Evaluation of the Phytoremediation Potential of the Sinapis alba Plant Using Extractable Metal Concentrations

Testing the feasibility of soil phytoremediation requires the development of models applicable on a large scale. Phytoremediation mechanisms include advanced rhizosphere biodegradation, phytoaccumulation, phytodegradation, and phytostabilization. The aim of this study was to evaluate the phytoremediation potential of the Sinapis alba. Identification of the factors influencing the extraction process of metals from contaminated soils in a laboratory system suitable for evaluating the phytoavailability of these metals in three solutions (M1-CaCl2, M2-DTPA, and M3-EDTA) included the following: distribution of metals in solution (Kd), soil properties and mobile fractions (SOC, CEC, pH), response surface methodology (RSM), and principal component analysis (PCA). The evaluation of the phytoremediation potential of the Sinapis alba plant was assessed using bioaccumulation coefficients (BACs). The accumulation of heavy metals in plants corresponds to the concentrations and soluble fractions of metals in the soil. Understanding the extractable metal fractions and the availability of metals in the soil is important for soil management. Extractable soluble fractions may be more advantageous in total metal content as a predictor of bioconcentrations of metals in plants. In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant. Multiple regression prediction models have been developed for estimating the amounts of As and Cd in plant organs. The performance of the predictive models generated based on the experimental data was evaluated by the adjusted coefficient of determination (aR2), model efficiency (RMSE), Durbin–Watson (DW) test, and Shapiro–Wilk (SW) test. The accumulation of the analyzed metals followed the pattern Root > Pods > Leaves > Seeds, stems > Flowers for As and Leaves > Root > Stem > Pods > Seeds > Flowers for Cd in soil contaminated with different metal concentrations. The obtained results showed a phytoremediation potential of the Sinapis alba plant.


Introduction
Plants are recognized for their ability to absorb minerals from the soil, but alongside the beneficial ones, there are also heavy metals.The development of the metal and oil extraction industry, the use of pesticides, and environmental pollution constitute, however, a danger regarding the safety of consuming plants from uncontrolled environments.Metals persist in the soil for a long time, having the ability to be transferred in the food chain [1,2], and the evaluation of their content in the soil and the estimation of their transfer rates to the vegetation present great interest [3].The type of metals present and their concentration decide if contaminated soil needs to be treated.Sources of harmful metal pollution must greatly increase the amount of soil metals available to plants before they can be recognized.The solubility of the metal linked with the solid phase has the most impact on how easily dangerous metals are absorbed by plants in the soil [4][5][6].The absorption, mobility, and toxicity of the metal to plants and animals that consume it are significantly determined by the degree of dispersion of soluble species.Factors influencing the solubility and distribution of chemically available metal species in soil influence the kinetics of sorptiondesorption reactions, the metal concentration in the extraction solution, and the form of soluble or insoluble chemical species [7,8].An accurate estimate of the phytoavailability of metals in soil and solid waste is becoming an increasingly serious risk.The evaluation of metal concentrations and their distribution can be associated with the understanding of the phytoavailability process of the metal.These attributes can be evaluated from the soil matrix using analytical techniques (spectroscopy) or approaches that address single chemical extractions of the metals.Biological instruments supply information regarding the direct entry of the contaminant into the plant, including measuring the plant's response to its toxicity.
Predicting the phytoavailability of metals in natural or accidentally polluted soils with hazardous metals is relatively difficult mostly due to the variety of soil types.The main factor determining the phytoavailability of dangerous metals in soil is the solubility of the metal paired with the solid phase [9].The rate of dissolution of soluble species substantially impacts the amounts of absorption, mobility, and toxicity of the metal in plants.Factors influencing the dissolution and distribution of chemically soluble metal species in soil include soil characteristics (metal concentration, mineralogy, particle size distribution) and soil processes [10].They influence the kinetics of sorption-desorption reactions, the metal concentration in the extraction solution, and the form of soluble or insoluble chemical species [11].The assessment and prediction of toxic metals in soil requires knowledge of their distribution in the solid-liquid system, expressed by the distribution coefficient Kd [12][13][14][15].The Kd value of metal indicates the effect of various reactions in the solid and liquid phases.Kd values based on the soluble phase are often correlated with soil properties.Due to the fact that part of the metal in the solid phase is quickly exchanged with the solution phase, different extraction techniques were used to quantify the mobile phase.Phytoavailability of metals is estimated by different chemical extraction methods such as neutral salts, weak acids, organic extraction, and resin-based techniques, all with varying success [16][17][18].Systematic studies on the distribution of toxic metals in contaminated soil samples were carried out using different extraction solutions, including EDTA [19][20][21][22].Other studies have tried quantitative prediction of phytoavailability using extraction techniques [23][24][25][26][27][28][29].
In this study, an analysis of the extraction capability of several extraction procedures was utilized to predict the phytoavailability of the metals As and Cd in contaminated soils.Sinapis alba was selected to investigate the accumulation potential of As and Cd due to its capacity to retain several potentially hazardous elements [30].
The objective of this study was to analyze the potential of the Sinapis alba plant for phytoremediation of the As and Cd from polluted soils of the plant and to develop prediction models for predicting the metal concentrations of this plant using the extractable metal concentration.

Characterization of Soils and Metallic Mobile Fractions
To understand the transport and bioavailability of extractable metals, we performed a detailed experimental study on the determination of the extractability of some solutions using the distribution coefficients Kd for As and Cd in contaminated soil samples using the laboratory batch method.Two crop soil samples taken from different areas (Soil1, Soil2) were first mixed with amendments to improve physical properties such as filterability, particle structure, and water infiltration necessary for optimal plant growth and then enriched with different concentrations of As, Cd, and Ni.Ni concentration was added in Plants 2023, 12, 3123 3 of 24 order to simulate multiple contaminations.The mobility process of Ni in the soil was not analyzed in the present study.The main characteristics of the initial soil samples are shown in Table 1.Mean value (n = 4) ± SD (standard deviation); Ntot-total nitrogen; Ptot-total phosphorus; SOC-soil organic carbon.
In this study, the spiking method was used to obtain soil samples contaminated with different concentrations of As, Cd, and Ni.The total concentrations of As and Cd in the artificially contaminated soil samples are presented in Table 2 for Soil 1 and Soil 2. Metal analysis was performed with the ICP-EOS method according to the international standards.
The mobility of As and Cd metals in contaminated soils was investigated using three types of extraction solutions.
The extracting solutions used in the experiment were as follows: The affinity of metals to the soil surface was also reflected by the distribution coefficient (Kd), which shows the ability of soil to retain the metal in the solid phase or release it into the extraction solution.Table 3 shows the values of As concentrations and the values of the Kd of As in all studied extraction solutions, while the values of Cd concentrations and associated Kd values in the studied extraction solutions are presented in Table 4.

