Transference of potentially toxic elements from soils to plants in a derelict Pb-Zn mining area (San Quintín mine, South-Central Spain)

Transference of potentially toxic elements from soils to plants in a derelict Pb-Zn mining area (San Quintín mine, South-Central Spain) | 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 Transference of potentially toxic elements from soils to plants in a derelict Pb-Zn mining area (San Quintín mine, South-Central Spain) José Ignacio Barquero Peralbo, Jesús Peco, Jaime Villena, Juan Antonio Campos, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6991303/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aims: Abandoned mining areas represent critical environmental pollution hotspots due to the persistence of waste materials enriched in potentially toxic elements (PTEs). This study evaluates the transfer of PTEs from contaminated soils to six plant species in the vicinity of the San Quintín Pb-Zn mine (Ciudad Real, Spain), a site impacted by over a century of mining activity. Methods: The studied species include the tree Quercus ilex , the shrubs Retama sphaerocarpa and Scrophularia canina , and the annual herbaceous species Spergularia rubra , Rumex bucephalophorus , and Hirschfeldia incana . Soil and plant tissue samples were analysed using X-ray fluorescence and atomic absorption spectrometry to determine concentrations of Zn, Pb, Hg, Cu, and other PTEs. Results: Results revealed a high heterogeneity in the bioaccumulation of elements such as Zn, Pb, Hg, and Cu among the studied species, with Spergularia rubra and Rumex bucephalophorus emerging as effective bioindicators of soil contamination. Specific correlations between soil and plant concentrations were identified, and atmospheric uptake was found to significantly influence Hg accumulation in plant tissues. Conclusions: This work enhances our understanding of plant uptake mechanisms in contaminated environments and provides a foundation for ecological restoration and environmental monitoring strategies in decommissioned mining areas, emphasizing the role of both edaphic properties and species-specific physiological adaptations. Potentially toxic elements (PTEs) Soil–plant transfer Bioaccumulation Abandoned mining areas San Quintín mine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Plants absorb essential elements, including Cu, Ni, Fe, Zn, Mn, and Mo, which are required for their normal physiological functions. However, when their concentrations exceed certain thresholds, these essential elements can also induce phytotoxic effects. In addition to these, plants can absorb non-essential elements with no known biological function, some of which are highly toxic (Kirkby, 2012 ; Peco et al., 2021 ). Both essential elements that become toxic at elevated concentrations and inherently toxic non-essential elements are collectively referred to as potentially toxic elements (PTEs) (Kisku et al., 2000 ; Thalassinos et al., 2023 ). Different plant species employ distinct mechanisms for the uptake of these elements from the soil. Nevertheless, the efficacy of this uptake strongly depends on the bioavailability of these elements in the soil (Newman et al., 1994). The so-called bioavailability, constrained by the chemical state or speciation of the elements, is conditioned by different factors (Galán & Romero, 2008 ). Primary among these factors are climate conditions, which control water availability and its temperature-dependent activity in the soil. Additionally, geochemical behaviour, mineralogical form, and the presence of biological populations in the soil, capable of reacting with minerals containing the elements and potentially destroying their crystalline structure, are noteworthy contributors (Molina et al., 2006; Kabata Pendias, 2011). As a result, the absorption of PTEs in plants is a highly intricate process due to the multitude variables involved. Going even further, the uptake of PTEs from the soils can favour their bioaccumulation in edible parts, producing a risk for the incorporation of PTEs (especially in toxic concentrations) into vertebrates, including humans. This situation becomes a significant public health concern (Kabata Pendias, 2011; Adamo et al., 2014 ). Decommissioned mining areas are of particular interest for the study of the process of elemental incorporation into plants and assessing risks related to such process. Mining areas, especially those abandoned before the development of an ecological consciousness, act as sources of PTEs for nearby agronomic fields. This is due to the eventual persistence of waste accumulations that can be easily disseminated with the help of rainwater and wind to the neighbour areas (Sánchez-Donoso et al., 2019 ). The presence of pyrite (FeS 2 ) in the mined ores is of particularly concern, since this mineral is easily hydrolysed in atmospheric conditions, producing acidity which also favours the hydrolysis of the rest of metallic sulphides, in particular sphalerite (ZnS), galena (PbS) and chalcopyrite (CuFeS 2 ) (Harries and Ritchie, 1987 ). This process induces the release of corresponding PTE ions into the soils, taking on different solubility forms depending on the geochemical affinity of each ion for others. For instance, Pb 2+ readily combine with SO 4 2+ or CO 3 2+ , giving origin to insoluble anglesite (PbSO 4 ) or cerussite (PbCO 3 ). The San Quintín Pb-Zn-Ag area, located in the Southern Central Iberian Zona of the Hesperian Massif, exploited a mineralization hosted by shales and greywackes of pre-Ordovician age, and constituted by a typically hydrothermal mineral paragenesis comprising all the previously mentioned minerals: galena rich in silver (most probably due to the presence of Ag sulfosalts associated to it), sphalerite, chalcopyrite (scarce), and abundant pyrite, with quartz and minor calcite as ganga minerals (Palero, 1991 ). Also, after this author, the mine was active since old times, with a most important period of activity between years 1894 and 1923, and with a declared production of galena concentrates of some 500 000 tonnes. As a result of this activity, piles of residua with high residual concentration of Zn were left in the area, due to the low price of this element. But in the 1950’s, the increase in the price of this element, used in galvanization in the automotive industry, supposed the installation in the area of a ‘modern’ froth flotation plant, in which the former residua were in part reprocessed to obtain Zn, Pb and Ag. This plant produced two types of tailings: the materials with lower grain size, including silt and clay, were disposed in an embarquement dam, meanwhile the materials with sand size passed though the froth flotation process and the residua were accumulated in a tailings pile. In 1990 an experimental test to obtain cinnabar (HgS) concentrates from the ore coming from the Almadén mercury mine (located some 80 km to the West; Hernández et al., 1999 , among many others) was performed, and both residua on the original ore and of the cinnabar concentrates obtained were also abandoned in the area. After that, in the same year 1990 the froth flotation plant was abandoned, as well as the still unprocessed dumps and the dams. It is worth to note that an old gallery for dumps drainage produce seasonal outputs of Acid Mine Drainage (AMD) (Fig. 1 B), with very low pH (2 in average) and with high saline concentration (Electric conductivity > 8000 µS-cm − 1 as a norm). The area is at present under remediation, funded by European Next Generation funds. Previous studies in the area have characterized elemental contents in soils and their speciation (Rodríguez et al., 2009 ). Other investigations have compared the composition of the tailings with those from other exploited ore deposits in the region (Higueras et al., 2011 ); assessed the transference of PTEs from soil to acorn-tree ( Quercus ilex ) leaves, transference of PTEs also on a regional basis (Higueras et al., 2017 ); studied the geomorphology of the wastes piles to obtain conclusions on the dispersion processes of the fine material by water and wind (Sánchez-Donoso et al., 2019 ; 2021); assessed the toxicity of the waste materials using integrated geochemical and toxicological studies (García-Lorenzo et al., 2019 ; Ferri-Moreno et al., 2023 ); and studied the microbial diversity of the different types of soils present in the area (Gallego et al., 2021 ) and its influence of the mobility of PTEs in the soil (Peco et al., 2023 ). These studies conclude that the area is heavily contaminated, with soils containing potentially toxic elements (PTEs) within the ranges reported by Rodríguez et al. ( 2009 ) and Higueras et al. ( 2011 ). Notably, elevated concentrations of Pb, Zn, Hg, and Cd have been detected in Quercus ilex leaves (Higueras et al., 2017 ), and the presence of mine residues poses a significant risk to living organisms (García-Lorenzo et al., 2019 ; Ferri-Moreno et al., 2023 ). Additionally, different types of soils exhibit distinct microbial population, each influencing the mobility and bioavailability of PTEs (Gallego et al., 2021 ; Peco et al., 2023 ). In the present study, we have used the San Quintín area as a natural laboratory, providing a unique opportunity to investigate the long-term impact of waste (most for over 100 years) on the environment. The study focuses on plant-soil interactions, particularly with plants living in the area since time before the main mine activity occurred ( Q. Ilex ), as well growing directly on the waste material, including long-standing species like Retama sphaerocarpa and Scrophularia canina , as well as annual species like Spergularia rubra , Rumex bucephalophorus and Hirschfeldia incana . Besides, we have a high number of previous studies based in the soil properties, very useful to complete the portrait of the area. The comprehensive analysis, including plants assessments presented in this study, contributes to evaluating the intricate plant-to-soil transfer process in this complex environment. This investigation serves as a basis to understand the peculiarities of processes in extreme environments, specifically decommissioned mine areas affected by AMD, and in an evolved state of vegetal cover regeneration. The main objectives of this study have been: a) to perform a detailed geochemical characterization of the San Quintín mining area, focusing on the distribution, speciation, and mobility of potentially toxic elements (PTEs) in soils and mining residues; b) to assess the species-specific patterns of PTE uptake and bioaccumulation in native and colonizing plants, identifying key physiological and environmental factors influencing this process and evaluating their potential as bioindicators or candidates for phytoremediation in contaminated environments; and c) to investigate the role of atmospheric deposition in mercury accumulation in plant tissues, considering species morphology and proximity to emission sources. The insights gained are not only valuable for assessing risks withing this environment concerning the human food chain but also hold significance for similar areas affected by similar contamination processes. MATERIALS AND METHODS Sampling and processing Site description and sampling Soil sampling was conducted in the eastern sector of the San Quintín area, selected for its broader spatial extent, high plant species diversity, and the presence of distinct environmental domains warranting detailed characterization. A total of 23 sampling sites were established, distributed across three primary domains: the area containing the oldest mining residues (5 sites), zones affected by the froth flotation process (7 sites), and peripheral soils (11 sites) (Fig. 1 ). According to data reported by Rodríguez et al. ( 2009 ), the highest concentrations of PTEs were found in the oldest wastes deposits, followed by the flotation tailings. Peripheral soils exhibited substantial variability in PTE concentrations, likely influenced by their proximity to contaminated zones, as well as by secondary dispersion mechanisms such as wind erosion, surface runoff, and post-mining anthropogenic activities. At each selected site, chosen based on the presence of vegetation of particular ecological or conservation interest, a composite soil sample was collected by excavating up to a maximum depth of 20 cm. In certain locations, this depth could not be reached due to excessive soil compaction, likely resulting from historical anthropogenic disturbances such as former waste deposits. Three subsamples were extracted from randomly distributed points within a 10–15 m² plot to ensure spatial representativeness. Approximately 5 kg of soil per site was obtained, homogenized, and stored in high-density polyethylene (HDPE) bags, properly labelled with site code, date, and sample ID. All samples were transported under ambient conditions and stored at 4°C upon arrival at the laboratory to preserve their physicochemical integrity. Additionally, the geographic coordinates of each sampling site were recorded using a handheld GPS device with sub-meter accuracy. Vegetation samples were also collected at each sampling site, including multiple species whenever possible. The sampled taxa included one tree species ( Quercus ilex ), two shrub species ( Retama sphaerocarpa and Scrophularia canina ), and three annual herbaceous species ( Hirschfeldia incana, Rumex bucephalophorus , and Spergularia rubra ). For the tree species, mature leaves were sampled from multiple individuals per site when available, avoiding both senescent and newly emerged leaves (typically located at the basal and apical portions of branches, respectively). In the case of annual herbs, the entire aerial part of at least ten individuals per species was collected to ensure representativeness. All plant material was stored in paper envelopes to allow for adequate drying and prevent fungal growth, and samples were processed within 10 days of collection to preserve their biochemical integrity. Sample preparation The preparation of soil samples started with their drying (in opened plastic bag for 15 days). Once dry, they were sieved to discard the > 2 mm fraction, which was weighted to make a rough estimation of their gravel content. The < 2 mm fraction was homogenized and subdivided into three aliquots: one for physicochemical determinations without further treatment, a second aliquot was grinded in an agata mortar to < 100 µm size for chemical analysis, and a third was retained as a backup sample. Plant samples were washed with tap water followed by deionised water. Afterward, they were dried in a laboratory stove prior to a trituration using a KINEMATICA mixer (MB800 B). A portion of the homogenized samples was separated for Hg analysis, while a second aliquot (5 g) was blended with 0.15 g of agglutinant (dissolution of Elvacite 2046 PANalytical and ACETONA PURISS (CH 3 (CO)CH 3 ), UN 1090). The mixture was inserted into aluminium vessels (RETSCH PP25) and compressed using a hydraulic hand press (SPECAC 250 kN) to obtain a pressed pill, utilized in the analytical procedure for X-Ray Fluorescence (XRF) analysis. Analytical methods The physicochemical properties of the soils, including reactivity (pH), salts contents based on electric conductivity (EC), and organic matter content, were determined using conventional methods according to the standards UNE-ISO 10390:2012 for pH (AENOR, 2012 ) and UNE 77308:2001 for EC (AENOR, 2001 ). In particular, pH and EC measurements were determined after dispersion of the sample in water at a soil/water ratio of 10 gr/50 mL; in the resulting dispersion, the reactivity was estimated using a CRISON GLP 22 pHmeter, and the EC, with a CRISON GLP 32 conductivimeter. The organic matter content (SOM) was estimated using the method proposed by Walkey & Black ( 1934 ), based on quantitative oxidative valuation of 1 soil suspension using a mixture of sulphuric acid and K dichromate. The grain size distribution was evaluated using a Fritsch ANALYSETTE MicroTec Plus device, following the procedure outlined by Garcia-Ordiales et al. (2018), and applying the traditional sand-silt-clay classification (Udden-Wentworth, 1922 ). Both soil and vegetal samples were analysed for Hg and multielement. Hg analysis was performed by means of atomic absorption spectrometry with Zeeman effect, using a LUMEX RA-915M device (Sholopov et al., 2004). Multielemental analysis was performed via X-ray fluorescence with energy dispersion (EDXRF), using a Malvern Panalytical Epsilon 1 device. Certified Reference Materials of soils (NIST 2710A) and plants (GC7162) were analysed to ensure precision and accuracy. PTE recovery rates were in the range of 93–114% (XRF) and 94–103% for Hg (ZAAS-HFM). Soil to plant transfer indices The bioaccumulation coefficients (BAC) were formulated (Eq. ( 1 ) as a simple parameter to assess the bioavailability of elements in the soil, and the capacity of the plant to uptake them (Inacio et al. 2014; Gruszecka-Kosowska 2019). $$\:\left(BAC\right)=\:\frac{\left[C\text{p}\text{l}\text{a}\text{n}\text{t}\right]}{\left[C\text{s}\text{o}\text{i}\text{l}\right]}$$ 1 where [ C plant] and [ C soil] represent the concentration (in mg kg − 1 ) of a given element in the leaves and soil, respectively, corresponding to the same sampling site. Values of BAC > 1 indicate a high bioaccumulation capacity, especially when BACs are calculated with total concentration in soil. The BACs calculated with the soluble fraction in soil are higher, although they more precisely express the hyperaccumulator condition of the plant in a certain polluted substrate. Statistical Analyses and Mapping Data treatment involved multivariate analyses performed using Minitab 19.1 and STATGRAPHICS Centurion v19.1.2. Hierarchical clustering was applied to explore relationships among variables, using Ward’s linkage method and correlation coefficient distance as the similarity measure. Spatial distribution patterns were generated by inverse distance squared interpolation with Surfer 21.1.158 (Golden Software) and ArcMap 10.8.1 (ArcGIS)." RESULTS Soil analysis The soil texture shows a prevailing composition of sandy loam soils (average: 75.5% sand, 10.4% silt and 14.1% clay), with higher sand contents located leeward to the sand-sized tailings in agricultural areas. The SOM contents range from 0.1 to 5.8%, with an average of 2.95%, which are common values for soils in this semiarid region (Gallardo, 2016 ).The lower values (0.1-1%) appear in the soils more clearly affected by the mining activity, intermediate values (1–3%) in soils peripheral to the most affected area, and the highest levels found in agricultural and pasture areas. Soil reactivity is acidic (pH: 5.7 ± 0.7), with the lowest values corresponding to the soils from the proximity of tailings dumps. The contents in salts, as assessed by the EC, show a direct inverse relationship with pH, reaching 12,000 µS cm − 1 in sites close to mine residua accumulation with the lower pH values. In areas affected less intensely by mining activities, there is no conspicuous relationships among both parameters, with near neutral soil reactivity and EC: 515.9 ± 1,585.4 µS cm − 1 ; this high variability in EC is conditioned by the diversity of soil typology in the mine area. The results of the soil samples are summarized in Table 1 . Considering the major elements, it is worth noting that the SiO 2 concentrations are relatively constant in terms of the coefficient of variation, although the range of values involved is wide (35.5–71.7%), as observed for Al 2 O3 and TiO 2 . This suggests the homogeneity of the geological substrate, primarily constituted by shales and greywackes, but affected by the presence of wine wastes. On the other hand, the maximum variability among major elements is found in CaO, which should be an indication of the occasional presence of carbonates rich in Ca in the mineralized veins. Amongst trace elements, the highest variability corresponds to: Zn > > Pb = Cu > Hg > Sb > S > Ba. This variability is linked to the variability in the abundance of the elements in the deposit and their mobility, which explains a similar variability patterns for Pb and Zn, with Zn being considerably less abundant but exhibiting higher mobility (Rodríguez et al., 2009 ). Table 1 Basic statistics for the elemental analysis of the soil samples. Abbreviations: SD: standard deviation; VC: Variation coefficient (in %). Variable Unit Average SD VC Minimum Maximum SiO 2 % 58.8 8.3 14.0 35.5 71.8 Al 2 O 3 % 12.4 3.3 26.4 5.0 19.9 CaO % 0.8 0.9 105.3 0.1 3.9 Fe 2 O 3 % 4.6 3.0 64.3 1.7 14.3 K 2 O % 2.2 0.5 24.5 1.3 3.2 P 2 O 5 % 0.2 0.1 43.5 0.1 0.5 TiO 2 % 0.7 0.1 18.0 0.5 0.9 S mg kg − 1 4,295.0 7,328.0 170.6 304.0 25,819.0 Cr mg kg − 1 76.5 20.1 26.3 38.4 109.6 Mn mg kg − 1 868.0 733.0 84.4 124.0 3,080.0 Cu mg kg − 1 185.1 357.0 192.9 18.1 1,680.0 Zn mg kg − 1 3,388.0 10,004.0 295.3 68.0 48,460.0 Sb mg kg − 1 111.9 190.4 170.2 13.1 805.6 Hg mg kg − 1 232.8 427.1 183.5 3.0 1,920.0 Pb mg kg − 1 8,000.0 15,913.0 198.9 70.0 57,270.0 Rb mg kg − 1 72.0 22.4 31.1 42.2 121.4 Sr mg kg − 1 58.2 30.7 52.8 25.9 139.9 Zr mg kg − 1 466.3 204.2 43.8 147.8 894.0 Sn mg kg − 1 69.9 17.5 25.1 53.3 123.6 Ba mg kg − 1 602.0 628.0 104.3 147.0 2,580.0 The cluster analysis of soil data results in the dendrogram shown in Fig. 2 for the different elements. Three major clusters (Cl) have been distinguished: Cl1 groups Al, Rb, K and Cr; this appears as a geogenic cluster related to compositional differences in soil composition. Cl2 includes S, Fe, Sb, Sn, Pb Ba, Cu, Zn, and, with minor similarity, Ca and Sr, which includes most of the PTEs characteristic of the mineralogy of the exploited ore deposit. Finally, Cl3 includes Si and Hg with P, Zr, Ti and Mn, what should be related to the presence of cinnabar mineralized rocks, coming from El Entredicho mine in Almadén, to test the froth flotation of this ore; Si is the main component of these samples, since the cinnabar is hosted by quartzites; and P, Zr, Ti and Mn could be also related to the presence, together with the mineralized quartzites, of big boulders of the magmatic rock which, together with the quartzites, host the cinnabar deposit of El Entredicho mine (Hernández et al., 1999 ). Besides, the dendrogram from the cluster analysis of samples (Fig. 2 ) clearly differentiates between three types of samples, which, furthermore, corresponds clearly with three domains with different degrees of contamination (Fig. 3 ): a highly contaminated domain, coincidental with the central area (brown colour), where the mine wastes, with high marginal contents in ore minerals, persist although substantial dismantling efforts; a second domain, corresponding to tailings from the froth flotation wastes, with intermediate level of contamination (red colour); a third domain corresponding to samples from marginal areas, characterized by limited contamination (green colour). The PCA analysis, illustrated in Fig. 4 , effectively discriminates soil samples according to their geochemical composition and substrate. The first two principal components explain a cumulative 62% of the total variance (47.1% by the first component and 14.9% by the second), providing a robust basis for interpreting the main geochemical gradients. Samples with negative scores on the first component are primarily associated with peripheral soils, characterized by low concentrations of lithogenic elements such as Si, Zr, Ti, and Mn, and relatively higher levels of Zn, Cu, Pb, Sb, Ba, Ca, S, Sn, Fe, and Sr (elements typically linked to anthropogenic inputs). The second component separates samples with elevated concentrations of P, Mn, and Zr (positive scores) from those with higher Hg content (negative scores). Former waste samples are mainly distributed along the positive side of the first component, reflecting their enrichment in ore-related elements, while their position along the second component is more variable, though predominantly negative. In contrast, tailing samples cluster near the origin, with low scores on both components, indicates a more homogeneous and less extreme geochemical profile. Plant analysis Table 2 synthetizes the analytical results for the studied plant species. We consider interesting to notice that the concentrations of primary and secondary macronutrients (K, S, and Ca) vary significantly among the species. For instance, S. rubra shows very high concentrations of K and S compared to Q. ilex and R. sphaerocarpa , which present levels roughly an order of magnitude lower. Meanwhile, the concentration of Ca in H. incana is about one order of magnitude lower than that found in the rest of the analysed species. These differences can be explained by species-specific physiological traits and ecological adaptations common in Mediterranean ecosystems. Species like S. rubra tend to accumulate higher amounts of potassium and sulfur, which are critical for processes such as osmotic regulation and protein synthesis, particularly in plants adapted to nutrient-rich or rapidly growing environments (Kruger, 1983 ; Sardans & Peñuelas, 2013 ). Conversely, the markedly lower Ca concentration in H. incana may reflect adaptations to soils with low Ca availability or acidic conditions, a pattern reported in species thriving in nutrient-poor substrates (Groves, 1983 ; Read & Mitchell, 1983 ). Overall, these nutrient concentration patterns align with the observed functional traits and phylogenetic differences that influence nutrient cycling and uptake strategies in Mediterranean plant communities (Prieto-Rubio et al., 2022 ), supporting that such variability is typical and expected in similar ecosystems. Considering the trace elements, both those considered as micronutrients, common in plants, and toxic for plants, and considering than most of them display a high to extreme variability in the soils of the area, the variability has two conditions: the one considering the different species, and the one considering the different specimens of the same species. The species with higher concentrations in the different elements are: Cu: S. rubra ~ R. bucephalophorus > all the rest; Fe: S. rubra > > H. incana ~ R. bucephalophorus > Q. ilex > S. canina > > R. sphaerocarpa ; Mn: Q. ilex > S. rubra > R. bucephalophorus > H. incana > R. sphaerocarpa > S. canina ; Zn: S. rubra > R. bucephalophorus > > R. sphaerocarpa ~ H. incana ~ S. canina > Q. ilex ; Hg: S. rubra > all the rest; and Pb: S. rubra > > R. bucephalophorus > > Q. ilex > the rest. Therefore, the plants with higher bioaccumulation capacity, considering the elements related with the mining activity, should be S. rubra and R. bucephalophorus , with Q. ilex also showing a noticeable capacity, in particular for Mn and Pb. Additionally, rubidium (Rb) and strontium (Sr) were analysed due to their chemical similarity to K and Ca, respectively. However, their concentrations were generally low and showed no consistent pattern among species, suggesting limited physiological relevance in this context. On the other hand, intraspecies variability, as expressed by the variation coefficient, is maxima for Hg in R. sphaerocarpa (for very low total concentrations), in Q. Ilex , and in S. rubra (229.9, 183.2 and 163.8%, respectively); Rb in S. canina (141.1%); and Pb in R. bucephalophorus, Q. ilex and R. Sphaerocarpa (148.4, 123.7 and 120.0% respectively). These variation coefficients are the best indications for a real variability in the bioaccumulation into each species, since these represent the variability related with variations in the contents and therefore, of bioavailability in the soil. Table 2 Mean, standard deviation, and coefficient of variation for primary and secondary macronutrients, micronutrients, common plant elements, and toxic elements in leaf tissues. All values are expressed in mg kg -1 , except for Hg, which is expressed in µg g -1 . Primary macronutrient Secondary macronutrients Micronutrients Common elements in plants Toxic elements for plants K S Ca Cu Fe Mn Zn Sr Rb Hg Pb Quercus ilex (n = 23) Mean 5,286.7 1,554.1 5,887.7 16.0 682.0 341.5 124.5 8.1 2.5 10.4 132.9 SD 1,474.2 659.8 2,570.7 5.2 480.9 264.7 83.4 4.2 1.5 19.0 164.3 VC (%) 27.9 42.5 43.7 32.4 70.5 77.5 67.0 51.8 58.8 183.2 123.7 Retama sphaerocarpa (n = 26) Mean 7,659.2 1,922.3 8,437.9 19.1 225.7 86.6 274.9 17.2 4.0 4.2 36.3 SD 3,485.5 625.9 3,173.4 6.5 83.5 38.2 202.6 12.1 2.8 9.3 43.8 VC (%) 45.5 32.6 37.6 34.1 37.0 44.1 73.7 70.5 70.4 224.9 120.9 Scrophularia canina (n = 6) Mean 16,718.0 2,695.8 8,664.8 15.3 466.7 40.4 221.0 11.8 9.0 5.8 67.8 SD 5,362.9 364.0 1,644.1 3.3 357.6 20.2 233.8 6.2 12.8 6.2 50.5 VC (%) 32.1 13.5 19.0 21.7 76.6 49.9 105.8 52.8 142.1 107.2 74.4 Hirschfeldia incana (n = 7) Mean 21,601.7 10,631.4 29,098.6 11.1 966.6 104.8 222.7 91.1 11.1 10.1 54.7 SD 4,141.1 1,797.8 12,216.9 2.0 593.9 76.9 130.9 48.5 9.7 10.5 47.8 VC (%) 19.2 16.9 42.0 18.3 61.4 73.4 58.8 53.2 86.6 103.5 87.4 Spergularia rubra (n = 8) Mean 40,414.3 5,111.3 6,217.1 36.1 2,711.5 290.5 1,232.3 7.3 49.9 27.6 1,459.6 SD 9,017.0 3,650.5 2,524.9 21.3 2,359.7 128.6 950.3 3.9 20.9 45.2 1,632.4 VC (%) 22.3 71.4 40.6 58.9 87.0 44.3 77.1 53.5 42.0 163.8 111.8 Rumex bucephalophorus (n = 7) Mean 24,512.9 2,259.3 4,848.0 22.0 960.7 154.0 927.0 8.7 8.7 8.9 552.0 SD 4,937.4 609.8 1,969.1 6.6 545.8 119.5 455.5 3.5 6.6 9.1 819.3 VC (%) 20.1 27.0 40.6 29.9 56.8 77.6 49.1 40.1 75.5 102.9 148.4 Barquero et al. ( 2024 ) ( Q. ilex ) Mean 4,000.0 1,032.3 7,000 7.2 322.9 833.6 26.4 12.7 2.5 0.114 0.8 Monaci et al. (2022) ( Q. ilex ) Mean 6,000.0 1,300.0 * 6.1 240.0 * 34.6 * * 0.06 3.1 Higueras et al. (2016) ( Q. ilex ) Mean * * * 4.6 * * 20.6 * * 19.71 1.5 Marschner ( 2012 ) Mean 10,000.0 1,000.0 5,000.0 6.0 100.0 50.0 20.0 * * * * Kabata-Pendias (2001) (Mature leaf tissue generalized for various species) Mean * * * 5–30 * 30–300 27–150 10–662 20–70 < 1 5–10 Toxicity level * * * 20–100 * 400-1,000 100–400 * * 1–3 30–300 The PCA performed on the elemental concentrations in plant tissues (Fig. 5 ) revealed clear species-specific clustering patterns, reflecting distinct uptake strategies and affinities for PTEs. The first two components accounted for 59.7% of the total variance (PC1: 35.6%, PC2: 24.1%). Spergularia rubra exhibited the highest scores along PC1, which was primarily associated with PTEs such as Cu, Zn, Pb, and Hg, as well as Rb, Fe, and K, indicating a strong bioaccumulation capacity. Hirschfeldia incana was clearly separated along PC2, showing highly negative values, and positive PC1 scores, suggesting a distinct elemental profile dominated by Mn, Sr, and Ca. Rumex bucephalophorus occupied the upper left quadrant, with positive values on both PC1 and PC2, indicating a mixed accumulation pattern. In contrast, Quercus ilex , Retama sphaerocarp a, and Scrophularia canina clustered in the lower left quadrant, with predominantly negative PC1 values and low PC2 scores, reflecting lower accumulation of PTEs and a more conservative elemental uptake strategy. These results underscore the functional diversity among species in response to soil contamination and support the potential use of S. rubra and R. bucephalophorus as fine bioindicators in metal-impacted environments. DISCUSSION Relationships between concentrations in soil and plant Elemental concentrations in plants do not necessarily depend on total concentrations in soils. We evaluated this relationship and found that the studied species exhibited contrasting responses in terms of bioaccumulation of secondary macronutrients, common plant elements, and toxic elements, relative to their concentrations in the soil. Among the studied species, contrasting patterns were observed regarding Ca bioaccumulation in relation to its soil concentration (Fig. 6 ). R. bucephalophorus showed a positive linear correlation (Ca in plant tissue = 0.166 × Ca in soil – 3,571.6; R² = 0.62), indicating that approximately 62% of the variability in Ca content in plant tissues can be explained by its concentration in the soil. This relationship suggests an efficient Ca uptake strategy in this species, possibly linked to its annual life cycle and opportunistic nutrient acquisition behaviour (White & Broadley, 2003 ; Marschner, 2012 ). In contrast, S. canina exhibited a moderate negative correlation (Fig. 6 , left; R² = 0.60), where higher soil Ca concentrations were associated with lower tissue concentrations. This may reflect physiological regulation mechanisms to prevent toxicity or ionic imbalances, particularly in acidic soils with high electrical conductivity. Such responses have been reported in plants adapted to nutrient-poor or acidic soils, where tight control of Ca transport becomes essential (Pathak et al., 2021 ). The limited mobility of Ca in the phloem and its dependence on transpiration flow also influence its redistribution, especially in young tissues or under stress conditions (White & Broadley, 2003 ). These differences highlight interspecific adaptive strategies to variable edaphic conditions, with R. bucephalophorus acting as an efficient accumulator species, while S. canina appears to exert stricter control over Ca uptake, likely as