{"paper_id":"30190237-8e8e-4f47-81ff-4ede26245510","body_text":"QICAR methods for assessing the ecotoxicity of soil metal(loid)s to lettuce | 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 QICAR methods for assessing the ecotoxicity of soil metal(loid)s to lettuce Xiaorong Luo, Xuedong Wang, Cunyan Xia, Jing Peng, Ying Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1628233/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 New pollution elements introduced by the rapid development of modern industry and agriculture may pose a serious threat to the soil ecosystem. To explore the ecotoxicity and risk of these elements, we systematically studied the acute toxicity of 19 metal(loid)s toward lettuce using hydroponic experiments and quantitative relationships between element toxicity and ionic characteristics using ion-grouping and ligand-binding theory methods, thereby establishing a quantitative ion character-activity relationship (QICAR) model for predicting the phytotoxicity threshold of data-poor elements. The toxicity of 19 ions to lettuce differed by more than four orders of magnitude (0.05 µM-953.16 µM). Correlation and linear regression analysis showed that the ionic characteristics significantly associated with this toxicity explained only 23.8%-50.3% of the toxicity variation ( R 2 Adj = 0.238–0.503, p < 0.05). Relationships between toxicity and ionic properties significantly improved after separating metal(loid) ions into soft and hard, with R 2 Adj of 0.793 and 0.784 ( p < 0.05), respectively. Three ligand-binding parameters showed different predictive effects on lettuce metal(loid) toxicity. Compared with the binding constant of the biotic ligand model (log K ) and the hard ligand scale (HLScale) ( p > 0.05), the softness consensus scale (σ Con ) was significantly correlated with toxicity and provided the best prediction ( R 2 Adj = 0.844, p < 0.001). We selected QICAR equations based on soft-hard ion classification and σ Con methods to predict phytotoxicity of metal(loid)s, which can be used to derive ecotoxicity for data-poor metal(loid)s, providing preliminary assessment of their ecological risks. Metal Quantitative ion character–activity relationship (QICAR) Toxicity lettuce Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction With industrial and agricultural modernization and urbanization, heavy metal pollution in the soil environment has become increasingly prominent. Pollution elements include not only heavy metals such as lead (Pb), cadmium (Cd) and mercury (Hg) that people often pay attention to, but also some uncommon new metal(loid)s such as lanthanum (La), antimony (Sb) and vanadium (V) (Gong et al. 2020 ; Ji et al. 2020 ). Nevertheless, there is little information about these new pollution elements, including their ecotoxicity, related risks and control standards, which seriously affects the pollution prevention and control work of the legislative management department. In addition, understanding the toxicity and risk information of elements requires many tests, which involve considerable time and cost. Therefore, it is particularly important to develop an element risk prediction model independent of toxicity tests. The quantitative structure-activity relationships (QSAR) model has been widely favored by scholars as a fast, accurate and independent toxicity test. QSAR modeling uses mathematical statistics to describe the internal relationship between the molecular structure of organic compounds and ecotoxicological effects (Hansch and Gao 1998 ; Karelson et al. 1996 ). Its basic assumption is that bioactivity depends on structural changes in organic compounds, while the structure of compounds can be characterized by various molecular structure characteristic parameters reflecting the structural characteristics (i.e., molecular structure descriptors) (Suh et al. 2011 ). Since the United States Environmental Protection Agency (USEPA) began to apply the QSAR method to estimate