Factors Influencing the Phytoavailability of Metals in Soils Contaminated with As and Cd
Soil pH strongly affects the speciation and mobility of metals in both soil and soil solution.The pH values of the extraction solutions are presented in Table 5. CEC is used as an indicator of soil nutrient retention capacity.The organic carbon in the soil is a natural or anthropogenic dynamic component, and due to the decomposition of the organic matter in the soil, the impact it has on it is significant.In order to understand the complex relationship between the characteristic parameters of the soil and the distribution of the metal in the extraction solution, principal component analysis (PCA) was used.This technique reflects how much each variable contributes to the correlation of the data and the interpretation of the relationship between the variables [31].The PCA analysis generated two main components.To evaluate each variable and how it affects the response variable Kd, the response surface methodology (RSM) was used.The response surface and contour plots of KdAs and KdCd were generated based on the obtained results [32]. Figure 1    The values of the principal components after Varimax rotation for As extraction solutions (M2, M2, M3) are presented for S3 in Table 7, for S4 in Table 8, for S5 in Table 9, and for S6 in Table 10.The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of increasing the contaminant dose to 25 mg/kg As (S4) are presented in Figure 2.
The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15mg/kg and Cd 3 mg/kg (S5) are presented in Figure 3. Figure 4 shows the response surface and contour plots of the KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg, Cd 3 mg/kg, and Ni 10 mg/kg (S6).
In the same way, we evaluated the evolution of the Cd desorption process in the three types of extraction solutions in the representative soils of this study.
The values of the principal components after Varimax rotation for Cd extraction solutions (M2, M2, M3) are presented for S1 in Table 11, for S2 in Table 12, for S5 in Table 13, and for S6 in Table 14.In the same way, we evaluated the evolution of the Cd desorption process in the three types of extraction solutions in the representative soils of this study.
The values of the principal components after Varimax rotation for Cd extraction solutions (M2, M2, M3) are presented for S1 in Table 11, for S2 in Table 12, for S5 in Table 13, and for S6 in Table 14.
Figure 5 shows the response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of a single contamination with Cd 2 mg/kg (S1).

Evaluation of the Extraction Capacity of As and Cd from Contaminated Soils in M1-CaCl2, M2-DTPA, and M3-EDTA Solution Comparison with Water Extraction
The diversity of influencing factors of the sorption/desorption process of toxic metals in soils can make it difficult to derive some reference values of Kd distribution coefficients of toxic metals to be used as reliable indicators for evaluating the remediation process of contaminated soils.A constant update of the Kd values of heavy metals may be necessary for a thorough understanding of the influence of different factors on these processes and the determination of the extraction capacity of the solutions.Figures 9 and 10 show the

Evaluation of the Extraction Capacity of As and Cd from Contaminated Soils in M1-CaCl2, M2-DTPA, and M3-EDTA Solution Comparison with Water Extraction
The diversity of influencing factors of the sorption/desorption process of toxic metals in soils can make it difficult to derive some reference values of Kd distribution coefficients of toxic metals to be used as reliable indicators for evaluating the remediation process of contaminated soils.A constant update of the Kd values of heavy metals may be necessary for a thorough understanding of the influence of different factors on these processes and the determination of the extraction capacity of the solutions.Figures 9 and 10 show the variation in the distribution coefficient Kd in the three extractive solutions, M1-CaCl2, M2-DTPA, and M3-EDTA for As and Cd, respectively, in soils contaminated with different concentrations of metals.

Evaluation of the Accumulation Potential of As and Cd in the Sinapis alba Plant
The bioaccumulation coefficient (BAC) was found as the ratio of the metal concentration in the plant organs to the metal concentration in the soil.BAC shows the ability of plants to retain toxic metals.The results obtained from the analysis of As and Cd concentrations in the parts of Sinapis alba grown in the greenhouse in soils contaminated with different metal concentrations (As, Cd, and Ni), as well as the accumulated metal concentrations in the plant, are presented in Table 15.The BAC-Cd and BAC-As values determined in the root (R), stem (St), leaves (L), flowers (F), pods (P), and seeds (Se) of Sinapis alba grown in soils contaminated with As, Cd, and Ni (S5 and S6) and the metal accumulation pattern in the plant are shown in Table 16.

Evaluation of the Accumulation Potential of As and Cd in the Sinapis alba Plant
The bioaccumulation coefficient (BAC) was found as the ratio of the metal concentration in the plant organs to the metal concentration in the soil.BAC shows the ability of plants to retain toxic metals.The results obtained from the analysis of As and Cd concentrations in the parts of Sinapis alba grown in the greenhouse in soils contaminated with different metal concentrations (As, Cd, and Ni), as well as the accumulated metal concentrations in the plant, are presented in Table 15.The BAC-Cd and BAC-As values determined in the root (R), stem (St), leaves (L), flowers (F), pods (P), and seeds (Se) of Sinapis alba grown in soils contaminated with As, Cd, and Ni (S5 and S6) and the metal accumulation pattern in the plant are shown in Table 16.
Mean value (n = 4) ± SD.PCA analysis was used to statistically identify the relationship between the extractable metal concentration as values of the KdAs and KdCd distribution coefficients obtained in the solution with the highest effectiveness for these metals (M1), the metal concentrations in the plant organs, and the metal concentration in the whole plant.The PCA analysis was applied to the soil contaminated with As and Cd (S5).The obtained results are presented in Table 17