a protective response to adverse soil conditions, thereby avoiding associated toxicities and maintaining cellular ionic homeostasis (Wang & Schreiber, 2024 ). Figure 7 shows the relationship between Sr concentrations in soil and plant tissues for S. canina. The data reveal a moderately negative linear trend, described by the regression equation: y = − 0.485x + 35.9 (R² = 0.48), indicating that approximately 48% of the variation in plant Sr content can be explained by its concentration in the soil. This inverse relationship suggests the presence of a regulatory mechanism in Sr uptake; whereby increased soil availability does not result in a higher accumulation in plant tissues. This behaviour may be attributed to the chemical similarity between Sr and Ca, which can lead to competitive inhibition at the root uptake level, particularly in species adapted to low Ca environments (Gupta & Walther, 2018 ). Such mechanisms have been documented in various taxa, especially under radiostrontium contamination, where plants exhibit selective exclusion or compartmentalization strategies to mitigate potential toxicity (Chatterjee et al., 2019 ). Similar responses have also been observed under environmental stress or metal contamination, where selective exclusion mechanisms are activated to maintain ionic homeostasis (Ghori et al., 2019 ). Figure 8 shows the relationships between total mercury (THg) concentrations in soils and plant tissues of the different studied species. Based on the observations, it is important to highlight that the San Quintín mining complex is characterized by significant local sources of Hg (e.g., ruins of the former ore-washing facility). In this area, atmospheric Hg (Hg 0 ) has been reported as a relevant component of environmental pollution (Esbrí et al., 2010 ), supporting the hypothesis that Hg content in plants may reflect atmospheric deposition (Stamenkovic & Gustin, 2009 ; Barquero et al., 2019 ; Naharro et al., 2020). In the studied area, the soils emit variable concentrations of Hg 0 , depending on their proximity to the Hg wastes shown in Fig. 1 . Therefore, it is proposed that Hg transfer to plants largely responds to atmospheric contamination originated from the soil compartment (Esbrí et al., 2016 ; Barquero et al., 2019 ; Naharro et al., 2019 ). In R. bucephalophorus , a small-sized plant, a strong exponential relationship is observed (THg plant tissue = 7.888 ln (THg soil) – 32.30; R² = 0.98). This suggests that proximity to the soil facilitates foliar uptake via air due to exposure to resuspended Hg 0 particles in the lower atmospheric layers. This logarithmic accumulation pattern reinforces the potential use of this species as a bioindicator in Hg contaminated environments. Differences in regression slopes among species may be partly explained by their life strategies: annual species (e.g., R. bucephalophorus ) accumulate Hg over a short vegetative cycle (logarithmic model), whereas biennial or perennial species (e.g., H. incana or S. canina ) are exposed to Hg 0 over multiple years, resulting in accumulation patterns better described by linear models. This supports the hypothesis that plant Hg concentrations reflect a spatiotemporal integration of contaminant exposure rather than a direct response to soil content (Kyllmar et al., 2011 ). Leaf area is a critical factor influencing the absorption of Hg 0 . Plants with larger leaf surfaces exhibit a greater capacity for dry deposition and stomatal uptake of elemental Hg 0 (Laacouri et al., 2013 ; Wohlgemuth et al., 2021 ). Although Q. ilex and R. sphaerocarpa show low correlations with soil Hg (R² = 0.07 and 0.12, respectively), this does not preclude significant Hg accumulation. Their large stature facilitates diffuse atmospheric deposition, enhancing Hg⁰ uptake through stomata and cuticle, particularly during nighttime hours. In the case of R. sphaerocarpa , its smaller leaves may limit direct uptake yet still intercept Hg 0 previously redistributed in the air. Although specific measurements of Hg in stems are lacking, literature suggests that root-shoot translocation in small plants is possible but rarely complete (e.g., Houttuynia; Greger et al., 2005 ; Laacouri et al., 2013 ). Given the known Hg emission sources, the vegetative morphology of each species and their spatial relationship to these sources significantly contribute to explaining the Hg concentrations detected in plant tissues beyond simple a correlation with the soil content. Bioaccumulation and transfer of elements from soil to plant The bioaccumulation coefficients (BAC) obtained for the six plant species show notable variability among elements and species, reflecting physiological differences in their capacity to absorb and accumulate nutrients, common elements, and toxic metals. Potassium, a primary macronutrient essential for osmoregulation and enzymatic activation, exhibited the highest values in S. rubra (2.05), followed by H. incana (1.22), suggesting an efficient adaptation to K deficient soils. Regarding secondary macronutrients, Ca and S showed significant accumulation in H. incana (6.12 and 10.69, respectively), possibly due to its belonging to the Brassicaceae family, known for its resilience in environments with elevated concentrations of heavy metals. The micronutrients Cu, Fe, Mn, and Zn, essential for redox and enzymatic processes, were more highly accumulated by S. rubra (Mn: 0.80, Zn: 0.66) and R. sphaerocarpa (Cu: 0.34, Zn: 0.41), which may be related to differential expression of metal transporters and the production of chelators such as phytochelatins (Alloway, 2013 ; Juwarkar & Yadav, 2010 ). The elements Sr and Rb, common but with no clear biological function, usually absorbed due to chemical similarity with Ca and K, were accumulated in higher proportions by H. incana (Sr: 2.01) and S. rubra (Rb: 0.72), suggesting a low ionic selectivity in these species. Table 3 Bioaccumulation Coefficient (BAC) for different elements analysed. Values of BAC > 1 shown in bolds. K S Ca Cu Fe Mn Zn Sr Rb Hg Pb Quercus ilex Mean ± SD 0.30 ± 0.10 1.21 ± 1.06 1.68 ± 1.54 0.30 ± 0.24 0.30 ± 0.20 0.50 ± 0.30 0.23 ± 0.41 0.16 ± 0.10 0.04 ± 0.02 0.17 ± 0.25 0.12 ± 0.18 Range 0.16–0.53 0.05–4 .18 0.21–5 .69 0.01–0.94 0.01–1 .00 0.10–1 .30 0.00–1 .96 0.04–0.37 0.01–0.08 0.00–0.88 0.00–0.62 Retama sphaerocarpa Mean ± SD 0.43 ± 0.22 1.35 ± 1.36 2.42 ± 2.11 0.34 ± 0.32 0.10 ± 0.01 0.20 ± 0.20 0.41 ± 0.59 0.33 ± 0.23 0.06 ± 0.05 0.09 ± 0.18 0.02 ± 0.04 Range 0.18–1 .20 0.06–5 .13 0.29–8 .96 0.01–1 .09 0.01–0.20 0.01–1 .00 0.00–2 .34 0.05–0.88 0.01–0.23 0.00–0.67 0.00–0.17 Scrophularia canina Mean ± SD 0.76 ± 0.19 1.49 ± 0.95 2.16 ± 2.07 0.26 ± 0.17 0.20 ± 0.20 0.10 ± 0.10 0.10 ± 0.04 0.26 ± 0.16 0.13 ± 0.16 0.02 ± 0.01 0.04 ± 0.04 Range 0.49–1 .00 0.12–2 .56 0.96–6 .32 0.04–0.48 0.01–0.50 0.01–0.30 0.04–0.14 0.08–0.52 0.04–0.45 0.01–0.04 0.00–0.10 Hirschfeldia incana Mean ± SD 1.22 ± 0.48 10.69 ± 5.67 6.12 ± 4.42 0.25 ± 0.09 0.50 ± 0.50 0.70 ± 1.30 0.47 ± 0.36 2.01 ± 1.62 0.18 ± 0.22 0.27 ± 0.30 0.08 ± 0.06 Range 0.75–2 .11 4.82–18.51 1.06–12.45 0.07–0.31 0.01–1 .70 0.10–3 .50 0.14–1 .16 0.54–4 .66 0.08–0.66 0.03–0.67 0.00–0.15 Spergularia rubra Mean ± SD 2.05 ± 0.88 1.69 ± 1.52 3.06 ± 2.10 0.22 ± 0.20 0.70 ± 0.80 0.80 ± 0.80 0.66 ± 0.55 0.17 ± 0.10 0.72 ± 0.45 0.12 ± 0.08 0.15 ± 0.16 Range 1.26–4 .02 0.12–3 .92 0.68–7 .00 0.04–0.64 0.30–2 .50 0.10–2 .70 0.00–1 .44 0.05–0.34 0.35–1 .75 0.01–0.23 0.02–0.54 Rumex bucephalophorus Mean ± SD 1.21 ± 0.37 0.62 ± 0.76 1.32 ± 0.99 0.09 ± 0.07 0.20 ± 0.20 0.50 ± 0.70 0.42 ± 0.39 0.15 ± 0.08 0.10 ± 0.06 0.03 ± 0.01 0.03 ± 0.02 Range 0.76–1 .73 0.07–2 .14 0.30–2 .55 0.01–0.22 0.01–0.80 0.01–2 .00 0.02–1 .18 0.07–0.27 0.03–0.18 0.01–0.05 0.01–0.08 Regarding toxic elements, Hg and Pb, with lack biological function and highly toxic, generally showed low values, although H. incana and S. rubra presented relatively elevated Hg levels (0.27 and 0.12, respectively). This is not due to a deficiency in exclusion mechanisms but rather to the direct uptake of elemental Hg⁰, favoured by the low stature of these species and their proximity to the local soil, acting as a source of Hg emissions. Accumulation of Pb was more homogeneous, with S. rubra standing out slightly (0.15). These results suggest that H. incana and S. rubra possess a broad accumulation profile, including macronutrients, micronutrients, and toxic elements, positioning them as potential candidates for phytoremediation strategies in contaminated soils (Nnaji et al., 2023 ). The ability of these species to accumulate multiple elements may be related to the expression of nonspecific transporters and the production of chelating compounds such as phytochelatins and metallothioneins, mechanisms widely documented in hyperaccumulator species (Alloway, 2013 ; Juwarkar & Yadav, 2010 ). The interspecific variability observed in BAC reflects evolutionary adaptations to specific environmental conditions, such as nutrient poor or heavy metal contaminated soils, as documented in plant families like Brassicaceae and Scrophulariaceae (Juwarkar & Yadav, 2010 ; Alloway, 2013 ). In the case of Q. ilex , a perennial woody species characteristic of Mediterranean ecosystems, a moderately low bioaccumulation pattern was observed compared to the herbaceous species analysed here. BAC values were below 1 for most elements, except for Ca (1.68) and S (1.21), indicating a limited accumulation capacity from the soil. This behaviour may be related to its deep root system and conservative nutrient strategy, typical of sclerophyllous species adapted to oligotrophic environments. Although Q. ilex has been described by many authors as a Mn bioaccumulator in its tissues (Rodà et al., 1999 ; Arena et al., 2014 ; Barquero et al., 2024 ), in this study it exhibited a relatively low BAC value (0.50). This may be attributed to the limited availability of Mn in acidic soils contaminated with iron. Under these conditions, Mn²⁺ is easily oxidized to insoluble forms such as MnO₂, especially in the presence of Fe oxides, reducing its mobility and bioavailability to plants (Paterson et al., 1985 ; Robson, 1988 ; Grangeon et al., 2020 ). As shown in section 3.3, the low height of herbaceous species near the ground may favour the uptake of Hg⁰, while Q. ilex , being a tall species, has less direct exposure to these emissions, capturing only the diffuse concentrations present in the local atmosphere. Altogether, these factors reinforce the role of Q. ilex as a resilient species with efficient regulatory mechanisms for element uptake, even in heavily anthropized environments, as demonstrated by Barquero et al, ( 2024 ). CONCLUSIONS The findings of this study confirm that the San Quintín abandoned mining area constitutes a highly contaminated environment, with elevated concentrations of PTEs in soils and mining residues. The detailed geochemical characterization revealed significant spatial heterogeneity in PTE distribution and speciation, reflecting the legacy of diverse mining activities and waste management practices over more than a century. The analysis of plant–soil interactions demonstrated marked inter- and intraspecific variability in the uptake and bioaccumulation of elements such as Zn, Pb, Hg, and Cu. Notably, Spergularia rubra and Rumex bucephalophorus exhibited high accumulation capacities, underscoring their potential as effective bioindicators and possible candidates for phytoremediation in metal-contaminated environments. These patterns were closely linked to both edaphic factors and species-specific physiological traits, highlighting the complexity of PTE transfer mechanisms. A particularly relevant outcome of this study is the assessment of atmospheric deposition as a key pathway for mercury accumulation in plant tissues. The results indicate that Hg uptake is not solely governed by soil concentrations, but also by proximity to Hg discrete emission sources, plant stature, and leaf surface area—factors that modulate exposure to gaseous elemental mercury (Hg⁰) in the local atmosphere. Overall, this work advances our understanding of the multifactorial processes governing PTEs dynamics in degraded ecosystems. 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Mobility of antimony in contrasting surface environments of a mine site: Influence of redox conditions and microbial communities. Environmental Science and Pollution Research, 30(48), 105808–105828. https://doi.org/10.1007/s11356-023-29734-9 Prieto-Rubio, J., Perea, A., Garrido, J.L., Alcántara, J.M., Azcón-Aguilar, C., López-García, A., & Rincón, A. (2022). Plant traits and phylogeny predict soil carbon and nutrient cycling in Mediterranean mixed forests. Ecosystems, 25(3), 707–722. https://doi.org/10.1007/s10021-022-00815-z Read, D.J., & Mitchell, D.T. (1983). Decomposition and mineralization processes in Mediterranean-type ecosystems and in heathlands of similar structure. In F. J. Kruger, D.T. Mitchell, & J.U.M. Jarvis (Eds.), Mediterranean-Type Ecosystems: The Role of Nutrients (pp. 208–232). Springer. https://doi.org/10.1007/978-3-642-70868-8_23 Robson, A.D. (1988). Manganese in soils and plants—An overview. In R. D. Graham, R. J. Hannam, & N. C. Uren (Eds.), Manganese in Soils and Plants (pp. 329–333). Springer. https://doi.org/10.1007/978-94-009-2817-6_22 Rodà, F., Retana, J., Gracia, C.A., & Bellot, J. (Eds.). (1999). Quercus ilex L. ecosystems: function, dynamics and management. Springer. https://doi.org/10.1007/978-94-017-2836-2 Rodríguez, L., Ruiz, E., Alonso-Azcárate, J., & Rincón, J. (2009). Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain. Journal of Environmental Management, 90(2), 1106–1116. https://doi.org/10.1016/j.jenvman.2008.04.007 Sánchez-Donoso, R., Martín-Duque, J.F., Crespo, E., & Higueras, P. (2019). Tailing’s geomorphology of the San Quintín mining site (Spain): Landform catalogue, aeolian erosion and environmental implications. Environmental Earth Sciences, 78(5), 166. https://doi.org/10.1007/s12665-019-8148-9 Sardans, J., & Peñuelas, J. (2013). Plant-soil interactions in Mediterranean forest and shrublands: Impacts of climatic change. Biogeochemistry, 113(1-3), 1–19. https://doi.org/10.1007/s10533-012-9792-2 Sholupov, S., Pogarev, S., Ryzhov, V., Mashyanov, N., & Stroganov, A. (2004). Zeeman atomic absorption spectrometer RA-915+ for direct determination of mercury in air and complex matrix samples. Fuel Processing Technology, 85(6–7), 473–485. https://doi.org/10.1016/j.fuproc.2003.11.003 Stamenkovic, J., Gustin, M.S. 2009 Nonstomatal versus stomatal uptake of atmospheric mercury. Environmental Science and Technology 43(5), pp. 1367-1372. https://doi.org/10.1021/es801583a Thalassinos, G., Petropoulos, S. A., Grammenou, A., & Antoniadis, V. (2023). Potentially toxic elements: A review on their soil behavior and plant attenuation mechanisms against their toxicity. Agriculture, 13(9), 1684. https://doi.org/10.3390/agriculture13091684 Udden-Wentworth, C.K. A scale of grade and class terms of clastic sediments. J. Geol. 1922, 30, 377–392. Walkley. A. and Black. I.A. (1934) An Examination of the Degtjareff Method for Determining Soil Organic Matter and a Proposed Modification of the Chromic Acid Titration Method. Soil Science 37, 29-38. http://dx.doi.org/10.1097/00010694-193401000-00003 Wang, Y., & Schreiber, S.L. (2024). Mechanisms of calcium homeostasis orchestrate plant growth and immunity. Nature , 619, 120–130. https://doi.org/10.1038/s41586-024-07100-0 White, P.J., & Broadley, M.R. (2003). Calcium in plants. Annals of Botany , 92(4), 487–511. https://doi.org/10.1093/aob/mcg164 Wohlgemuth, D., Bahlmann, E., & Kesselmeier, J. (2021). Mercury uptake by leaves: A comparison of different plant species and morphologies. Biogeosciences, 18, 6313–6328. https://doi.org/10.5194/bg-18-6313-2021 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6991303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480186566,"identity":"0370a6c4-3136-49f8-94f0-052acf199226","order_by":0,"name":"José Ignacio Barquero Peralbo","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7716-3356","institution":"University of Castilla-La Mancha: Universidad de Castilla-La Mancha","correspondingAuthor":true,"prefix":"","firstName":"José","middleName":"Ignacio Barquero","lastName":"Peralbo","suffix":""},{"id":480186567,"identity":"acc5fb6b-4396-426e-91c1-5d9272fe05e5","order_by":1,"name":"Jesús Peco","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Peco","suffix":""},{"id":480186568,"identity":"44b6e8c4-ea85-4b14-b13b-b0059b27264c","order_by":2,"name":"Jaime Villena","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Villena","suffix":""},{"id":480186569,"identity":"13dda5b5-7d28-4172-a05f-f6ff4ef1106e","order_by":3,"name":"Juan Antonio Campos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Antonio","lastName":"Campos","suffix":""},{"id":480186570,"identity":"f51612d9-a97e-4532-bdca-9034db3bd920","order_by":4,"name":"José María Esbrí","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"María","lastName":"Esbrí","suffix":""},{"id":480186571,"identity":"857b09d7-b681-40b2-a213-d55f183927ff","order_by":5,"name":"Francisco J. García-Navarro","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"J.","lastName":"García-Navarro","suffix":""},{"id":480186572,"identity":"77c35049-c23e-43dc-b76c-a56ddca5a85d","order_by":6,"name":"Marta María Moreno","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"María","lastName":"Moreno","suffix":""},{"id":480186573,"identity":"0c291973-4477-47fd-9a39-eccf11a1cf25","order_by":7,"name":"Pablo Higueras","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Higueras","suffix":""}],"badges":[],"createdAt":"2025-06-27 11:52:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6991303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6991303/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86135479,"identity":"31e512b0-1770-43fb-b8c1-b178213d525d","added_by":"auto","created_at":"2025-07-07 07:42:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":853941,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Location of the study area in the San Quintín mining district (Ciudad Real, Spain). (B) Mine gallery drainage outlet showing acid mine drainage (AMD) discharge. (C) Detailed site map displaying sampling locations, mining infrastructure, waste deposits (including flotation tailings, olive mill residues, and cinnabar-rich dumps), along with important environmental characteristics relevant to the study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/f3c9d91380e926e6def222cd.png"},{"id":86135478,"identity":"7e9baaa8-d6e1-442e-9195-d71225199e54","added_by":"auto","created_at":"2025-07-07 07:42:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82997,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram for the analytical values for soils.