the thermodynamic properties and toxicity classification of different organic chemicals in 1978, the QSAR model has made considerable progress in predicting the properties and toxicity of organic compounds (Abramenko et al. 2020 ). QSAR was subsequently applied to inorganic elements and was defined as the quantitative ion character-activity relationship (QICAR) by Newman and McCloskey ( 1996 ). The QICAR is used for predicting metal ecotoxicity according to the relationships between ionic structure characteristics and activity (Newman et al. 1998 ; Walker et al. 2003 ). Since the QICAR model was proposed, it has been widely used in aquatic environments. For instance, Chen et al. ( 2015 ) applied QICAR for exploring the relationships between the physicochemical properties of 34 metal ions and their acute toxicity to eight marine organisms, finding that the softness index (σp) can appropriately predict the acute toxicity of metals. Li et al. ( 2012 ) also found good relationships between the toxicity of metals to Cypris subglobose in freshwater and physicochemical properties such as electronegativity (Xm) using the QICAR method. However, recent studies have highlighted that significant differences in metal properties may cause different dose-responses of organisms to individual elements, making it difficult to use one or more properties to quantify the relationships between various elements and their ecotoxicity (Chen and Wang 2007 ; Zamil et al. 2009 ). The hard-soft acid-base theory (HSAB) provides an effective way to solve this problem. HSAB theory was proposed by Ralph G. Pearson in 1963 based on Lewis acid-base theory (Pearson 1963 ). He believed that Lewis acids and bases can be separated into two categories (i.e., soft and hard) according to different properties. \"Soft\" refers to those ions with a larger radius, higher polarizability and lower charge density, while hard ions have the opposite properties (Pearson 1963 , 1968 ; Pearson and Mawby 1967 ). A QICAR model based on hard-soft grouping has been used to predict metal ecotoxicity (Meng et al. 2019b ; Zamil et al. 2009 ). Li et al. ( 2020 ) classified metals into soft, borderline and hard ions based on HSAB theory and found that QICARs developed by borderline plus soft or plus hard ions could accurately predict ecological risk assessment screening values. Nevertheless, studies related to soil plants have identified hard ligands such as carboxyl groups and soft ligands such as thiol groups in plant roots, and the ability of these ligands to coordinate metals are different (Kopittke et al. 2014 ). Hence, there is difficulty in quantifying metal-ligand binding ability by employing a single property of these elements. Kinraide developed the hard ligand scale (HLScale) in 2007 (Kinraide and Yermiyahu 2007 ) and the softness consensus scale (σ Con ) in 2009 (Kinraide 2009 ), respectively. The HLScale is the averaged and normalized binding strengths of metals to 13 hard ligands such as oxalic acid, citric acid and carbonate. Kinraide and Yermiyahu ( 2007 ) concluded that a majority of cations are inclined to bind to hard ligands, and the strength of binding between them correlates well with ion binding to biomass such as plasma membranes. There are some exceptions to this; the toxicities of soft ions such as Tl + and Ag + correlate poorly with the HLScale, suggesting that their binding ability to hard ligands may not be the main factor causing their toxic effects. Therefore, Kinraide ( 2009 ) also proposed a consensus scale related to ionic softness, i.e., σ Con ; σ Con has a significantly positive correlation with metals binding to soft ligands, and the interaction of σ Con and charge (Z) can predict toxicity well ( R 2 = 0.923) (Kinraide 2009 ). Although both HLScale and σ Con show good performance in element toxicity prediction, some ions such as Ag + still perform poorly. Moreover, these predictive results also require further verification with