Prediction Models of As and Cd Concentrations in the Sinapis alba
In this study, multiple regression models for predicting the concentration of As and Cd in the organs of the plant Sinapis alba grown in the laboratory greenhouse were developed using the extractable metal concentrations evaluated by the distribution coefficients KdAs and KdCd in the analyzed solutions and the accumulation of the concentration of metal in the plant.The equations of the prediction model, the average experimental and predictive values of the metal in the plant organs, and the values of the parameters used in the validation process of the multiple regression model (aR 2 , DW, SW, RMSE) are presented in Table 18 for As and in Table 19 for Cd (N is the number of observations and k is the number of variables)

Evaluation of the Influence of Factors on the Extraction of As and Cd from Contaminated Soils Using PCA and RSM
The sorption process is associated with processes such as ion exchange, precipitation, adsorption, and complexation by which dissolved toxic metal ions can bind to the soil causing metal accumulation.Desorption includes the extraction of metal ions absorbed from the soil into the soil-solution system, spreading the contamination over a large surface [33].The extraction capacity of the three extraction solutions (M1-CaCl 2 , M2-DTPA, and M3-EDTA) was evaluated using the extractable As and Cd concentrations as values of the distribution of the metals analyzed in the solution phase, KdAs and KdCd.EDTA and DTPA are two chelating agents commonly used in sorption-desorption studies due to their ability to form stable complexes with a wide variety of metals.The nonselective nature of EDTA in metal extraction is a disadvantage, as this agent extracts a wide variety of metals, including alkaline earth cations such as Ca and Mg.Another disadvantage would be that EDTA is hardly biodegradable and can remain adsorbed on soil particles [34,35].With calcium being the primary cation of the adsorption complex of soils, the M2-CaCl 2 solution is more capable of extracting other cations adsorbed on the surface of soil particles than other solutions without Ca.Due to CaCl 2 , the Ca-DTPA complex facilitates ion exchange by forming complex combinations with various forms of toxic metals [36].
The evaluation of the factors that can influence the desorption process of As and Cd in the three extracting solutions was determined using PCA and the response surface plots of KdAs and KdCd.The experimental data obtained proposed the correct evaluation of the concentrations of toxic metals in soils with a high level of contamination.The aqueous extracts were not used in this study, because the water used as an extraction solution extracts soluble forms of the chemical species of As and Cd that do not reflect the real level of contamination of the analyzed soils.The experiment focused on identifying an extractant (for As and Cd) designed to ensure the maximum efficiency of the desorption process in contaminated soils, similar to other studies carried out [37].
Soil pH is an important factor influencing the availability of metals in the soil.Previous studies have established a relationship between soil pH and metal availability [32].The desorption of metals from the soil particle structure is also favored by the low pH value of the extraction solution.Various studies confirm the dependence of metal mobility on pH value [38].The cation exchange capacity (CEC) of the soil represents the quantity of cations that can be retained on the clay-humic adsorption complex of the soil, at a certain pH.CEC is used as a measure of soil fertility, indicating the ability to retain nutrients in the soil.The richer the soil in clay and organic matter, the more important the CEC is [39].The cation exchange capacity is related to the sum of the exchangeable bases and can be calculated empirically by summing the basic exchangeable cations (Ca 2+ , Mg 2+ , Na + , K + ) and the acidic cations (H + , Al 3+ , NH 4 + ) that are attracted to the negative charges on the particle surface of the soil [40].The mobility of As, tested on different types of clay soils, indicated weak surface retention of minerals in the composition of these types of soils [36].Data from literature studies show that Cd desorption increases at pH ≈ 2, with Cd uptake being negatively correlated with pH value [41].Of particular importance, in the context of finding metal concentrations, is the retention potential of metals in the surface layer of soils.This has important implications for the mobility of metals in the soil profile (depth) and the bioavailability of metals to organisms living in the surface layer and plants.Crop soils generally have low SOC concentrations.Clay soil types generally retain more organic matter than sandy soils and therefore more organic carbon [42].Changes in stable SOC usually occur very slowly (over decades) and, therefore, changes in the organic carbon content of agricultural soils are small, being most often determined in the upper layers.Due to its stability over time, SOC can be an important factor influencing the actual sorption/desorption processes of different toxic metals in soil [43,44].
Numerous studies have shown the influence of pH on the behavior of Cd and As mobility.In the aqueous environment, Cd 2+ shows relative mobility, with the process depending on the pH, the presence of organic molecules, and the hardness of the water.The concentration of Cd in the soil can reach high levels of total concentrations, but the desorption of Cd 2+ ions and its absorption by plants is supported by an increased acidity [45].
The study of As sorption/desorption processes in soil indicated an increase in the desorption process at higher concentrations explained by the non-selective sorption to the surface of soil particles of specific As chemical species.Similar studies showed the influence of pH in increasing the mobility of high concentrations of As due to its association with Fe ions [46].
The sorption/desorption distribution coefficient (Kd) is an important parameter frequently studied to understand the mobility of a compound in the environment and its distribution between water, sludge, soil, and sediment compartments.In addition, Kd is an essential parameter for evaluating the bioavailability and leaching processes; therefore, it is directly related to distribution coefficients.The desorption process involves selecting a distribution coefficient with the lowest value (high mobility) [47].The characterization of Kd in the laboratory system and the extrapolation in the real environment can be difficult, mostly due to the complexity of the mechanisms involved in the sorption/desorption process [43].
PCA was applied to identify the variability of factors that influence the desorption process of As and Cd in the soluble phase and which can influence a correct assessment of metal concentrations in contaminated soils.The multivariate data set consisted of five variables: two basic characteristic parameters of the soil (SOC, metal concentration in the soil) and three characteristic parameters of the extraction solutions (CEC, pH, and Kd).In order to eliminate errors due to non-correlation and maximize the variance of the components of the two factors, the correlation matrix extracted by PCA was subjected to orthogonal Varimax rotation.The significant factors obtained after Varimax rotation and loadings indicate how each parameter is related to these factors [48,49].
The next applied step included the application of RSM for the simultaneous evaluation of the relationship between the effects of the parameters involved in the sorptiondesorption process of mobile As and Cd species [50].The data obtained were aimed at evaluating the extraction capacity of the solutions used in this study.The response surface and contour plots of the distribution coefficients KdAs and KdCd were used to follow the evolution of the extraction process and of the interdependence reactions determined by the variability of the characteristic parameters of the soil and of the extraction solutions.Rapid evolution of the interaction effects is shown by the close-curved lines in the contour plot [45].