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/b7ead92b01487f678a49038f.png"},{"id":86135482,"identity":"2123d848-0d9e-4e98-b674-614e93c33bbe","added_by":"auto","created_at":"2025-07-07 07:42:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":365108,"visible":true,"origin":"","legend":"\u003cp\u003eScheme showing the location of the samples on the different substrates, as well as the dendrogram confirming the geochemical similarities between the samples taken on these substrates.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/d66389c166423a933bcc53e5.png"},{"id":86137677,"identity":"5fbe6a97-ac8d-4efa-8b1e-2376c03cd08f","added_by":"auto","created_at":"2025-07-07 07:58:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100859,"visible":true,"origin":"","legend":"\u003cp\u003eFactor analysis double projection graphic for the two first factors, soil samples grouped by substrate.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/ad13f83a06cb4ef584f3008c.png"},{"id":86136264,"identity":"c181990e-ed93-458a-bff9-9b3f8f6fd50c","added_by":"auto","created_at":"2025-07-07 07:50:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":109675,"visible":true,"origin":"","legend":"\u003cp\u003eFactor analysis double projection graphic for the two first factors, plant samples grouped by species.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/4c3a311d0b96af174ecdd89f.png"},{"id":86136265,"identity":"5a7c7fc5-1434-47c1-8f78-1da0ca5530f5","added_by":"auto","created_at":"2025-07-07 07:50:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95462,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between soil and plant tissue Ca concentrations in two studied species: Scrophularia canina (left), and Rumex bucephalophorus (right).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/bae22586e3cf48250c392f89.png"},{"id":86135486,"identity":"1dba3b8f-761d-42f2-b954-20958e690932","added_by":"auto","created_at":"2025-07-07 07:42:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelationship between soil and plant tissue Sr concentrations in Scrophularia canina.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/636520baa096640018609237.png"},{"id":86137680,"identity":"790fb314-dad1-4886-8672-1d840dfa3bbf","added_by":"auto","created_at":"2025-07-07 07:58:23","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":314144,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between THg concentrations in soil and plant tissues of six species present in the study area, including height range and leaf area of each species.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/565abe5ed881a28a4f2f43dd.png"},{"id":86954824,"identity":"9b0298d3-e757-454c-ad65-bc11ffe2c49a","added_by":"auto","created_at":"2025-07-17 14:57:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3280724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6991303/v1/1be643f0-8638-468f-84c2-cb31eac0b500.pdf"}],"financialInterests":"","formattedTitle":"Transference of potentially toxic elements from soils to plants in a derelict Pb-Zn mining area (San Quintín mine, South-Central Spain)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePlants absorb essential elements, including Cu, Ni, Fe, Zn, Mn, and Mo, which are required for their normal physiological functions. However, when their concentrations exceed certain thresholds, these essential elements can also induce phytotoxic effects. In addition to these, plants can absorb non-essential elements with no known biological function, some of which are highly toxic (Kirkby, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peco et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Both essential elements that become toxic at elevated concentrations and inherently toxic non-essential elements are collectively referred to as potentially toxic elements (PTEs) (Kisku et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Thalassinos et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Different plant species employ distinct mechanisms for the uptake of these elements from the soil. Nevertheless, the efficacy of this uptake strongly depends on the bioavailability of these elements in the soil (Newman et al., 1994). The so-called bioavailability, constrained by the chemical state or speciation of the elements, is conditioned by different factors (Gal\u0026aacute;n \u0026amp; Romero, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Primary among these factors are climate conditions, which control water availability and its temperature-dependent activity in the soil. Additionally, geochemical behaviour, mineralogical form, and the presence of biological populations in the soil, capable of reacting with minerals containing the elements and potentially destroying their crystalline structure, are noteworthy contributors (Molina et al., 2006; Kabata Pendias, 2011). As a result, the absorption of PTEs in plants is a highly intricate process due to the multitude variables involved. Going even further, the uptake of PTEs from the soils can favour their bioaccumulation in edible parts, producing a risk for the incorporation of PTEs (especially in toxic concentrations) into vertebrates, including humans. This situation becomes a significant public health concern (Kabata Pendias, 2011; Adamo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDecommissioned mining areas are of particular interest for the study of the process of elemental incorporation into plants and assessing risks related to such process. Mining areas, especially those abandoned before the development of an ecological consciousness, act as sources of PTEs for nearby agronomic fields. This is due to the eventual persistence of waste accumulations that can be easily disseminated with the help of rainwater and wind to the neighbour areas (S\u0026aacute;nchez-Donoso et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The presence of pyrite (FeS\u003csub\u003e2\u003c/sub\u003e) in the mined ores is of particularly concern, since this mineral is easily hydrolysed in atmospheric conditions, producing acidity which also favours the hydrolysis of the rest of metallic sulphides, in particular sphalerite (ZnS), galena (PbS) and chalcopyrite (CuFeS\u003csub\u003e2\u003c/sub\u003e) (Harries and Ritchie, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). This process induces the release of corresponding PTE ions into the soils, taking on different solubility forms depending on the geochemical affinity of each ion for others. For instance, Pb\u003csup\u003e2+\u003c/sup\u003e readily combine with SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2+\u003c/sup\u003e or CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2+\u003c/sup\u003e, giving origin to insoluble anglesite (PbSO\u003csub\u003e4\u003c/sub\u003e) or cerussite (PbCO\u003csub\u003e3\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eThe San Quint\u0026iacute;n Pb-Zn-Ag area, located in the Southern Central Iberian Zona of the Hesperian Massif, exploited a mineralization hosted by shales and greywackes of pre-Ordovician age, and constituted by a typically hydrothermal mineral paragenesis comprising all the previously mentioned minerals: galena rich in silver (most probably due to the presence of Ag sulfosalts associated to it), sphalerite, chalcopyrite (scarce), and abundant pyrite, with quartz and minor calcite as ganga minerals (Palero, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Also, after this author, the mine was active since old times, with a most important period of activity between years 1894 and 1923, and with a declared production of galena concentrates of some 500 000 tonnes. As a result of this activity, piles of residua with high residual concentration of Zn were left in the area, due to the low price of this element. But in the 1950\u0026rsquo;s, the increase in the price of this element, used in galvanization in the automotive industry, supposed the installation in the area of a \u0026lsquo;modern\u0026rsquo; froth flotation plant, in which the former residua were in part reprocessed to obtain Zn, Pb and Ag. This plant produced two types of tailings: the materials with lower grain size, including silt and clay, were disposed in an embarquement dam, meanwhile the materials with sand size passed though the froth flotation process and the residua were accumulated in a tailings pile. In 1990 an experimental test to obtain cinnabar (HgS) concentrates from the ore coming from the Almad\u0026eacute;n mercury mine (located some 80 km to the West; Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, among many others) was performed, and both residua on the original ore and of the cinnabar concentrates obtained were also abandoned in the area. After that, in the same year 1990 the froth flotation plant was abandoned, as well as the still unprocessed dumps and the dams. It is worth to note that an old gallery for dumps drainage produce seasonal outputs of Acid Mine Drainage (AMD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), with very low pH (2 in average) and with high saline concentration (Electric conductivity\u0026thinsp;\u0026gt;\u0026thinsp;8000 \u0026micro;S-cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as a norm). The area is at present under remediation, funded by European Next Generation funds.\u003c/p\u003e\u003cp\u003ePrevious studies in the area have characterized elemental contents in soils and their speciation (Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Other investigations have compared the composition of the tailings with those from other exploited ore deposits in the region (Higueras et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); assessed the transference of PTEs from soil to acorn-tree (\u003cem\u003eQuercus ilex\u003c/em\u003e) leaves, transference of PTEs also on a regional basis (Higueras et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); studied the geomorphology of the wastes piles to obtain conclusions on the dispersion processes of the fine material by water and wind (S\u0026aacute;nchez-Donoso et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; 2021); assessed the toxicity of the waste materials using integrated geochemical and toxicological studies (Garc\u0026iacute;a-Lorenzo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ferri-Moreno et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and studied the microbial diversity of the different types of soils present in the area (Gallego et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and its influence of the mobility of PTEs in the soil (Peco et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies conclude that the area is heavily contaminated, with soils containing potentially toxic elements (PTEs) within the ranges reported by Rodr\u0026iacute;guez et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Higueras et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Notably, elevated concentrations of Pb, Zn, Hg, and Cd have been detected in \u003cem\u003eQuercus ilex\u003c/em\u003e leaves (Higueras et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the presence of mine residues poses a significant risk to living organisms (Garc\u0026iacute;a-Lorenzo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ferri-Moreno et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, different types of soils exhibit distinct microbial population, each influencing the mobility and bioavailability of PTEs (Gallego et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peco et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the present study, we have used the San Quint\u0026iacute;n area as a natural laboratory, providing a unique opportunity to investigate the long-term impact of waste (most for over 100 years) on the environment. The study focuses on plant-soil interactions, particularly with plants living in the area since time before the main mine activity occurred (\u003cem\u003eQ. Ilex\u003c/em\u003e), as well growing directly on the waste material, including long-standing species like \u003cem\u003eRetama sphaerocarpa\u003c/em\u003e and \u003cem\u003eScrophularia canina\u003c/em\u003e, as well as annual species like \u003cem\u003eSpergularia rubra\u003c/em\u003e, \u003cem\u003eRumex bucephalophorus\u003c/em\u003e and \u003cem\u003eHirschfeldia incana\u003c/em\u003e. Besides, we have a high number of previous studies based in the soil properties, very useful to complete the portrait of the area. The comprehensive analysis, including plants assessments presented in this study, contributes to evaluating the intricate plant-to-soil transfer process in this complex environment. This investigation serves as a basis to understand the peculiarities of processes in extreme environments, specifically decommissioned mine areas affected by AMD, and in an evolved state of vegetal cover regeneration.\u003c/p\u003e\u003cp\u003eThe main objectives of this study have been: a) to perform a detailed geochemical characterization of the San Quint\u0026iacute;n mining area, focusing on the distribution, speciation, and mobility of potentially toxic elements (PTEs) in soils and mining residues; b) to assess the species-specific patterns of PTE uptake and bioaccumulation in native and colonizing plants, identifying key physiological and environmental factors influencing this process and evaluating their potential as bioindicators or candidates for phytoremediation in contaminated environments; and c) to investigate the role of atmospheric deposition in mercury accumulation in plant tissues, considering species morphology and proximity to emission sources.\u003c/p\u003e\u003cp\u003eThe insights gained are not only valuable for assessing risks withing this environment concerning the human food chain but also hold significance for similar areas affected by similar contamination processes.