different plants. Recently, some scholars have also linked QICAR to the binding constant ( K ) of the biotic ligand model (BLM) to explore the relationship between phytotoxicity and metal-ligand binding ability (Li et al. 2016 ; Meng et al. 2019a , 2020 ; Zhou et al. 2011 ). BLM assumes that bioactive sites are bioligands (BLs), which may be physiologically active sites resulting in direct responses or transport sites resulting in indirect responses. In BLM, toxicity is determined by the affinity of ions binding to the BLs (expressed as conditional binding constant, K ); that is to say, toxicity will occur when ions binding to BLs reach a certain amount. However, this law between toxicity and K has not been verified for additional elements and plant species. The objectives of the present study were to 1) explore the toxicity of a large number of metal(loid) ions to typical crops in hydroponic culture; 2) correlate toxicity with various physicochemical properties of metal(loid)s and quantify the relationships between them, and consider the impact of metal(loid) ion classification on these relationships; 3) investigate the relationships between toxicity and metal-ligand binding affinity (HLScale and σ Con , and BLM-based K ); 4) develop QICARs for predicting the toxicity threshold of data-poor metal(loid) elements to plants. Our results are of great significance for obtaining ecotoxicity and risk information for elements contaminating the soil. Materials And Methods Experimental setup A 4-day acute toxicity test was conducted using lettuce ( Lactuca sativa L., cv. Italian Lettuce) in hydroponic culture with a simplified nutrient solution. Each solution treatment group contained one of 19 metal(loid) ions: Ag + , Cu 2+ , Zn 2+ , Ni 2+ , Co 2+ , Cd 2+ , Mg 2+ , La 3+ , Sc 3+ , Cr 3+ , Cr 6+ , As 3+ , As 5+ , Se 4+ , Se 6+ , Sb 3+ , Sb 5+ , Ⅴ 5+ , Mo 6+ . Each treatment group was set up with seven concentration levels and one control, with three biological replicates per group, yielding a total of 456 treatments. The concentration range of metal(loid) ions in the above solutions was determined using a pre-experiment to ensure non-toxic to lethal concentrations (Table S1, Supplementary material). Test solution The basal culture solution for 19 metal(loid)s was 0.2 mM Ca prepared in deionized water, where the basal Ca was supplied as CaCl 2 for 18 metal(loid)s and as Ca(NO 3 ) 2 ⋅4H 2 O for Ag. 2-( N -Morpholino)-ethanesulfonic acid (MES) was used as a butter to modulate pH to approximately pH 6.0. All solutions were equilibrated for 1 d before use. Toxicity bioassays Lettuce root elongation tests were conducted according to the standardized protocol in ISO 11269-1: 2012. Sterilized and thoroughly rinsed lettuce seeds were transferred to an incubator for 48 h of pre-germination at 25 ℃, 80% humidity, and no light irradiation. Seedlings with a length of approximately 1 cm were transferred to a plastic pot filled with 250 mL of test solution and fixed using a nylon net. Six seedlings were introduced into each pot, and all the culture pots were randomly placed in the incubator chamber (temperature regime of 20/18 ℃ day/night; photoperiod of 16/8 h light/dark; photon flux density of approximately 90 µmol⋅m − 2 ⋅s − 1 ). During the experiment, the test solutions were refreshed every 2 days. Root length of each seedling was measured after 4 days of exposure. The relative net root elongation (RNE %) was calculated as follows (Eq. ( 1 )): $$\\text{R}\\text{N}\\text{E (\\%) = }\\frac{\\text{R}{\\text{E}}_{\\text{t}}\\text{-}{\\text{RE}}_{\\text{0}}}{{\\text{RE}}_{\\text{c}}\\text{-}\\text{R}{\\text{E}}_{\\text{0}}}\\text{×10}\\text{0}$$ 1 where RE t (cm) is the average root elongation of six seedlings exposed to the metal(loid) ion treatment; RE c (cm) is the average root elongation of the control group; RE 0 (cm) is the root elongation before exposure (i.e., 1 cm). Microscopic examination of root tips Root tip samples for scanning