The Desorption Process of As in Contaminated Soils
In the case of S3 soil samples contaminated with As, the results show that the metal extraction from the soil is moderately influenced by the SOC variation (PC1_M1: +0.642; PC1_M2: −0.475; PC2_M3: −0.720) and the CEC variation for M3.Increasing the contamination level reduces the extractable metal concentration in solutions M1 and M2 (Table 7).The extraction of As from the S4 soil is strongly positively influenced by the CEC variability in the M1 and M3 solutions (PC2_M1: −0.916 and PC2_M3: 0.733) and by a low concentration of SOC in the soil for the M2 solution.Increasing the concentration of As in the soil decreases the efficiency of the metal desorption process in the solution (Table 8).As concerns desorption from soil contaminated with As and Cd, S5 is favored by low pH values in solutions M1 and M2 (PC1_M1: 0.598; PC2_M2: −0.896) and by CEC variability in solution M3 (PC2_M3: 0.800).The metal concentration in the soil has a negative influence on the extraction of As in the analyzed solutions (Table 9).The pH was the factor that moderately influenced the extraction of As from soil contaminated with As, Cd, and Ni (S6) in the three solutions (PC2_M1: 0.634; PC2_M2: −0.540; PC2_M3: −0.545).Low pH values favor the desorption of As in solutions M1 and M3.Another factor with moderate variability was SOC for solutions M1 and M2 and CEC for solution M3 (Table 10).
The response surface and contour plots of KdAs presented that the As sorption/desorption process is accompanied by interactions between the parameters determined as variable factors that influence the extraction of the metal from the S3 soil (Figure 1a-c) for all three extraction solutions.The increase in the level of contamination in the S4 soil slightly stabilizes the evolution of the desorption process, but at concentrations of As higher than 26 mg/kg in the soil, the metal is retained in the solid fraction (Figure 2a-c).The extraction of As from soils S5 and S6 (Figures 3 and 4a-c) indicated a reduction in the metal desorption process in the extraction solutions, also confirmed by the increase in KdAs.

The Desorption Process of Cd in Contaminated Soils
The results obtained in the case of soil sample S1 contaminated with Cd show that the extraction of the metal from the soil is strongly influenced by the variability of the pH in solution M1 (PC2_M1: −0.798), CEC in solutions M2 and M3 (PC2_M2: −0.891; PC2_M3: −0.892), and low values of metal concentrations in soil (Table 11).The extraction of Cd from the S2 soil in the M1 solution is influenced by the variability of the metal concentration and SOC in the soil (PC2_M1: −0.695).In the M2 solution, the variable factors that can influence the Cd desorption process are the CEC of the extraction solution (PC2_M2: −0.696) and the reduced level of contamination.In solution M3, Cd extraction is moderately influenced by pH and SOC (Table 12).The desorption of Cd from the soil contaminated with As and Cd (S5) is favored by the low values of the metal concentration in the soil and by CEC in the M2 and M3 solutions.For solution M1, low pH values favor the desorption process in the extraction solution (Table 13).The data obtained in the case of soil contaminated with As, Cd, and Ni (S6) show a strong influence of pH (PC1_M1: −0.972) and CEC (PC2_M1: −0.958) in the extraction process of Cd in solution M1.The extraction of Cd in the M2 solution is influenced by the pH variation (PC1_M2: 0.904) and the metal concentration in the soil (PC2_M2: −0.923).The extraction of Cd in solution M3 is favored by low concentrations of CEC (PC2_M3: −0.914) and the contamination level (PC1_M3: −0.926) of the soil (Table 13).
The response surface and contour plots of KdCd showed a slow evolution of the Cd desorption process in S1 soil (Figure 5a-c) for all three extraction solutions.Increasing the concentration of Cd to 5 mg/kg in soil S2 (Figure 6a-c) had the effect of stabilizing the metal sorption/desorption process over time.The extraction of Cd from soils S5 and S6 (Figures 7  and 8a-c) indicated a slowing down of the desorption process accompanied by interactions between the characteristic parameters of the soil and the extraction solutions determined as factors that strongly influenced the distribution of the metal in the analyzed solutions.

Evaluation of the Extraction Capacity of the Studied Solutions for Determining the Extractable As and Cd Concentrations Compared to the Extracting of Water from Contaminated Soils
To determine the concentrations of As and Cd extracted from soils contaminated with different concentrations of As, Cd, and Ni, three types of extractive solutions (M1, M2, and M3) were used in comparison with deionized water (DIwater-M) extract.The evaluation of the extraction capacity of the determination of extractable metal concentrations was found using the values of the KdAs and KdCd distribution coefficients for these metals in the analyzed solutions (Figure 9 for As and Figure 10  Other studies have obtained similar results [51].
The experimental results showed that M1-CaCl 2 and M3-EDTA are solutions that can best evaluate the concentration of toxic metals in soils contaminated with As and Cd.The method of extracting As and Cd metals into DIwater was not adequate for determining the amounts of extractable metal from soils with high levels of contamination.