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eSampling and processing\u003c/p\u003e\u003cp\u003e\u003cem\u003eSite description and sampling\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSoil sampling was conducted in the eastern sector of the San Quint\u0026iacute;n area, selected for its broader spatial extent, high plant species diversity, and the presence of distinct environmental domains warranting detailed characterization. A total of 23 sampling sites were established, distributed across three primary domains: the area containing the oldest mining residues (5 sites), zones affected by the froth flotation process (7 sites), and peripheral soils (11 sites) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to data reported by Rodr\u0026iacute;guez et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the highest concentrations of PTEs were found in the oldest wastes deposits, followed by the flotation tailings. Peripheral soils exhibited substantial variability in PTE concentrations, likely influenced by their proximity to contaminated zones, as well as by secondary dispersion mechanisms such as wind erosion, surface runoff, and post-mining anthropogenic activities.\u003c/p\u003e\u003cp\u003eAt each selected site, chosen based on the presence of vegetation of particular ecological or conservation interest, a composite soil sample was collected by excavating up to a maximum depth of 20 cm. In certain locations, this depth could not be reached due to excessive soil compaction, likely resulting from historical anthropogenic disturbances such as former waste deposits. Three subsamples were extracted from randomly distributed points within a 10\u0026ndash;15 m\u0026sup2; plot to ensure spatial representativeness. Approximately 5 kg of soil per site was obtained, homogenized, and stored in high-density polyethylene (HDPE) bags, properly labelled with site code, date, and sample ID. All samples were transported under ambient conditions and stored at 4\u0026deg;C upon arrival at the laboratory to preserve their physicochemical integrity. Additionally, the geographic coordinates of each sampling site were recorded using a handheld GPS device with sub-meter accuracy.\u003c/p\u003e\u003cp\u003eVegetation samples were also collected at each sampling site, including multiple species whenever possible. The sampled taxa included one tree species (\u003cem\u003eQuercus ilex\u003c/em\u003e), two shrub species (\u003cem\u003eRetama sphaerocarpa\u003c/em\u003e and \u003cem\u003eScrophularia canina\u003c/em\u003e), and three annual herbaceous species (\u003cem\u003eHirschfeldia incana, Rumex bucephalophorus\u003c/em\u003e, and \u003cem\u003eSpergularia rubra\u003c/em\u003e). For the tree species, mature leaves were sampled from multiple individuals per site when available, avoiding both senescent and newly emerged leaves (typically located at the basal and apical portions of branches, respectively). In the case of annual herbs, the entire aerial part of at least ten individuals per species was collected to ensure representativeness. All plant material was stored in paper envelopes to allow for adequate drying and prevent fungal growth, and samples were processed within 10 days of collection to preserve their biochemical integrity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSample preparation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe preparation of soil samples started with their drying (in opened plastic bag for 15 days). Once dry, they were sieved to discard the \u0026gt;\u0026thinsp;2 mm fraction, which was weighted to make a rough estimation of their gravel content. The \u0026lt;\u0026thinsp;2 mm fraction was homogenized and subdivided into three aliquots: one for physicochemical determinations without further treatment, a second aliquot was grinded in an agata mortar to \u0026lt;\u0026thinsp;100 \u0026micro;m size for chemical analysis, and a third was retained as a backup sample.\u003c/p\u003e\u003cp\u003ePlant samples were washed with tap water followed by deionised water. Afterward, they were dried in a laboratory stove prior to a trituration using a KINEMATICA mixer (MB800 B). A portion of the homogenized samples was separated for Hg analysis, while a second aliquot (5 g) was blended with 0.15 g of agglutinant (dissolution of Elvacite 2046 PANalytical and ACETONA PURISS (CH\u003csub\u003e3\u003c/sub\u003e(CO)CH\u003csub\u003e3\u003c/sub\u003e), UN 1090). The mixture was inserted into aluminium vessels (RETSCH PP25) and compressed using a hydraulic hand press (SPECAC 250 kN) to obtain a pressed pill, utilized in the analytical procedure for X-Ray Fluorescence (XRF) analysis.\u003c/p\u003e\u003cp\u003eAnalytical methods\u003c/p\u003e\u003cp\u003eThe physicochemical properties of the soils, including reactivity (pH), salts contents based on electric conductivity (EC), and organic matter content, were determined using conventional methods according to the standards UNE-ISO 10390:2012 for pH (AENOR, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and UNE 77308:2001 for EC (AENOR, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In particular, pH and EC measurements were determined after dispersion of the sample in water at a soil/water ratio of 10 gr/50 mL; in the resulting dispersion, the reactivity was estimated using a CRISON GLP 22 pHmeter, and the EC, with a CRISON GLP 32 conductivimeter. The organic matter content (SOM) was estimated using the method proposed by Walkey \u0026amp; Black (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1934\u003c/span\u003e), based on quantitative oxidative valuation of 1 soil suspension using a mixture of sulphuric acid and K dichromate. The grain size distribution was evaluated using a Fritsch ANALYSETTE MicroTec Plus device, following the procedure outlined by Garcia-Ordiales et al. (2018), and applying the traditional sand-silt-clay classification (Udden-Wentworth, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1922\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBoth soil and vegetal samples were analysed for Hg and multielement. Hg analysis was performed by means of atomic absorption spectrometry with Zeeman effect, using a LUMEX RA-915M device (Sholopov et al., 2004). Multielemental analysis was performed via X-ray fluorescence with energy dispersion (EDXRF), using a Malvern Panalytical Epsilon 1 device.\u003c/p\u003e\u003cp\u003eCertified Reference Materials of soils (NIST 2710A) and plants (GC7162) were analysed to ensure precision and accuracy. PTE recovery rates were in the range of 93\u0026ndash;114% (XRF) and 94\u0026ndash;103% for Hg (ZAAS-HFM).\u003c/p\u003e\u003cp\u003eSoil to plant transfer indices\u003c/p\u003e\u003cp\u003eThe bioaccumulation coefficients (BAC) were formulated (Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as a simple parameter to assess the bioavailability of elements in the soil, and the capacity of the plant to uptake them (Inacio et al. 2014; Gruszecka-Kosowska 2019).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\left(BAC\\right)=\\:\\frac{\\left[C\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\right]}{\\left[C\\text{s}\\text{o}\\text{i}\\text{l}\\right]}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere [\u003cem\u003eC\u003c/em\u003eplant] and [\u003cem\u003eC\u003c/em\u003esoil] represent the concentration (in mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of a given element in the leaves and soil, respectively, corresponding to the same sampling site. Values of BAC\u0026thinsp;\u0026gt;\u0026thinsp;1 indicate a high bioaccumulation capacity, especially when BACs are calculated with total concentration in soil. The BACs calculated with the soluble fraction in soil are higher, although they more precisely express the hyperaccumulator condition of the plant in a certain polluted substrate.\u003c/p\u003e\u003cp\u003eStatistical Analyses and Mapping\u003c/p\u003e\u003cp\u003eData treatment involved multivariate analyses performed using Minitab 19.1 and STATGRAPHICS Centurion v19.1.2. Hierarchical clustering was applied to explore relationships among variables, using Ward\u0026rsquo;s linkage method and correlation coefficient distance as the similarity measure. Spatial distribution patterns were generated by inverse distance squared interpolation with Surfer 21.1.158 (Golden Software) and ArcMap 10.8.1 (ArcGIS).\"\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eSoil analysis\u003c/p\u003e\u003cp\u003eThe soil texture shows a prevailing composition of sandy loam soils (average: 75.5% sand, 10.4% silt and 14.1% clay), with higher sand contents located leeward to the sand-sized tailings in agricultural areas. The SOM contents range from 0.1 to 5.8%, with an average of 2.95%, which are common values for soils in this semiarid region (Gallardo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).The lower values (0.1-1%) appear in the soils more clearly affected by the mining activity, intermediate values (1\u0026ndash;3%) in soils peripheral to the most affected area, and the highest levels found in agricultural and pasture areas. Soil reactivity is acidic (pH: 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7), with the lowest values corresponding to the soils from the proximity of tailings dumps. The contents in salts, as assessed by the EC, show a direct inverse relationship with pH, reaching 12,000 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in sites close to mine residua accumulation with the lower pH values. In areas affected less intensely by mining activities, there is no conspicuous relationships among both parameters, with near neutral soil reactivity and EC: 515.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,585.4 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; this high variability in EC is conditioned by the diversity of soil typology in the mine area.\u003c/p\u003e\u003cp\u003eThe results of the soil samples are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Considering the major elements, it is worth noting that the SiO\u003csub\u003e2\u003c/sub\u003e concentrations are relatively constant in terms of the coefficient of variation, although the range of values involved is wide (35.5\u0026ndash;71.7%), as observed for Al\u003csub\u003e2\u003c/sub\u003eO3 and TiO\u003csub\u003e2\u003c/sub\u003e. This suggests the homogeneity of the geological substrate, primarily constituted by shales and greywackes, but affected by the presence of wine wastes. On the other hand, the maximum variability among major elements is found in CaO, which should be an indication of the occasional presence of carbonates rich in Ca in the mineralized veins. Amongst trace elements, the highest variability corresponds to: Zn\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;=\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Hg\u0026thinsp;\u0026gt;\u0026thinsp;Sb\u0026thinsp;\u0026gt;\u0026thinsp;S\u0026thinsp;\u0026gt;\u0026thinsp;Ba. This variability is linked to the variability in the abundance of the elements in the deposit and their mobility, which explains a similar variability patterns for Pb and Zn, with Zn being considerably less abundant but exhibiting higher mobility (Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\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\u003eBasic statistics for the elemental analysis of the soil samples. Abbreviations: SD: standard deviation; VC: Variation coefficient (in %).\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e71.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,295.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,328.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e170.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e304.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25,819.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e109.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e868.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e733.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e124.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3,080.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e357.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e192.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,680.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,388.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,004.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e295.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48,460.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e190.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e170.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e805.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e427.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e183.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,920.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15,913.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e198.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57,270.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e121.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e139.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e466.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e204.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e147.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e894.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e123.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emg kg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e602.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e628.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e147.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2,580.0\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 cluster analysis of soil data results in the dendrogram shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the different elements. Three major clusters (Cl) have been distinguished: Cl1 groups Al, Rb, K and Cr; this appears as a geogenic cluster related to compositional differences in soil composition. Cl2 includes S, Fe, Sb, Sn, Pb Ba, Cu, Zn, and, with minor similarity, Ca and Sr, which includes most of the PTEs characteristic of the mineralogy of the exploited ore deposit. Finally, Cl3 includes Si and Hg with P, Zr, Ti and Mn, what should be related to the presence of cinnabar mineralized rocks, coming from El Entredicho mine in Almad\u0026eacute;n, to test the froth flotation of this ore; Si is the main component of these samples, since the cinnabar is hosted by quartzites; and P, Zr, Ti and Mn could be also related to the presence, together with the mineralized quartzites, of big boulders of the magmatic rock which, together with the quartzites, host the cinnabar deposit of El Entredicho mine (Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBesides, the dendrogram from the cluster analysis of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) clearly differentiates between three types of samples, which, furthermore, corresponds clearly with three domains with different degrees of contamination (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): a highly contaminated domain, coincidental with the central area (brown colour), where the mine wastes, with high marginal contents in ore minerals, persist although substantial dismantling efforts; a second domain, corresponding to tailings from the froth flotation wastes, with intermediate level of contamination (red colour); a third domain corresponding to samples from marginal areas, characterized by limited contamination (green colour).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe PCA analysis, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, effectively discriminates soil samples according to their geochemical composition and substrate. The first two principal components explain a cumulative 62% of the total variance (47.1% by the first component and 14.9% by the second), providing a robust basis for interpreting the main geochemical gradients. Samples with negative scores on the first component are primarily associated with peripheral soils, characterized by low concentrations of lithogenic elements such as Si, Zr, Ti, and Mn, and relatively higher levels of Zn, Cu, Pb, Sb, Ba, Ca, S, Sn, Fe, and Sr (elements typically linked to anthropogenic inputs). The second component separates samples with elevated concentrations of P, Mn, and Zr (positive scores) from those with higher Hg content (negative scores). Former waste samples are mainly distributed along the positive side of the first component, reflecting their enrichment in ore-related elements, while their position along the second component is more variable, though predominantly negative. In contrast, tailing samples cluster near the origin, with low scores on both components, indicates a more homogeneous and less extreme geochemical profile.