electron microscope (SEM) examination were prepared following the method of Xu et al. ( 2017 ). Approximately 1 cm of lettuce root tip was immersed in a cell culture plate filled with tert -butanol, which was wrapped with sealing film and aluminum foil and transferred to a refrigerator (-20°C) for 12 h. Subsequently, lettuce root tips were lyophilized at -20°C, 14 Pa (LGJ-18S; Beijing, China). After being coated with Au using an ion sputterer (JEOL JFC-1100; Tokyo, Japan), lettuce root tips were examined with a field emission SEM (Hitachi S4800, Japan). Chemical measurements Test solution pH was measured using a pHS-3C precision acidity meter (INESA; Shanghai, China). Actual concentrations of metal(loid) ions in the test solutions were determined using an inductively coupled plasma mass spectrometer (ICP-MS, USA-Agilent-Agilent 7700/7800). For quality-control purposes, a single-element standard reagent and a reagent blank were tested every 20 samples, and the internal standard recovery of each element was 100%. Free metal(loid) ion activities in solutions were measured using the speciation software Visual MINTEQ 3.1 (An et al. 2020 ; Gustafsson 2014 ) with solution pH, temperature and ion concentrations as input variables. Data source and modeling The log-logistic function (Eq. ( 2 )) was applied to fit the dose-effect relationship between free metal(loid) ions activity and RNE, which allowed calculation of the dose inhibiting 50% of root elongation (EC 50 ). $$\\text{R}\\text{N}\\text{E (\\%)}\\text{ =}\\frac{{\\text{Y}}_{\\text{0}}}{\\text{1+}{\\text{e}}^{\\text{b}\\left(\\text{X}\\text{-}\\text{M}\\right)}}\\text{×100}$$ 2 where X is free ion activity of metal(loid)s (µM), and Y 0 , b and M (lg {EC 50 }) are the fitting constants. Twenty-three significant physicochemical properties of metal(loid)s were chosen, as reported in previous studies or found in the Handbook of Chemistry and Physics (Le Faucheur et al. 2021 ; Rumble 2018 ; Walker et al. 2013 ). The values of properties such as boiling point (BP), melting point (MP), density (D) and difference in ionization potentials between the ion oxidation numbers OX and OX − 1 (ΔIP) (Kaiser 1980 ) were obtained from Lide and Haynes ( 2013 ); atomic mass (AW), atomic number (AN), atomic radius (AR), Pauling ionic radius (r), ionization potential (IP), electrochemical potential (ΔE 0 ) (Kaiser 1980 ; Newman and McCloskey 1996 ), electronegativity (Xm), and ionic charge (Z) were obtained from Wolterbeek and Verburg ( 2001 ); logarithmic of first hydrolysis constant (|log(-K OH )|), covalent radius (CR) and softness index (σp) were from Base and Mesmer ( 1976 ), Shannon and Prewitt ( 1970 ) and Pearson and Mawby ( 1967 ), respectively; electron density (AR/AW) (Base and Mesmer 1976 ), covalent index (Xm 2 r), polarization force parameters (Z 2 /r, Z/r 2 , Z/r), atomic ionization potential (AN/ΔIP) and similar polarization force parameters (Z/AR, Z/AR 2 ) were derived by calculation. Detailed values of these physicochemical properties are shown in Table S2. Three constants (HLscale, σ Con and BLM-based log K ) characterizing the metal(loid)-ligand binding affinity were selected in addition to the above physicochemical properties. Values of log K were derived from published studies using analogous test species and experimental conditions to the present study (Table 1 ); values of σ Con and HLScale were derived from Kinraide ( 2009 ) (Table 1 ). Internal connections between metal(loid) toxicity and ionic characteristics, HLScale, σ Con and log K were analyzed on the basis of the QICAR model principle, respectively. These relationships were quantified using univariate linear regression. To lower the self-correlation between metal(loid) ion characteristics, principal component analysis (PCA), a dimension-reduction statistical method, was employed to create a new group of predictive variables, termed principal components (PCs). Generally, relatively few PCs can show most