Evaluation of the Accumulation Potential of As and Cd in the Sinapis alba Plant
Most plants growing in soils polluted with toxic metals show a physiology in which they can avoid the uptake of metals, while in others, the accumulation process may differ between different parts of the plant.Mechanisms of toxic metal accumulation at the whole plant level involve the regulation of several processes, including metal uptake by the root [52].Accumulation of toxic metals in plant roots can cause disturbances in the ratio of nutrients in plant tissues and changes in water balance [53].To assess the bioaccumulation potential of Sinapis alba, As and Cd concentrations were analyzed in the root, stem, leaves, flowers, pods, and seeds of plants grown in soils contaminated with As, Cd, and Ni.The total metal concentration in the plant was obtained by summing the concentrations in the plant organs.The evaluation of As and Cd concentrations (Table 15) in the plant grown in S5 and S6 soils indicated the accumulation of As in the root (2.56 ± 0.12 mg/kg in S5 and 9.02 ± 0.12 mg/kg in S6) and the accumulation of Cd in leaves (1.46 ± 0.10 mg/kg in S5 and 2.52 ± 0.10 in S6).
The BAC-As and BAC-Cd bioaccumulation coefficients (Table 16) showed values between 0.1 and 1 in the two soils S5 and S6.The highest average values of BAC-As obtained (0.166 for soil S5 and 0.851 for S6) showed a moderate potential for accumulation of As in Sinapis alba root.The highest mean BAC-Cd values obtained (0.567 for soil S5 and 0.813 for S6)) indicated a moderate potential for phytoaccumulation of Cd in Sinapis alba leaves.The accumulation pattern of As and Cd metals in the plant parts was in the following order: for AsS5-Root >Pods > Leaves > Stem, Seeds >Flowers; for AsS6-Root > Leaves > Stem, Flowers > Pods, Seeds; for CdS5-Leaves > Root > Stem > Pods > Seeds > Flowers; and for CdS6-Leaves > Root > Stem >Flowers > Pods > Seeds.
Other studies confirm the retention of As predominantly in the roots of Sinapis alba [54] and translocation of Cd in the aboveground part of the plant [30,55].The results suggest that the ability of the Sinapis alba plant to survive high concentrations of As and Cd indicates that it could be used in a phytoremediation strategy for contaminated soils in areas affected by the mining industry.

Predictive Models Useful in Evaluating the Concentration of As and Cd in Sinapis alba Plant
The accumulation of heavy metals in plants is connected to the concentrations and chemical fractions of metals in the soil.Understanding dissolved chemical elements and the availability of metals in the soil is essential for soil management.Extractable fractions may be more advantageous in total metal content as a predictor of metal bioconcentrations in plants.In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant.Principal components regression (PCR) is a regression technique similar to multiple linear regression that models the relationship between an original variable and predictor variables, using principal components instead of predictor values.The PCA analysis was used to evaluate the interdependence relationships between the parameters proposed for the development of predictive models of As and Cd concentrations in the Sinapis alba plant.The results of the PCA analysis for As showed that the first main component, PC1, is correlated with six variables (Kd_M1S5, Asplant_S5, Root_S5, Stem_S5, Leaves_S5, Pods_S5) varying together.The first main component correlates strongly with Asplant_S5 (correlation 0.933).This shows that the increase in the concentration of As in the whole plant determines the increase in the metal concentration in the Stem (correlation 0.920), Leaves (correlation 0.886), Root (correlation 0.680), and the decrease in the KdAs distribution coefficient (correlation −0.715).The second main component, PC2, correlates with the variables Flowers_S5 and Seeds_S5 but does not present an element of interest for the purpose of this evaluation.The results of the PCA analysis for Cd showed that the first main component, PC1, is correlated with six variables (Root_S5, Stem_S5, Flowers_S5, Pods_S5) varying together.The second main component, PC2, correlates strongly with three variables (Kd_M1S5, Asplant_S5, Root_S5, Leaves_S5).The PC2 component correlates strongly with Asplant_S5 (correlation 0.931), and the increase in this variable causes the increase in As concentration in the Leaves (correlation 0.812) and positively influences the KdCd values (correlation 0.763).
The obtained results confirmed the interdependence relationship between the variables proposed to be used in the development of prediction models of As and Cd concentrations in the Sinapis alba plant using extractable metal concentrations, evaluated with the KdAs and KdCd distribution coefficients.

Prediction Models of As and Cd Concentrations in the Sinapis alba Plant
Regression modeling is one of the most widely used statistical processes for estimating relationships between dependent and independent variables, frequently applied in a wide range of successful applications.Multiple regression includes many techniques for modeling and analyzing variables to identify real-world problems.The conventional method is based on the assumption that the maximum accuracy of inaccessible data is obtained from models with the least amount of error in modeling the available data [56].Regression models can be useful in monitoring the accumulation of toxic metals in plants grown in contaminated soils [57] or in assessing the phytoremediation potential of plants [58].In this stage of the study, predictive models of As and Cd concentration values in the plant parts were developed which, according to the data obtained from the PCA analysis, present statistical relationships as variables.
The aR 2 values obtained in the prediction models of the As concentration in the organs of the Sinapis alba grown in the S5 soil presented values between 0.9912 and 0.9974.The aR 2 values obtained in the prediction models of the As concentration in the organs of the Sinapis alba grown in the S6 soil presented values between 0.9862 and 0.9989.The aR 2 value was 0.9986 for the prediction models of the Cd concentration in the leaves of the Sinapis alba grown in the S5 soil and 0.9987 for the prediction model of the Cd concentration in the leaves of the plant grown in the S6 soil.
The Durbin-Watson test values for a significance level of 5%, used for the analysis of serial autocorrelation in the multiple regression models generated for the prediction of KdAs and KdCd (N = 12; k = 4), were between 1.85 and 2. The results of the SW test for checking the normality of the data indicated no nonnormality values for the independent variables KdAs, KdCd, and the concentration of As and Cd in the organs of Sinapis alba for a significance level of 5% (p-value < 0.05).The root mean square error, RMSE, values obtained in the prediction models showed values between 0.152 and 0.524 for KdAs and between 0.148 and 0.643 for KdCd.The RMSE values obtained in the prediction models showed values between 0.037 and 0.331 for the As concentration and values between 0.092 and 0.103 for the Cd concentration in the organs of Sinapis alba.Small RMSE values indicated a good fit of the models to the original experimental data sets.
Studies to date have sought different extraction methods that more effectively reflect the bioavailability of metals to be extracted from the soil-plant system.L. P. Gough developed multiple regression models for the prediction of copper, iron, manganese, and zinc levels in plants using DTPA and EDTA soil extractants [61].Due to the characteristics of the soil parameters, the total concentration of metals in the soil cannot fully indicate the behavior of the metal in its mobility process in the soil-plant system.The relative contributions of variables on sorption/desorption mechanisms are complex and time-consuming.