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePlant analysis\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e synthetizes the analytical results for the studied plant species. We consider interesting to notice that the concentrations of primary and secondary macronutrients (K, S, and Ca) vary significantly among the species. For instance, \u003cem\u003eS. rubra\u003c/em\u003e shows very high concentrations of K and S compared to \u003cem\u003eQ. ilex\u003c/em\u003e and \u003cem\u003eR. sphaerocarpa\u003c/em\u003e, which present levels roughly an order of magnitude lower. Meanwhile, the concentration of Ca in \u003cem\u003eH. incana\u003c/em\u003e is about one order of magnitude lower than that found in the rest of the analysed species. These differences can be explained by species-specific physiological traits and ecological adaptations common in Mediterranean ecosystems. Species like \u003cem\u003eS. rubra\u003c/em\u003e tend to accumulate higher amounts of potassium and sulfur, which are critical for processes such as osmotic regulation and protein synthesis, particularly in plants adapted to nutrient-rich or rapidly growing environments (Kruger, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Sardans \u0026amp; Pe\u0026ntilde;uelas, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, the markedly lower Ca concentration in \u003cem\u003eH. incana\u003c/em\u003e may reflect adaptations to soils with low Ca availability or acidic conditions, a pattern reported in species thriving in nutrient-poor substrates (Groves, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Read \u0026amp; Mitchell, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Overall, these nutrient concentration patterns align with the observed functional traits and phylogenetic differences that influence nutrient cycling and uptake strategies in Mediterranean plant communities (Prieto-Rubio et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), supporting that such variability is typical and expected in similar ecosystems.\u003c/p\u003e\u003cp\u003eConsidering the trace elements, both those considered as micronutrients, common in plants, and toxic for plants, and considering than most of them display a high to extreme variability in the soils of the area, the variability has two conditions: the one considering the different species, and the one considering the different specimens of the same species. The species with higher concentrations in the different elements are: Cu: \u003cem\u003eS. rubra\u003c/em\u003e\u0026thinsp;~\u0026thinsp;\u003cem\u003eR. bucephalophorus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;all the rest; Fe: \u003cem\u003eS. rubra\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eH. incana\u003c/em\u003e\u0026thinsp;~\u0026thinsp;\u003cem\u003eR. bucephalophorus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eQ. ilex\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eS. canina\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. sphaerocarpa\u003c/em\u003e; Mn: \u003cem\u003eQ. ilex\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eS. rubra\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. bucephalophorus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eH. incana\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. sphaerocarpa\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eS. canina\u003c/em\u003e; Zn: \u003cem\u003eS. rubra\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. bucephalophorus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. sphaerocarpa\u003c/em\u003e\u0026thinsp;~\u0026thinsp;\u003cem\u003eH. incana\u003c/em\u003e\u0026thinsp;~\u0026thinsp;\u003cem\u003eS. canina\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eQ. ilex\u003c/em\u003e; Hg: S. \u003cem\u003erubra\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;all the rest; and Pb: \u003cem\u003eS. rubra\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. bucephalophorus\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eQ. ilex\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;the rest.\u003c/p\u003e\u003cp\u003eTherefore, the plants with higher bioaccumulation capacity, considering the elements related with the mining activity, should be \u003cem\u003eS. rubra\u003c/em\u003e and \u003cem\u003eR. bucephalophorus\u003c/em\u003e, with \u003cem\u003eQ. ilex\u003c/em\u003e also showing a noticeable capacity, in particular for Mn and Pb. Additionally, rubidium (Rb) and strontium (Sr) were analysed due to their chemical similarity to K and Ca, respectively. However, their concentrations were generally low and showed no consistent pattern among species, suggesting limited physiological relevance in this context.\u003c/p\u003e\u003cp\u003eOn the other hand, intraspecies variability, as expressed by the variation coefficient, is maxima for Hg in \u003cem\u003eR. sphaerocarpa\u003c/em\u003e (for very low total concentrations), in \u003cem\u003eQ. Ilex\u003c/em\u003e, and in \u003cem\u003eS. rubra\u003c/em\u003e (229.9, 183.2 and 163.8%, respectively); Rb in \u003cem\u003eS. canina\u003c/em\u003e (141.1%); and Pb in \u003cem\u003eR. bucephalophorus, Q. ilex\u003c/em\u003e and \u003cem\u003eR. Sphaerocarpa\u003c/em\u003e (148.4, 123.7 and 120.0% respectively). These variation coefficients are the best indications for a real variability in the bioaccumulation into each species, since these represent the variability related with variations in the contents and therefore, of bioavailability in the soil.\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\u003eMean, standard deviation, and coefficient of variation for primary and secondary macronutrients, micronutrients, common plant elements, and toxic elements in leaf tissues. All values are expressed in mg kg\u003csup\u003e-1\u003c/sup\u003e, except for Hg, which is expressed in \u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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\u003e\u003cem\u003ePrimary macronutrient\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSecondary macronutrients\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMicronutrients\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eCommon elements in plants\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e\u003cem\u003eToxic elements for plants\u003c/em\u003e\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\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eSr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eRb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuercus ilex\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,286.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,554.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,887.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e682.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e341.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e124.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e132.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,474.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e659.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,570.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e480.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e264.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e164.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e183.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e123.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetama sphaerocarpa\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,659.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,922.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,437.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e225.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e274.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,485.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e625.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,173.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e202.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e73.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e70.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e224.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e120.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScrophularia canina\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,718.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,695.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,664.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e466.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e221.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e67.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,362.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,644.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e357.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e233.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e105.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e142.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e107.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e74.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHirschfeldia incana\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,601.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,631.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29,098.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e966.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e104.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e222.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e54.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,141.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,797.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12,216.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e593.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e130.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e47.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e86.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e103.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e87.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpergularia rubra\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,414.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,111.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,217.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,711.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e290.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,232.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1,459.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,017.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,650.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,524.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,359.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e128.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e950.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e45.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1,632.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e42.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e163.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e111.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRumex bucephalophorus\u003c/b\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,512.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,259.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,848.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e960.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e154.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e927.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e552.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,937.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e609.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,969.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e545.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e455.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e819.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e102.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e148.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eBarquero et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003e\u003cb\u003eQ. ilex\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,032.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e322.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e833.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonaci et al. (2022) (\u003c/b\u003e\u003cb\u003eQ. ilex\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,300.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigueras et al. (2016) (\u003c/b\u003e\u003cb\u003eQ. ilex\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eMarschner (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKabata-Pendias (2001) (Mature leaf tissue generalized for various species)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30\u0026ndash;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u0026ndash;662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToxicity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e400-1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100\u0026ndash;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e30\u0026ndash;300\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 PCA performed on the elemental concentrations in plant tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed clear species-specific clustering patterns, reflecting distinct uptake strategies and affinities for PTEs. The first two components accounted for 59.7% of the total variance (PC1: 35.6%, PC2: 24.1%). \u003cem\u003eSpergularia rubra\u003c/em\u003e exhibited the highest scores along PC1, which was primarily associated with PTEs such as Cu, Zn, Pb, and Hg, as well as Rb, Fe, and K, indicating a strong bioaccumulation capacity. \u003cem\u003eHirschfeldia incana\u003c/em\u003e was clearly separated along PC2, showing highly negative values, and positive PC1 scores, suggesting a distinct elemental profile dominated by Mn, Sr, and Ca. \u003cem\u003eRumex bucephalophorus\u003c/em\u003e occupied the upper left quadrant, with positive values on both PC1 and PC2, indicating a mixed accumulation pattern. In contrast, \u003cem\u003eQuercus ilex\u003c/em\u003e, \u003cem\u003eRetama sphaerocarp\u003c/em\u003ea, and \u003cem\u003eScrophularia canina\u003c/em\u003e clustered in the lower left quadrant, with predominantly negative PC1 values and low PC2 scores, reflecting lower accumulation of PTEs and a more conservative elemental uptake strategy. These results underscore the functional diversity among species in response to soil contamination and support the potential use of \u003cem\u003eS. rubra\u003c/em\u003e and \u003cem\u003eR. bucephalophorus\u003c/em\u003e as fine bioindicators in metal-impacted environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eRelationships between concentrations in soil and plant\u003c/p\u003e\u003cp\u003eElemental concentrations in plants do not necessarily depend on total concentrations in soils. We evaluated this relationship and found that the studied species exhibited contrasting responses in terms of bioaccumulation of secondary macronutrients, common plant elements, and toxic elements, relative to their concentrations in the soil.\u003c/p\u003e\u003cp\u003eAmong the studied species, contrasting patterns were observed regarding Ca bioaccumulation in relation to its soil concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). \u003cem\u003eR. bucephalophorus\u003c/em\u003e showed a positive linear correlation (Ca in plant tissue\u0026thinsp;=\u0026thinsp;0.166 \u0026times; Ca in soil \u0026ndash; 3,571.6; R\u0026sup2; = 0.62), indicating that approximately 62% of the variability in Ca content in plant tissues can be explained by its concentration in the soil. This relationship suggests an efficient Ca uptake strategy in this species, possibly linked to its annual life cycle and opportunistic nutrient acquisition behaviour (White \u0026amp; Broadley, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Marschner, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, \u003cem\u003eS. canina\u003c/em\u003e exhibited a moderate negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, left; R\u0026sup2; = 0.60), where higher soil Ca concentrations were associated with lower tissue concentrations. This may reflect physiological regulation mechanisms to prevent toxicity or ionic imbalances, particularly in acidic soils with high electrical conductivity. Such responses have been reported in plants adapted to nutrient-poor or acidic soils, where tight control of Ca transport becomes essential (Pathak et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The limited mobility of Ca in the phloem and its dependence on transpiration flow also influence its redistribution, especially in young tissues or under stress conditions (White \u0026amp; Broadley, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These differences highlight interspecific adaptive strategies to variable edaphic conditions, with \u003cem\u003eR. bucephalophorus\u003c/em\u003e acting as an efficient accumulator species, while \u003cem\u003eS. canina\u003c/em\u003e appears to exert stricter control over Ca uptake, likely as a protective response to adverse soil conditions, thereby avoiding associated toxicities and maintaining cellular ionic homeostasis (Wang \u0026amp; Schreiber, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the relationship between Sr concentrations in soil and plant tissues for \u003cem\u003eS. canina.