of the total variability of the dataset. Therefore, multivariate linear relationships based on PCA were also established. F and p values were obtained from an analysis of variance to evaluate the magnitude of correlation between toxicity and characteristics; adjusted determination coefficient ( R 2 Adj ) and root-mean-square error ( RMSE ) were used to test the goodness-of-fit of equations. An optimal QICAR equation was selected by considering the highest R 2 Adj and F -statistic with p < 0.05, and the lowest RMSE . Correlation statistics and model building were conducted using SPSS version 24.0 (IBM, NY, USA). Validation of the QICAR model Verification of the model was performed by self-verification and external verification methods. The external verification data set were not involved in the modeling. Sc 3+ and Se 4+ were used for external verification of soft-hard ion grouping modeling, while La 3+ , Sc 3+ and Se 4+ were used for external verification of the σ Con method. Results Dose-response relationships The fitted dose-response curves for 19 metal(loid) exposures are shown in Fig. S1. With an increase of free ion activity, RNE decreased gradually, indicating that these metal(loid) ions produced obvious inhibition of lettuce root growth. The {EC 50 } values of 19 metal(loid)s to lettuce, fitted by dose-response curves, varied from 0.05 µM (Ag + ) to 953.16 µM (Sb 5+ ), with a difference of 19063.20 times between the highest and lowest values, greater than four orders of magnitude (Table 1 ). The only ion with {EC 50 } in the range of 10 − 2 – 10 − 1 was Ag + , indicating that Ag + causes the most obvious inhibition to lettuce roots. Ions with {EC 50 } in the range 10 − 1 – 10 0 comprised Se 6+ , Cu 2+ , Ni 2+ , La 3+ , Co 2+ , Sc 3+ and Cr 6+ ; the lower {EC 50 } values (0.15 µM-0.89 µM) revealed that these ions are also highly toxic to lettuce. Meanwhile, ions with {EC 50 } in the range 10 0 – 10 1 accounted for the largest proportion, with nine metal(loid) ions ranked in descending order of their toxicity as follows: Cd 2+ , Cr 3+ , Mo 6+ , Zn 2+ , As 3+ , Sb 3+ , V 5+ , As 5+ and Se 4+ ({EC 50 } = 1.21 µM-7.67 µM). In the range of 10 2 – 10 3 , the larger {EC 50 } values of Mg 2+ (804.44 µM) and Sb 5+ (953.16 µM) indicated lower toxicity than the other ions tested. In sum, there was great variation in the toxicity of 19 metal(loid) ions, indicating that these ions have different toxic effects on lettuce. Table 1 {EC 50 }, log K , σ Con and HLScale values for different metal(loid) ions. {} refers to free ion activity Ion {EC 50 } µM log K a σ Con b HLScale c Ag + 0.05 {0.04 − 0.06} 6.39 0.84 −0.28 Se 6+ 0.15 {0.07 − 0.38} − 0.19 − Cu 2+ 0.27 {0.21 − 0.35} 7.4 0.57 −0.09 Ni 2+ 0.32 {0.21 − 0.48} 5.1 0.29 −0.41 * La 3+ 0.53 {0.34 − 0.83} − −0.53 0.18 Co 2+ 0.74 {0.34 − 1.60} − 4.65 0.27 * Sc 3+ 0.81 {0.38 − 1.72} − −0.69 0.88 Cr 6+ 0.89 {0.33 − 2.40} − 0.27 − Cd 2+ 1.21 {0.74 − 1.98} 3.96 0.17 −0.48 Cr 3+ 1.80 {1.19 − 2.74} − 0.02 0.78 Mo 6+ 1.96 {0.70 − 5.45} − 0.43 − Zn 2+ 2.33 {1.33 − 4.08} 4.00 −0.09 −0.41 As 3+ 2.75 {1.90 − 3.97} − −0.16 − Sb 3+ 3.44 {2.48 − 4.79} − −0.04 − V 5+ 5.87 {3.97 − 8.68} − − 0.13 − As 5+ 5.87 {4.55 − 7.59} − − 0.16 − * Se 4+ 7.67 {4.48 − 13.13} − 0.00 − Mg 2+ 804.44{584.22 − 1107.69} 2.86 −1.02 −0.88 Sb 5+ 953.16{833.79 − 1089.62} − −0.01 − a Log K was derived from Le et al. ( 2013 ), Liu et al. ( 2014 ) and Qiu et al. ( 2015 ). b σ Con values were derived from Kinraide ( 2009 ), if available. Otherwise, they were calculated using the formula σ Con = 0.0454D + E 0 * 0.067I P and are printed in italics, where D is density, E 0 is standard electrode potential and I p is the first ionization potential [Barbalace (2008) Environmental Chemistry http://environmentalchemistry.com ]. c HLScale values were taken from Kinraide ( 2009 ). * These ions were used for external verification. Toxic symptoms of metal(loid) ions on lettuce roots Since metal(loid) toxicity to lettuce varies