Limitation
This study may have possible limitations.Prediction models (stationary experimental design) of metal concentrations in Sinapis alba were developed using the best-fit extraction method of mobile metal concentration.The practicability of using the future results obtained is reduced to the area where the variables present values in the uncertainty range established by the experimental design and the time that determines the maturity of the plant.

Characterization of the Soil Samples and the Plant
Two uncontaminated topsoil samples were collected from different areas at a depth of 0-30 cm, dried at room temperature, and passed through a 2 mm sieve.The soil samples were mixed with universal soil amendment in a ratio of 1:3 to provide the plants with the nutrients required for rapid growth.The physical-chemical parameters characteristic of the soil were analyzed before the enrichment with metals.The method of enrichment of soil samples according to the experimental plan consisted of spraying with solutions of different and combined concentrations of metal in a repetitive thin layer, calculated by the amount of soil required for plant cultivation and leaching tests in three extraction solutions followed by homogenization [61,62].After 10 days of stabilization, the six soil samples were homogenized and prepared according to the experimental design, as described in our previous work [55].Four samples taken from each soil were used in the evaluation of the extraction capacity of three solutions (M1-CaCl 2 , M2-DTPA, and M3-EDTA).For comparison, deionized water was used [63].The metal concentrations in the soil samples were determined after the stabilization of the contamination stage by mineralization in HNO 3 and HCl in a volume ratio 1:3.The four soil-solution samples in the proportion specified by the experiment were stirred for 2 h at a frequency of 40 rotation/minute.Three samples were taken from the totally filtered leachates and the concentration of metals was analyzed.Four other samples from each soil were prepared for the cultivation of Sinapis alba seeds.
Germination of Sinapis alba seeds took place in the greenhouse system at a temperature of approximately 28 • C. Plants were grown in each sample lot (n = 4) corresponding to each contaminated soil type under laboratory conditions.For each sample, the sampling took place in two stages: in the first stage, the plants that reached the flowering stage were collected, and in the second stage, the plants that reached maturity were collected.The plant samples taken were dried by lyophilization (Lyophilizer Christ Alpha 1-2 LSCbasic, Osterode am Harz, Germany), separated according to the type of organ analyzed (root, stem, leaves, flowers, pods, and seeds), grinded (Mortar Grinder RM 200, Retsch Romania Verder scientific, Bucharest), and divided into three samples.Plant samples and seeds were taken before cultivation, were mineralized in a microwave digestion system (Ethos Up Microwave Digestion Systems-Milestone, Italy), and analyzed for metals of interest in the study.The obtained results indicated values of As and Cd concentrations in the seeds used before the experiment below the determination limit of the device.As and Cd metal analysis in Sinapis alba parts, extraction solutions, and soil were determined by inductively coupled plasma optical emission spectrometry (AVIO 500 ICP-EOS Hydride Generation Spectrometer FIAS 400 Perkin Elmer, USA).The characteristic parameters of the soil were determined as follows: pH and conductivity were determined electrochemically in aqueous extract 1:5; total nitrogen was determined volumetrically after mineralization in sulfuric acid and salicylic acid; total phosphorus was determined spectrometrically (Specord 210 Plus UV-Vis Spectrometer Analytik Jena, Germany) after mineralization in nitric acid and perchloric acid in a ratio of 1:5; soil organic carbon was determined by the Walkley-Black method by oxidation with potassium bichromate and sulfuric acid.
where basic metal cations represents the sum of the ions Ca 2+ , Mg 2+ , Na + , K + , Al 3+ , and NH 4 + .The distribution coefficient Kd is a measure of the sorption of a metal as a metal ion in a geo-environment and is site-specific.It is a ratio between the amount of metal ions adsorbed on the soil mass and the amount of metal ions remaining in solution at equilibrium [64] and is calculated with Equation (2): where Si is the metal concentration in the soil and Ms is the metal concentration in the extractive solution.
The bioaccumulation coefficient (BAC) is calculated (Equation ( 3)) as the ratio of metal concentration in the plant parts (roots, stems, leaves, flowers, and leaves) to those in the external medium, such as soil, to qualify trace metal accumulation in plants.

BAC = Cplant/Si
(3) where Cplant are trace metal concentrations in plant parts (mg/kg) and Si are trace metal concentrations in soil (mg/kg).Four categories of metal bioaccumulation are proposed: BAC value lower than 0.01 categorizes a plant as a non-accumulator, between 0.01 and 0.1 as a low accumulator, between 0.1 and 1.0 as a moderate accumulator, and between 1.0 and 10.0 as a high accumulator or hyperaccumulator [64].

Response Surface Methodology (RMS)
The response surface method (RSM) is a collection of mathematical algorithms and statistical techniques for building empirical models.Optimizing the response Y (output dependent variable) that is influenced by the independent variable X is carried out by carefully designing experiments that represent a series of tests, called runs.They modify the input variables in order to identify the reason for the change in the output response.The response can be plotted, either in three-dimensional space or as contour plots that help visualize the shape of the response surface.One of the main advantages of RSM is that a large amount of information can be obtained from a limited number of experiments.By constructing models and plots, the effects of variables and their interaction on the response can be studied.The response surface methodology uses statistical models.That is why practitioners must be aware that even the best statistical model is an approximation of reality.In reality, models and parameter values are unknown and subject to uncertainty.An estimated optimal point is not necessarily optimal in reality, due to estimation errors and model inadvertences.This method is quite difficult to build, requiring more time compared to other statistical models.However, the response surface methodology can help researchers to effectively improve their experimental studies.For example, Box's initial response surface modeling allowed chemical engineers to improve a process that required expensive experiments [65,66].

PCA Analysis
In order to understand the complex relationship between the characteristic parameters of the soil and the statistical relationships between the variables of the multiple regression model developed in this study, PCA analysis was used [67].The methodology used included the own analysis of the correlation matrix, the characteristic parameters of the soil, the extraction solutions, and the analyzed plants.Each variable shows a loading that shows how well a variable is accounted for by the model components.They reflect the contribution of each variable (parameter) to the significant variation in the data set and the relationship between the variables.The correlation was used due to the differences between the measurement units (metal concentration in mg/kg and Kd in L/kg) by normalizing the variables using division by their standard deviations.Varimax rotation was used to maximize the sum of variance of the squared loadings, as all coefficients can be either large or close to zero, with few intermediate values.This method simplified the interpretation of the PCA analysis results by associating each variable to a specific factor.

