metal extraction using amino acids. The functional groups of the side chain play a much more important role in metal extraction. Yamauchi et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) demonstrated that His, which is a potentially tridentate ligand, is capable of strongly coordinating metal cations via imidazole nitrogen.\u003c/p\u003e \u003cp\u003eBy applying the analysis of variance (ANOVA) to the mean values of extracted Cu expressed in mg/kg in relation to 9 different locations, F\u0026thinsp;=\u0026thinsp;13.536\u0026thinsp;\u0026gt;\u0026thinsp;F\u003csub\u003ecritical,9,81\u003c/sub\u003e=2.21, was calculated. The maximum allowed difference between mean values is D\u0026thinsp;=\u0026thinsp;19,326. The results of the statistical analysis of the differences between the mean values of individual locations are shown in the Supplementary file (Table S5 and S6). Mean values from locations S1, S5 and S9 are significantly higher than the others. The lowest amounts of Cu were extracted from locations S2, S3, S4. The difference in mean values between locations S5, S7 and S9 as well as S6, S7, S8 and S9 is insignificant.\u003c/p\u003e \u003cp\u003eThe least significant difference for p\u0026thinsp;=\u0026thinsp;0.05, based on Fisher's test between the mean values of Cu (mg/kg) washed with amino acids for all locations, is LSD\u0026thinsp;=\u0026thinsp;16.6.\u003c/p\u003e \u003cp\u003eThe descending order of locations by extracted Cu (wt.%) is: S1\u0026thinsp;\u0026gt;\u0026thinsp;S5\u0026thinsp;\u0026gt;\u0026thinsp;S9\u0026thinsp;\u0026gt;\u0026thinsp;S7\u0026thinsp;\u0026gt;\u0026thinsp;S6\u0026thinsp;\u0026gt;\u0026thinsp;S8\u0026thinsp;\u0026gt;\u0026thinsp;S3\u0026thinsp;\u0026gt;\u0026thinsp;S4\u0026thinsp;\u0026gt;\u0026thinsp;S2. This order of locations is the same as the order of the increasing amount of pseudo-total Cu content. The content in the residual (immobilized) fraction is also important for the amount of Cu extracted from certain locations. The selected locations significantly differ in terms of organic matter and carbonate content (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a statistically insignificant difference between the results of the mean values of washed Cu (expressed in wt.%) from individual locations, with the tested amino acids (F\u003csub\u003ecal\u003c/sub\u003e=0.920, F\u003csub\u003ecritical\u003c/sub\u003e=2.21). The calculated maximum allowed difference between the values was D\u0026thinsp;=\u0026thinsp;14.317. The least significant difference for p\u0026thinsp;=\u0026thinsp;0.05, based on Fisher's test between the mean values of Cu (wt.%) is LSD\u0026thinsp;=\u0026thinsp;16.6. The permissible difference between the results (expressed in wt.%) from different locations indicates that the physicochemical characteristics of the soil do not have a significant effect on Cu extraction. The structure of amino acids has a much greater impact.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe largest part of Cu (50.6% \u0026plusmn; 7.10 wt.%) in the investigated vineyard soils is bound to humic acids and other organic substances in the soil. The copper content in the exchangeable and easily accessible fraction, reducible, and residual fractions is in the following order: 2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 wt.%, 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3,67 wt.%, and 27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43 wt.%.\u003c/p\u003e \u003cp\u003eThe average content of Cu extracted with amino acids is 34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7 mg/kg or 30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46 wt.% relative to the pseudo-total content. This is more than what was extracted with carboxylic acid salts (8.45 wt.%) but less than with EDTA \u0026ndash; 79.9 wt.%. However, amino acids have several very favorable properties, recommending them for the extraction of Cu and other heavy metals from soil.\u003c/p\u003e \u003cp\u003eThe descending order of Cu removal with carboxylic acid salts was: citrate (20.3 mg/kg)\u0026thinsp;\u0026gt;\u0026thinsp;oxalate (10.2)\u0026thinsp;\u0026gt;\u0026thinsp;tartrate (5.40)\u0026thinsp;\u0026gt;\u0026thinsp;acetate (3.82). When calculated relative to the pseudo-total, the range of extracted Cu varied from 2.99 wt.% for acetate to 15.9 wt.% for citrate. The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His\u0026thinsp;\u0026gt;\u0026thinsp;Ser\u0026thinsp;\u0026gt;\u0026thinsp;Thr\u0026thinsp;\u0026gt;\u0026thinsp;Leu\u0026thinsp;\u0026gt;\u0026thinsp;Gly\u0026thinsp;\u0026gt;\u0026thinsp;Val\u0026thinsp;\u0026gt;\u0026thinsp;Phe\u0026thinsp;\u0026gt;\u0026thinsp;Gly-Gly\u0026thinsp;~\u0026thinsp;Ala\u0026thinsp;\u0026gt;\u0026thinsp;Arg.