\u003c/em\u003e The data reveal a moderately negative linear trend, described by the regression equation: y\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.485x\u0026thinsp;+\u0026thinsp;35.9 (R\u0026sup2; = 0.48), indicating that approximately 48% of the variation in plant Sr content can be explained by its concentration in the soil.\u003c/p\u003e\u003cp\u003eThis inverse relationship suggests the presence of a regulatory mechanism in Sr uptake; whereby increased soil availability does not result in a higher accumulation in plant tissues. This behaviour may be attributed to the chemical similarity between Sr and Ca, which can lead to competitive inhibition at the root uptake level, particularly in species adapted to low Ca environments (Gupta \u0026amp; Walther, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such mechanisms have been documented in various taxa, especially under radiostrontium contamination, where plants exhibit selective exclusion or compartmentalization strategies to mitigate potential toxicity (Chatterjee et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similar responses have also been observed under environmental stress or metal contamination, where selective exclusion mechanisms are activated to maintain ionic homeostasis (Ghori et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the relationships between total mercury (THg) concentrations in soils and plant tissues of the different studied species. Based on the observations, it is important to highlight that the San Quint\u0026iacute;n mining complex is characterized by significant local sources of Hg (e.g., ruins of the former ore-washing facility). In this area, atmospheric Hg (Hg\u003csup\u003e0\u003c/sup\u003e) has been reported as a relevant component of environmental pollution (Esbr\u0026iacute; et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), supporting the hypothesis that Hg content in plants may reflect atmospheric deposition (Stamenkovic \u0026amp; Gustin, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Barquero et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naharro et al., 2020). In the studied area, the soils emit variable concentrations of Hg\u003csup\u003e0\u003c/sup\u003e, depending on their proximity to the Hg wastes shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Therefore, it is proposed that Hg transfer to plants largely responds to atmospheric contamination originated from the soil compartment (Esbr\u0026iacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Barquero et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naharro et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn \u003cem\u003eR. bucephalophorus\u003c/em\u003e, a small-sized plant, a strong exponential relationship is observed (THg plant tissue\u0026thinsp;=\u0026thinsp;7.888 ln (THg soil) \u0026ndash; 32.30; R\u0026sup2; = 0.98). This suggests that proximity to the soil facilitates foliar uptake via air due to exposure to resuspended Hg\u003csup\u003e0\u003c/sup\u003e particles in the lower atmospheric layers. This logarithmic accumulation pattern reinforces the potential use of this species as a bioindicator in Hg contaminated environments. Differences in regression slopes among species may be partly explained by their life strategies: annual species (e.g., \u003cem\u003eR. bucephalophorus\u003c/em\u003e) accumulate Hg over a short vegetative cycle (logarithmic model), whereas biennial or perennial species (e.g., \u003cem\u003eH. incana\u003c/em\u003e or \u003cem\u003eS. canina\u003c/em\u003e) are exposed to Hg\u003csup\u003e0\u003c/sup\u003e over multiple years, resulting in accumulation patterns better described by linear models. This supports the hypothesis that plant Hg concentrations reflect a spatiotemporal integration of contaminant exposure rather than a direct response to soil content (Kyllmar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLeaf area is a critical factor influencing the absorption of Hg\u003csup\u003e0\u003c/sup\u003e. Plants with larger leaf surfaces exhibit a greater capacity for dry deposition and stomatal uptake of elemental Hg\u003csup\u003e0\u003c/sup\u003e (Laacouri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wohlgemuth et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although \u003cem\u003eQ. ilex\u003c/em\u003e and \u003cem\u003eR. sphaerocarpa\u003c/em\u003e show low correlations with soil Hg (R\u0026sup2; = 0.07 and 0.12, respectively), this does not preclude significant Hg accumulation. Their large stature facilitates diffuse atmospheric deposition, enhancing Hg⁰ uptake through stomata and cuticle, particularly during nighttime hours. In the case of \u003cem\u003eR. sphaerocarpa\u003c/em\u003e, its smaller leaves may limit direct uptake yet still intercept Hg\u003csup\u003e0\u003c/sup\u003e previously redistributed in the air. Although specific measurements of Hg in stems are lacking, literature suggests that root-shoot translocation in small plants is possible but rarely complete (e.g., Houttuynia; Greger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Laacouri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given the known Hg emission sources, the vegetative morphology of each species and their spatial relationship to these sources significantly contribute to explaining the Hg concentrations detected in plant tissues beyond simple a correlation with the soil content.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBioaccumulation and transfer of elements from soil to plant\u003c/p\u003e\u003cp\u003eThe bioaccumulation coefficients (BAC) obtained for the six plant species show notable variability among elements and species, reflecting physiological differences in their capacity to absorb and accumulate nutrients, common elements, and toxic metals. Potassium, a primary macronutrient essential for osmoregulation and enzymatic activation, exhibited the highest values in \u003cem\u003eS. rubra\u003c/em\u003e (2.05), followed by \u003cem\u003eH. incana\u003c/em\u003e (1.22), suggesting an efficient adaptation to K deficient soils. Regarding secondary macronutrients, Ca and S showed significant accumulation in \u003cem\u003eH. incana\u003c/em\u003e (6.12 and 10.69, respectively), possibly due to its belonging to the Brassicaceae family, known for its resilience in environments with elevated concentrations of heavy metals. The micronutrients Cu, Fe, Mn, and Zn, essential for redox and enzymatic processes, were more highly accumulated by \u003cem\u003eS. rubra\u003c/em\u003e (Mn: 0.80, Zn: 0.66) and \u003cem\u003eR. sphaerocarpa\u003c/em\u003e (Cu: 0.34, Zn: 0.41), which may be related to differential expression of metal transporters and the production of chelators such as phytochelatins (Alloway, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Juwarkar \u0026amp; Yadav, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The elements Sr and Rb, common but with no clear biological function, usually absorbed due to chemical similarity with Ca and K, were accumulated in higher proportions by \u003cem\u003eH. incana\u003c/em\u003e (Sr: 2.01) and \u003cem\u003eS. rubra\u003c/em\u003e (Rb: 0.72), suggesting a low ionic selectivity in these species.\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\u003eBioaccumulation Coefficient (BAC) for different elements analysed. Values of BAC\u0026thinsp;\u0026gt;\u0026thinsp;1 shown in bolds.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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\u003eK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuercus ilex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.68\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u0026ndash;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u0026ndash;4\u003cb\u003e.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u0026ndash;5\u003cb\u003e.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u0026ndash;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;1\u003cb\u003e.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u0026ndash;1\u003cb\u003e.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u0026ndash;1\u003cb\u003e.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.04\u0026ndash;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u0026ndash;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u0026ndash;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00\u0026ndash;0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetama sphaerocarpa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.35\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.42\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u0026ndash;1\u003cb\u003e.20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u0026ndash;5\u003cb\u003e.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u0026ndash;8\u003cb\u003e.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u0026ndash;1\u003cb\u003e.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026ndash;1\u003cb\u003e.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u0026ndash;2\u003cb\u003e.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.05\u0026ndash;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u0026ndash;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u0026ndash;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00\u0026ndash;0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScrophularia canina\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.49\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.16\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u0026ndash;1\u003cb\u003e.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u0026ndash;2\u003cb\u003e.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u0026ndash;6\u003cb\u003e.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u0026ndash;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026ndash;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u0026ndash;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.08\u0026ndash;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.04\u0026ndash;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.01\u0026ndash;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00\u0026ndash;0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHirschfeldia incana\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.22\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10.69\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.12\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e2.01\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u0026ndash;2\u003cb\u003e.11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.82\u0026ndash;18.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.06\u0026ndash;12.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u0026ndash;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;1\u003cb\u003e.70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u0026ndash;3\u003cb\u003e.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u0026ndash;1\u003cb\u003e.16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.54\u0026ndash;4\u003cb\u003e.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u0026ndash;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u0026ndash;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00\u0026ndash;0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpergularia rubra\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.05\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.69\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.06\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.26\u0026ndash;4\u003c/b\u003e.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u0026ndash;3\u003cb\u003e.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68\u0026ndash;7\u003cb\u003e.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u0026ndash;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.30\u0026ndash;2\u003cb\u003e.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u0026ndash;2\u003cb\u003e.70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u0026ndash;1\u003cb\u003e.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.05\u0026ndash;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.35\u0026ndash;1\u003cb\u003e.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.01\u0026ndash;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.02\u0026ndash;0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRumex bucephalophorus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u0026ndash;1\u003cb\u003e.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026ndash;2\u003cb\u003e.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u0026ndash;2\u003cb\u003e.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u0026ndash;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026ndash;2\u003cb\u003e.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u0026ndash;1\u003cb\u003e.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.07\u0026ndash;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u0026ndash;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.01\u0026ndash;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01\u0026ndash;0.08\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\u003eRegarding toxic elements, Hg and Pb, with lack biological function and highly toxic, generally showed low values, although \u003cem\u003eH. incana\u003c/em\u003e and \u003cem\u003eS. rubra\u003c/em\u003e presented relatively elevated Hg levels (0.27 and 0.12, respectively). This is not due to a deficiency in exclusion mechanisms but rather to the direct uptake of elemental Hg⁰, favoured by the low stature of these species and their proximity to the local soil, acting as a source of Hg emissions. Accumulation of Pb was more homogeneous, with \u003cem\u003eS. rubra\u003c/em\u003e standing out slightly (0.15). These results suggest that \u003cem\u003eH. incana\u003c/em\u003e and \u003cem\u003eS. rubra\u003c/em\u003e possess a broad accumulation profile, including macronutrients, micronutrients, and toxic elements, positioning them as potential candidates for phytoremediation strategies in contaminated soils (Nnaji et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ability of these species to accumulate multiple elements may be related to the expression of nonspecific transporters and the production of chelating compounds such as phytochelatins and metallothioneins, mechanisms widely documented in hyperaccumulator species (Alloway, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Juwarkar \u0026amp; Yadav, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe interspecific variability observed in BAC reflects evolutionary adaptations to specific environmental conditions, such as nutrient poor or heavy metal contaminated soils, as documented in plant families like Brassicaceae and Scrophulariaceae (Juwarkar \u0026amp; Yadav, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Alloway, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the case of \u003cem\u003eQ. ilex\u003c/em\u003e, a perennial woody species characteristic of Mediterranean ecosystems, a moderately low bioaccumulation pattern was observed compared to the herbaceous species analysed here. BAC values were below 1 for most elements, except for Ca (1.68) and S (1.21), indicating a limited accumulation capacity from the soil. This behaviour may be related to its deep root system and conservative nutrient strategy, typical of sclerophyllous species adapted to oligotrophic environments. Although \u003cem\u003eQ. ilex\u003c/em\u003e has been described by many authors as a Mn bioaccumulator in its tissues (Rod\u0026agrave; et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Arena et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barquero et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in this study it exhibited a relatively low BAC value (0.50). This may be attributed to the limited availability of Mn in acidic soils contaminated with iron. Under these conditions, Mn\u0026sup2;⁺ is easily oxidized to insoluble forms such as MnO₂, especially in the presence of Fe oxides, reducing its mobility and bioavailability to plants (Paterson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Robson, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Grangeon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As shown in section 3.3, the low height of herbaceous species near the ground may favour the uptake of Hg⁰, while \u003cem\u003eQ. ilex\u003c/em\u003e, being a tall species, has less direct exposure to these emissions, capturing only the diffuse concentrations present in the local atmosphere. Altogether, these factors reinforce the role of \u003cem\u003eQ. ilex\u003c/em\u003e as a resilient species with efficient regulatory mechanisms for element uptake, even in heavily anthropized environments, as demonstrated by Barquero et al, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe findings of this study confirm that the San Quint\u0026iacute;n abandoned mining area constitutes a highly contaminated environment, with elevated concentrations of PTEs in soils and mining residues. The detailed geochemical characterization revealed significant spatial heterogeneity in PTE distribution and speciation, reflecting the legacy of diverse mining activities and waste management practices over more than a century.