Multiple Regression Models
Multiple regression is a type of linear regression that extends the simple case of a dependent variable and an independent variable to several independent variables and allows for the estimation of coefficients of each independent variable, evaluating how well they explain the variation of the dependent variable.In addition, multiple regression can be used to test hypotheses about the interaction effects of different variables and to compare the fit of different models [68].One of the main advantages of multiple regression is that it can include several independent variables, finding the complex nature of real-world phenomena.Multiple regression can identify interactions between variables, which can determine situations where the effect of one variable depends on the level of another variable.One of the disadvantages of multiple regression is that it can make the results difficult to interpret when multiple independent variables or complex interactions are involved.An essential condition in generating a predictable model is the verification of hypotheses and multiple regression conditions such as linearity, normality, independence, and multicollinearity.These conditions can be checked with diagnostic tests and charts.If these assumptions are violated, the results may be inaccurate or misleading.Overfitting can occur when too many independent variables are involved or when the variables are highly correlated with each other, and the model may lose its ability to generalize to new data.
Model evaluation shows the performance of the prediction model facilitating its practicability.The accuracy of a regression model indicates how close the predictive value is to the true value.
The methodology for the development of models for predicting the concentrations of As and Cd in the organs of the Sinapis alba plant followed the evaluation of the aR2 parameters, the Durbin-Watson test, the Shapiro-Wilk test, the probability value of each variable p < 0.05 included in the model, histogram plots, and the RMSE value.
The adjusted R square (aR2) determines the model fit of the dependent variables without considering the problem of overfitting when there are many variables and a complicated model.The adjusted R-square penalizes additional independent variables added to the model by adjusting the measure of error (RMSE or MAE) to prevent overfitting problems.
The root mean square error (RMSE) is the square root of the MSE calculated by the sum of the square of the prediction error, which is the experimental value minus the predicted value and then divided by the number of observations.The Durbin-Watson test was used in the analysis of the autocorrelation of residuals indicating the influence of past values on future values in the data set generated by the model and was related to the tabulated critical values for the no-intercept model [69].
The Shapiro-Wilk test was used to check the normality of experimental data which works better with small data sets [70].The test results did not reveal non-normality for the independent variable (p-value < 0.05 for a 5% significance level).Based on this result and after a visual examination of the histograms of the independent variables, it was decided to use the parametric Pearson correlation test.
Number Cruncher Statistical Systems (NCSS 2021) software was used for calculations and for statistical data analysis (PCA analysis, the response surface and contour plots, multiple regression equations and validation tests of the generated models, tests, plots, and calculations).

Conclusions
The research paper explores the results of using the Sinapis alba plant for soil phytoremediation, focusing on the bioaccumulation of the metals As and Cd.The study identifies the factors that influence the extraction of metals from contaminated soils and evaluates the extraction capacity of three solutions, M1-CaCl2, M2-EDTA, and M3-DTPA, and develops predictive multiple regression models to determine the concentrations of As and cd in the Sinapis alba plant using extractable metal concentrations.According to the study's findings, As and Cd may be removed from contaminated soil by the Sinapis alba plant using phytoremediation.
shows the 3D and contour plots of the response surface of the KdAs distribution coefficients in the extraction solutions M1 (a), M2 (b), and M3 (c) under a single 10 mg/kg As contamination (S3).

Figure 1 .Table 8 .
Figure 1.The response surface and contour plots for KdAs in extraction solutions M1 (a), M2 (b), and M3 (c) for a single contamination with 10 mg/kg As contamination (S3).Table 8.The values of these two principal components (Component Loadings after Varimax Rotation) for As extraction solutions (M2, M2, M3) in soil S4.Soil Solution Extraction The Values of Component Loadings after Varimax Rotation

Figure 1 .
Figure 1.The response surface and contour plots for KdAs in extraction solutions M1 (a), M2 (b), and M3 (c) for a single contamination with 10 mg/kg As contamination (S3).

Figure 2 .
Figure 2. The response surface and contour plots of KdAs in extraction solutions M1 (a), M2 (b), and M3 (c) for a single contamination with 25 mg/kg As (S4).The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15mg/kg and Cd 3 mg/kg (S5) are presented in Figure 3.Figure 4 shows the response surface and contour plots of the KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg, Cd 3 mg/kg, and Ni 10 mg/kg (S6).
Figure 4 shows the response surface and contour plots of the KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg, Cd 3 mg/kg, and Ni 10 mg/kg (S6).

Figure 2 .
Figure 2. The response surface and contour plots of KdAs in extraction solutions M1 (a), M2 (b), and M3 (c) for a single contamination with 25 mg/kg As (S4).

Figure 3 .Table 10 .Figure 4 .
Figure 3.The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg and Cd 2 mg/kg (S5).Table 10.The values of these two principal components (Component Loadings after Varimax Rotation) for As extraction solutions (M2, M2, M3) in soil S6.Soil Solution Extraction The Values of Component Loadings after Varimax Rotation

Figure 3 .Figure 3 .
Figure 3.The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg and Cd 2 mg/kg (S5).

Figure 4 .
Figure 4.The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of combined contamination with As 15 mg/kg, Cd 3 mg/kg, and Ni 10 mg/kg (S6).

Figure 4 .Table 11 .
Figure 4.The response surface and contour plots of KdAs in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of combined contamination with As 15 mg/kg, Cd 3 mg/kg, and Ni 10 mg/kg (S6).

Figure 5
Figure 5 shows the response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of a single contamination with Cd 2 mg/kg (S1).

Figure 5 .
Figure 5.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 2 mg/kg (S1).The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 5 mg/kg (S2) are presented in Figure 6.The matrices of Pearson coefficients and the 3D and contour plots of the KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with As 15 mg/kg and Cd 2 mg/kg (S5) and of combined contaminations with Cd 3 mg/kg, As 15 mg/kg, and Ni 10 mg/kg (S6) are presented in Figures 7 and 8.