\u003c/p\u003e \u003cp\u003eThe results of this paper show that the amino acid structure is the most important factor for efficient Cu extraction, while the physicochemical properties of the vineyard soil have less influence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests: \u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read, have understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and the supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBojana Knežević carried out the sampling and analysis of the samples. Vlatko Kastratović wrote the main manuscript, pictures/illustrations and preparation of the tables. All authors contributed to the structure of the manuscript and were involved in the review and editing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrunetto, G., Bastos de Melo, G.W., Terzano, R., Del Buono, D., Astolfi, S., Tomasi, N., Pii, Y., Mimmo, T., \u0026amp; Cesco, S. (2016). Copper accumulation in vineyard soils: Rhizosphere processes and agronomic practices to limit its toxicity. Chemosphere, 162, 293\u0026ndash;307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2016.07.104\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2016.07.104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao, J.C., Hong, A., Okey, R.W., \u0026amp; Peters, R.W. 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Frontiers in Earth Science, 10:901570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2022.901570\u003c/span\u003e\u003cspan address=\"10.3389/feart.2022.901570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"carboxylic acid, chelating agents, EDTA, extraction, fungicides, remediation","lastPublishedDoi":"10.21203/rs.3.rs-4263629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4263629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCopper compounds in the form of fungicides are most commonly used as a measure of protection against vine diseases. Typically, around a dozen treatments are done throughout a year with a dispersion of approximately 5\u0026ndash;10 kg of Cu per hectare. For many years, the remediation of heavy metals has often involved the use of ex-situ soil washing with chelating reagents. Amino acids have a lower metal chelation capability compared to EDTA and its derivatives, but they have numerous other advantages in comparison. The main goal of this research was to investigate the ability of 9 amino acids and one dipeptide to extract Cu from various vineyard soil samples and compare their chelating ability with other 'green' chelating agents. The average content of Cu extracted with amino acids is 34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7 mg/kg or 30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43 wt.% relative to the pseudo-total content. This is more than what was extracted with carboxylic acid salts (9.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49 mg/kg or 8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56 wt.%) but less than with EDTA (98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;42.7 mg/kg or 79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12 wt.%). The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His\u0026thinsp;\u0026gt;\u0026thinsp;Ser\u0026thinsp;\u0026gt;\u0026thinsp;Thr\u0026thinsp;\u0026gt;\u0026thinsp;Leu\u0026thinsp;\u0026gt;\u0026thinsp;Gly\u0026thinsp;\u0026gt;\u0026thinsp;Val\u0026thinsp;\u0026gt;\u0026thinsp;Phe\u0026thinsp;\u0026gt;\u0026thinsp;Gly-Gly\u0026thinsp;~\u0026thinsp;Ala\u0026thinsp;\u0026gt;\u0026thinsp;Arg. The results of this paper show that the amino acid structure is the most important factor for efficient Cu extraction, while the physicochemical properties of the vineyard soil have less impact.\u003c/p\u003e","manuscriptTitle":"Removal of Copper From the Vineyard Land of Pješivci (Montenegro) Using Amino Acids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 11:49:20","doi":"10.21203/rs.3.rs-4263629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-13T12:31:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T07:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271710089104299368058867001311420059225","date":"2024-06-02T07:26:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204618107937117075034423607892426134656","date":"2024-06-01T22:21:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-01T21:32:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-15T14:47:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-15T14:47:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2024-04-14T04:44:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f8e53fb7-db37-4150-98c5-fac98c75b8c4","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:06:09+00:00","versionOfRecord":{"articleIdentity":"rs-4263629","link":"https://doi.org/10.1007/s12665-024-11875-w","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2024-09-24 15:57:58","publishedOnDateReadable":"September 24th, 2024"},"versionCreatedAt":"2024-04-24 11:49:20","video":"","vorDoi":"10.1007/s12665-024-11875-w","vorDoiUrl":"https://doi.org/10.1007/s12665-024-11875-w","workflowStages":[]},"version":"v1","identity":"rs-4263629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4263629","identity":"rs-4263629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}