\u003c/p\u003e\u003cp\u003eThe analysis of plant\u0026ndash;soil interactions demonstrated marked inter- and intraspecific variability in the uptake and bioaccumulation of elements such as Zn, Pb, Hg, and Cu. Notably, \u003cem\u003eSpergularia rubra\u003c/em\u003e and \u003cem\u003eRumex bucephalophorus\u003c/em\u003e exhibited high accumulation capacities, underscoring their potential as effective bioindicators and possible candidates for phytoremediation in metal-contaminated environments. These patterns were closely linked to both edaphic factors and species-specific physiological traits, highlighting the complexity of PTE transfer mechanisms.\u003c/p\u003e\u003cp\u003eA particularly relevant outcome of this study is the assessment of atmospheric deposition as a key pathway for mercury accumulation in plant tissues. The results indicate that Hg uptake is not solely governed by soil concentrations, but also by proximity to Hg discrete emission sources, plant stature, and leaf surface area\u0026mdash;factors that modulate exposure to gaseous elemental mercury (Hg⁰) in the local atmosphere.\u003c/p\u003e\u003cp\u003eOverall, this work advances our understanding of the multifactorial processes governing PTEs dynamics in degraded ecosystems. It provides a scientific basis for the development of ecological restoration strategies and environmental monitoring frameworks in decommissioned mining areas, with broader implications for risk assessment and land management in similarly affected regions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamo, P., Lavazzo, P., Albanese, S., Agrelli, D., De Vivo, B., \u0026amp; Lima, A. (2014). Bioavailability and soil-to-plant transfer factors as indicators of potentially toxic element contamination in agricultural soils. Science of the Total Environment, 500, 11\u0026ndash;22. https://doi.org/10.1016/j.scitotenv.2014.08.085 \u003c/li\u003e\n\u003cli\u003eAENOR (2001). UNE 77308:2001: Calidad del suelo. Determinaci\u0026oacute;n de la conductividad el\u0026eacute;ctrica espec\u0026iacute;fica. https://tienda.aenor.com/norma-une-77308-2001-n0024111. (last access June 2024).\u003c/li\u003e\n\u003cli\u003eAENOR (2012). UNE-ISO 10390:2012: Calidad del suelo. Determinaci\u0026oacute;n del pH. https://tienda.aenor.com/norma-astm-d4972-01-2007-056326,. (last access June 2024).\u003c/li\u003e\n\u003cli\u003eAlloway, B.J. (2009). Soil factors associated with zinc deficiency in crops and humans. Environmental Geochemistry and Health, 31(5), 537\u0026ndash;548. https://doi.org/10.1007/s10653-009-9255-4 \u003c/li\u003e\n\u003cli\u003eAlloway, B.J. (2013). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability (3rd ed.). Springer. https://doi.org/10.1007/978-94-007-4470-7 \u003c/li\u003e\n\u003cli\u003eArena, C., De Maio, A., De Nicola, F., Santorufo, L., Vitale, L., \u0026amp; Maisto, G. (2014). Assessment of eco-physiological performance of Quercus ilex L. leaves in urban area by an integrated approach. 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Gupta \u0026amp; C. Walther (Eds.), Strontium contamination in the environment (pp. 85\u0026ndash;97). Springer. https://doi.org/10.1007/978-3-030-15314-4_5 \u003c/li\u003e\n\u003cli\u003eEsbr\u0026iacute;, J. M., Mart\u0026iacute;n-Crespo, T., G\u0026oacute;mez-Ortiz, D., Monescillo, C. I., Lorenzo, S., \u0026amp; Higueras, P. (2010, May). Mercury dispersion in soils of an abandoned lead-zinc-silver mine, San Quint\u0026iacute;n (Spain). EGU General Assembly 2010, Abstract EGU2010-14998. European Geosciences Union. https://ui.adsabs.harvard.edu/abs/2010EGUGA..1214998E \u003c/li\u003e\n\u003cli\u003eEsbr\u0026iacute;, J.M., Mart\u0026iacute;nez-Coronado, A., \u0026amp; Higueras, P. (2016). 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M., Roque\u0026ntilde;\u0026iacute;, N., Fontolan, G., Flor-Blanco, G., Cienfuegos, P., \u0026amp; Loredo, J. (2018). Occurrence and speciation of arsenic and mercury in estuarine sediments affected by mining activities (Asturias, northern Spain). Chemosphere, 198, 281\u0026ndash;289. https://doi.org/10.1016/j.chemosphere.2018.01.146 \u003c/li\u003e\n\u003cli\u003eGhori, N.H., Ghori, T., Hayat, M.Q., Imadi, S.R., Gul, A., Altay, V., \u0026amp; Ozturk, M. (2019). Heavy metal stress and responses in plants. International Journal of Environmental Science and Technology, 16(3), 1807\u0026ndash;1828. https://doi.org/10.1007/s13762-019-02215-8 \u003c/li\u003e\n\u003cli\u003eGrangeon, S., Bataillard, P., \u0026amp; Coussy, S. (2020). The nature of manganese oxides in soils and their role as scavengers of trace elements: Implication for soil remediation. In Environmental Soil Remediation and Rehabilitation (pp. 399\u0026ndash;429). Springer. https://doi.org/10.1007/978-3-030-40348-5_7 \u003c/li\u003e\n\u003cli\u003eGreger, M., L\u0026ouml;fstedt, C., \u0026amp; \u0026Ouml;born, I. (2005). Mercury uptake, translocation and accumulation in plants. Environmental Pollution, 138(1), 109\u0026ndash;117. https://doi.org/10.1016/j.envpol.2005.02.016 \u003c/li\u003e\n\u003cli\u003eGroves, R.H. (1983). Nutrient cycling in Australian heath and South African fynbos. In F. J. Kruger, D. T. Mitchell, \u0026amp; J. U. M. Jarvis (Eds.), Mediterranean-Type Ecosystems: The Role of Nutrients (pp. 179\u0026ndash;191). Springer. https://doi.org/10.1007/978-3-642-70868-8_22 \u003c/li\u003e\n\u003cli\u003eGruszecka‑Kosowska, A. (2019). Human health risk assessment and potentially harmful element contents in the fruits cultivated in Southern Poland. International Journal of Environmental Research and Public Health, 16(24), 5096. https://doi.org/10.3390/ijerph16245096 \u003c/li\u003e\n\u003cli\u003eGupta, D.K., \u0026amp; Walther, C. (Eds.). (2018). Behaviour of Strontium in Plants and the Environment. Springer International Publishing. https://doi.org/10.1007/978-3-319-66574-0 \u003c/li\u003e\n\u003cli\u003eHarries. J.R., Ritchie. A.I.M. (1987) The effect of rehabilitation on the rate of oxidation of pyrite in a mine waste rock dump. Environ. Geochem. Health 9, 27\u0026ndash;36 https://doi.org/10.1007/BF01686172\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez, A., J\u0026eacute;brak, M., Higueras, P., Oyarzun, R., Morata, D., Munh\u0026aacute;, J. (1999). The Almad\u0026eacute;n mercury mining district, Spain. Mineralium Deposita, 34: 539-548. https://doi.org/10.1007/s001260050219 \u003c/li\u003e\n\u003cli\u003eHigueras, P., Esbr\u0026iacute;, J.M., Garc\u0026iacute;a-Ordiales, E., Alonso-Azc\u0026aacute;rate, J., \u0026amp; Mart\u0026iacute;nez-Coronado, A. (2017). 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ISBN: 9780123849052. https://doi.org/10.1016/B978-0-12-384905-2.00001-7 \u003c/li\u003e\n\u003cli\u003eKisku, G.C., Barman, S.C., Bhargava, S.K. (2000). Contamination of soil and plants with potentially toxic elements irrigated with mixed industrial effluent and its impact on the environment. Water Air and Soil Pollution, 120(1-2), pp. 121\u0026ndash;137 https://doi.org/10.1023/A:1005202304584 \u003c/li\u003e\n\u003cli\u003eKruger, F. J. (1983). Responses of plants to nutrient supply in Mediterranean-type ecosystems. In F. J. Kruger, D. T. Mitchell, \u0026amp; J. U. M. Jarvis (Eds.), Mediterranean-Type Ecosystems: The Role of Nutrients (pp. 415\u0026ndash;427). Springer. https://doi.org/10.1007/978-3-642-70868-8_26 \u003c/li\u003e\n\u003cli\u003eKyllmar, K., Carlsson, C., Gustafsson, D., \u0026amp; Ul\u0026eacute;n, B. (2011). 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Bioaccumulation for heavy metal removal: A review. \u003cem\u003eSN Applied Sciences, 5\u003c/em\u003e(125). https://doi.org/10.1007/s42452-023-05351-6 \u003c/li\u003e\n\u003cli\u003ePalero. F.J. (1991). Evoluci\u0026oacute;n geotect\u0026oacute;nica y yacimientos minerales de la regi\u0026oacute;n del Valle de Alcudia (sector meridional de la Zona Centro Ib\u0026eacute;rica). Tesis Doctoral. Universidad de Salamanca. 827 pp.\u003c/li\u003e\n\u003cli\u003ePalero, F.J., Both, R., Arribas, A., Mangas, J., \u0026amp; Mart\u0026iacute;n-Izard, A. (2003). Geology and metallogenic evolution of the polymetallic deposits of the Alcudia Valley mineral field, Eastern Sierra Morena, Spain. Economic Geology, 98(3), 577\u0026ndash;605. https://doi.org/10.2113/gsecongeo.98.3.577 \u003c/li\u003e\n\u003cli\u003ePaterson, E., Goodman, B.A., \u0026amp; Farmer, V.C. (1985). The chemistry of aluminium, iron and manganese oxides in acid soils. In Soil Acidity (pp. 97\u0026ndash;124). Springer. https://doi.org/10.1007/978-3-642-74442-6_5 \u003c/li\u003e\n\u003cli\u003ePathak, R.K., Singh, D.B., Sharma, H., \u0026amp; Pandey, D. (2021). Calcium uptake and translocation in plants. En \u003cem\u003eCalcium Transport Elements in Plants\u003c/em\u003e (pp. 373\u0026ndash;386). Elsevier. https://doi.org/10.1016/B978-0-12-821792-4.00018-7 \u003c/li\u003e\n\u003cli\u003ePeco, J.D., Higueras, P., Campos, J.A., Esbr\u0026iacute;, J.M., Moreno, M.M., Battaglia-Brunet, F., \u0026amp; Sandalio, L.M. (2021). Abandoned mine lands reclamation by plant remediation technologies. Sustainability, 13(12), 6555. https://doi.org/10.3390/su13126555 \u003c/li\u003e\n\u003cli\u003ePeco, J.D., Thouin, H., Esbr\u0026iacute;, J.M., Campos-Rodr\u0026iacute;guez, H.R., Garc\u0026iacute;a-Noguero, E.M., Breeze, D., Villena, J., Gloaguen, E., Higueras, P.L., \u0026amp; Battaglia-Brunet, F. (2023). 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Springer. https://doi.org/10.1007/978-3-642-70868-8_23 \u003c/li\u003e\n\u003cli\u003eRobson, A.D. (1988). Manganese in soils and plants\u0026mdash;An overview. In R. D. Graham, R. J. Hannam, \u0026amp; N. C. Uren (Eds.), Manganese in Soils and Plants (pp. 329\u0026ndash;333). Springer. https://doi.org/10.1007/978-94-009-2817-6_22 \u003c/li\u003e\n\u003cli\u003eRod\u0026agrave;, F., Retana, J., Gracia, C.A., \u0026amp; Bellot, J. (Eds.). (1999). Quercus ilex L. ecosystems: function, dynamics and management. Springer. https://doi.org/10.1007/978-94-017-2836-2 \u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez, L., Ruiz, E., Alonso-Azc\u0026aacute;rate, J., \u0026amp; Rinc\u0026oacute;n, J. (2009). Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain. 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Fuel Processing Technology, 85(6\u0026ndash;7), 473\u0026ndash;485. https://doi.org/10.1016/j.fuproc.2003.11.003 \u003c/li\u003e\n\u003cli\u003eStamenkovic, J., Gustin, M.S. 2009 Nonstomatal versus stomatal uptake of atmospheric mercury. Environmental Science and Technology 43(5), pp. 1367-1372. https://doi.org/10.1021/es801583a \u003c/li\u003e\n\u003cli\u003eThalassinos, G., Petropoulos, S. A., Grammenou, A., \u0026amp; Antoniadis, V. (2023). Potentially toxic elements: A review on their soil behavior and plant attenuation mechanisms against their toxicity. Agriculture, 13(9), 1684. https://doi.org/10.3390/agriculture13091684 \u003c/li\u003e\n\u003cli\u003eUdden-Wentworth, C.K. A scale of grade and class terms of clastic sediments. J. Geol. 1922, 30, 377\u0026ndash;392.\u003c/li\u003e\n\u003cli\u003eWalkley. A. and Black. I.A. (1934) An Examination of the Degtjareff Method for Determining Soil Organic Matter and a Proposed Modification of the Chromic Acid Titration Method. Soil Science 37, 29-38. http://dx.doi.org/10.1097/00010694-193401000-00003\u003c/li\u003e\n\u003cli\u003eWang, Y., \u0026amp; Schreiber, S.L. (2024). Mechanisms of calcium homeostasis orchestrate plant growth and immunity. \u003cem\u003eNature\u003c/em\u003e, 619, 120\u0026ndash;130. https://doi.org/10.1038/s41586-024-07100-0 \u003c/li\u003e\n\u003cli\u003eWhite, P.J., \u0026amp; Broadley, M.R. (2003). Calcium in plants. \u003cem\u003eAnnals of Botany\u003c/em\u003e, 92(4), 487\u0026ndash;511. https://doi.org/10.1093/aob/mcg164 \u003c/li\u003e\n\u003cli\u003eWohlgemuth, D., Bahlmann, E., \u0026amp; Kesselmeier, J. (2021). Mercury uptake by leaves: A comparison of different plant species and morphologies. Biogeosciences, 18, 6313\u0026ndash;6328. https://doi.org/10.5194/bg-18-6313-2021 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Potentially toxic elements (PTEs), Soil–plant transfer, Bioaccumulation, Abandoned mining areas, San Quintín mine","lastPublishedDoi":"10.21203/rs.3.rs-6991303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6991303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and aims: Abandoned mining areas represent critical environmental pollution hotspots due to the persistence of waste materials enriched in potentially toxic elements (PTEs). This study evaluates the transfer of PTEs from contaminated soils to six plant species in the vicinity of the San Quintín Pb-Zn mine (Ciudad Real, Spain), a site impacted by over a century of mining activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: The studied species include the tree \u003cem\u003eQuercus ilex\u003c/em\u003e, the shrubs \u003cem\u003eRetama sphaerocarpa\u003c/em\u003e and \u003cem\u003eScrophularia canina\u003c/em\u003e, and the annual herbaceous species \u003cem\u003eSpergularia rubra\u003c/em\u003e, \u003cem\u003eRumex bucephalophorus\u003c/em\u003e, and\u003cem\u003e Hirschfeldia incana\u003c/em\u003e. Soil and plant tissue samples were analysed using X-ray fluorescence and atomic absorption spectrometry to determine concentrations of Zn, Pb, Hg, Cu, and other PTEs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Results revealed a high heterogeneity in the bioaccumulation of elements such as Zn, Pb, Hg, and Cu among the studied species, with \u003cem\u003eSpergularia rubra\u003c/em\u003e and \u003cem\u003eRumex bucephalophorus\u003c/em\u003e emerging as effective bioindicators of soil contamination. Specific correlations between soil and plant concentrations were identified, and atmospheric uptake was found to significantly influence Hg accumulation in plant tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: This work enhances our understanding of plant uptake mechanisms in contaminated environments and provides a foundation for ecological restoration and environmental monitoring strategies in decommissioned mining areas, emphasizing the role of both edaphic properties and species-specific physiological adaptations.\u003c/p\u003e","manuscriptTitle":"Transference of potentially toxic elements from soils to plants in a derelict Pb-Zn mining area (San Quintín mine, South-Central Spain)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 07:42:09","doi":"10.21203/rs.3.rs-6991303/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14ed0aa9-75e4-43df-ac30-274c87d9b037","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-17T14:49:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-07 07:42:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6991303","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6991303","identity":"rs-6991303","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}