Figure 5 . 24 Figure 6 . 24 Figure 6 .
Figure 5.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 2 mg/kg (S1).The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 5 mg/kg (S2) are presented in

Figure 7 .
Figure 7.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with Cd 2 mg/kg and As 15 mg/kg (S5).

Figure 6 .
Figure 6.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 5 mg/kg (S2).

Figure 6 .
Figure 6.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of single contamination with Cd 5 mg/kg (S2).Table 13.The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S5.Soil Solution Extraction The Values of Component Loadings after Varimax Rotation

Figure 7 .
Figure 7.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with Cd 2 mg/kg and As 15 mg/kg (S5).Table 14.The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S6.Soil Solution Extraction The Values of Component Loadings after Varimax Rotation

Figure 7 .
Figure 7.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of contamination with Cd 2 mg/kg and As 15 mg/kg (S5).Plants 2023, 12, x FOR PEER REVIEW 10 of 24

Figure 8 .
Figure 8.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of combined contamination with Cd 3 mg/kg, As 15 mg/kg, and Ni 10 mg/kg (S6).

Figure 8 .
Figure 8.The response surface and contour plots of KdCd in the extraction solutions M1 (a), M2 (b), and M3 (c) in the case of combined contamination with Cd 3 mg/kg, As 15 mg/kg, and Ni 10 mg/kg (S6).

2. 4 .
Evaluation of the Extraction Capacity of As and Cd from Contaminated Soils in M1-CaCl 2 , M2-DTPA, and M3-EDTA Solution Comparison with Water Extraction The diversity of influencing factors of the sorption/desorption process of toxic metals in soils can make it difficult to derive some reference values of Kd distribution coefficients of toxic metals to be used as reliable indicators for evaluating the remediation process of contaminated soils.A constant update of the Kd values of heavy metals may be necessary for a thorough understanding of the influence of different factors on these processes and the determination of the extraction capacity of the solutions.Figures 9 and 10 show the variation in the distribution coefficient Kd in the three extractive solutions, M1-CaCl 2 , M2-DTPA, and M3-EDTA for As and Cd, respectively, in soils contaminated with different concentrations of metals.and M3 (c) in the case of combined contamination with Cd 3 mg/kg, As 15 mg/kg, and Ni 10 mg/kg (S6).

3. 4 . 1 .
Evaluation of the Interdependence of the Variable Parameters Used in the Development of Multiple Regression Models for the Evaluation of As and Cd Concentrations in the Sinapis alba Plant

66 .
The values of the Durbin-Watson test used for the analysis of serial autocorrelation in the gender-error multiple regression models for predicting the concentration of As and Cd in the parts of the Sinapis alba (N = 12; k = 2) were between 2.27 and 2.80.The results of the Durbin-Watson test indicate values higher than d U (d-Durbin-Watson critical values-95%, d L -lower limit value, d U -upper limit value), so there is no autocorrelation between the residual values.The critical values for the models without intercept are d L = 0.397 and d U = 1.682 (N = 12, k = 4) and d L = 0.674 and d U = 2.268 (N = 12, k = 2) [59,60].
The cation exchange capacity (CEC) was estimated empirically based on the content of cations in the extraction solutions with Equation (1): CEC = basic metal cations + acidic metal cations [meq/100g]

Table 1 .
Characteristics of soils with amendment used in the experiment.

Table 2 .
The concentration of As and Cd in different artificially contaminated soils.

Table 3 .
The values of As concentrations in the CaCl 2 , DTPA, and EDTA extraction solutions and the values of the Kd distribution coefficients of As in these solutions.

Table 4 .
Values of the Cd concentrations in the CaCl 2 , DTPA, and EDTA extraction solutions and the values of the Kd distribution coefficients of Cd in these solutions.

Table 5 .
The pH values of the extraction solutions.
Table 6 shows the cation exchange capacity values estimated in the extraction solutions and the organic carbon values in the contaminated soil samples.

Table 6 .
The values of the estimated cation exchange capacity (CEC) and soil organic carbon (SOC) in the studied soils.Evaluation of the Influence of Factors on the Extraction of Metals As and Cd from Contaminated Soils Using PCA and RSM

Table 8 .
The values of these two principal components (Component Loadings after Varimax Rotation) for As extraction solutions (M2, M2, M3) in soil S4.

Table 10 .
The values of these two principal components (Component Loadings after Varimax Rotation) for As extraction solutions (M2, M2, M3) in soil S6.

Table 9 .
The values of these two principal components (Component Loadings after Varimax Rotation) for As extraction solutions (M2, M2, M3) in soil S5.

Table 12 .
The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S2.

Table 13 .
The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S5.

Table 14 .
The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S6.

Table 11 .
The values of these two principal components (Component Loadings after Varimax Rotation) for Cd extraction solutions (M2, M2, M3) in soil S1.

Table 15 .
The values of As and Cd concentrations determined in the organs of the Sinapis alba grown in soils contaminated with As, Cd, and Ni.

Table 15 .
The values of As and Cd concentrations determined in the organs of the Sinapis alba grown in soils contaminated with As, Cd, and Ni.

Table 16 .
The values of the BAC-As and BAC-Cd determined in the organs of the Sinapis alba grown in soils contaminated with As, Cd, and Ni and the metal accumulation pattern.

Table 17 .
The values of these two principal components (Component Loadings after Varimax Rotation) for the variables included in the predictive model of metal concentration of As and Cd in Sinapis alba.
* Conc_plant_S5: total content of metal (As, Cd) in the plant according to experimental data.

Table 18 .
The equations of the multiple regression model, The average experimental and predictive values of extractable As concentration in the organs of the Sinapis alba, and the values of the model validation parameters (aR 2 , DW, SW, and RMSE).

Table 19 .
The equations of the multiple regression model, the experimental and predictive values of extractable Cd concentrations in the leaves of the Sinapis alba, and the values of the model validation parameters (aR 2 , DW, SW, and RMSE).