greatly, we observed the ultrastructure of lettuce roots treated with these pollution elements to help clarify the reasons for metal(loid) toxicity. We selected element treatment groups with variable valences (As 3+ , As 5+ , Se 4+ and Se 6+ ) that displayed obvious toxicity differences to lettuce and used scanning electron microscopy to examine the damage to root tips in comparison with the control group when these ions inhibited 60% of root elongation (Fig. 1 ). Root tip cells and epidermal cells of control lettuce root samples were intact (Fig. 1 a). For variable-valence element As, As 3+ -treated lettuce root tips were small and loose, whereas As 5+ -treated root tips were curved with no obvious ruptures (Fig. 1 b-c). The difference in toxicity symptoms of As to lettuce roots corresponded to that in toxicity effects of As on lettuce; that is, the toxicity of As 3+ to lettuce was stronger than that of As 5+ (Table 1 ). Similarly, Se 6+ treatment destroyed lettuce root caps while Se 4+ had no obvious effect on lettuce root tips (Fig. 1 d-e). Overall, these root symptoms confirmed root cell damage caused by metal(loid) ions, while elements with variable valence caused different toxicity symptoms in lettuce roots, suggesting that they have different mechanisms of toxicity to lettuce. Modeling based on toxicity and ionic characteristics of 19 metal(loid)s The toxicity of metal(loid)s to lettuce was expressed as log {EC 50 }. Correlation analysis between log {EC 50 } and 23 characteristics identified five properties significantly correlated with log {EC 50 } ( p < 0.05) (Fig. 2 ). These characteristics were AR/AW, D, σp, Xm 2 /r and ΔE 0 in descending order of correlation ( r = 0.732 ~ 0.538, p = 0.001 ~ 0.032). Among them, D and Xm 2 /r were negatively correlated with log {EC 50 }, while AR/AW, σp and ΔE 0 showed positive correlations with log {EC 50 }, indicating that metal(loid) toxicity to lettuce increased with increasing D and Xm 2 /r, but decreased with increasing AR/AW, σp and ΔE 0 . The univariate linear relationships and regression equations established by log {EC 50 } and the above five characteristics with p < 0.05 are shown in Fig. S2 and Table 2 , respectively. AR/AW had the best linear relationship with log {EC 50 } but could only explain 50.3% of the toxicity variation ( R 2 Adj = 0.503, p = 0.001). Given the self-correlation between the ionic characteristics of metal(loid)s, we further investigated the multivariate linear relationships between log {EC 50 } and characteristics using the PCA method (Table S3). Results showed that the cumulative contribution rate of new independent variables (PCx) with different combinations of properties created by PCA was more than 80%, with PCx1 involving D and σp showing the best multivariate relationship ( R 2 Adj = 0.460, p = 0.002). However, by comparing R 2 Adj , RMSE , F- statistics and p- value, there was no significant difference between PCA-based multivariate linear relationships and univariate linear relationships. Table 2 Univariate linear regression equations between log {EC 50 } and physicochemical properties with p < 0.05. R 2 is the coefficient of determination, R 2 Adj is the adjusted coefficient of determination, RMSE and p are the root-mean-square error and the statistical level of significance, respectively Type Regression equation R 2 AdjR 2 RMSE F p Metal(loid) ions log {EC 50 } = 46.750 AR/AW – 0.996 0.536 0.503 0.66 16.187 0.001 log {EC 50 } = -0.295 D + 2.283 0.492 0.456 0.69 13.563 0.002 log {EC 50 } = 21.682 σp – 2.416 0.378 0.334 0.76 8.517 0.011 log {EC 50 } = -0.605 Xm 2 /r + 1.581 0.292 0.241 0.81 5.765 0.031 log {EC 50 } = 0.700 ΔE 0 – 0.316 0.289 0.238 0.81 5.692 0.032 Soft ions log {EC 50 } = -0.371 D + 2.899 0.822 0.793 0.29 27.756 0.002 log {EC 50 } = 0.843 Z – 1.925 0.734 0.690 0.35 16.566 0.007 log {EC 50 } = 0.081 IP – 1.670 0.635 0.574 0.41 10.418 0.018 log {EC 50 } = 0.916 Z/AR − 1.431 0.571 0.500 0.45 7.999 0.030 log {EC 50 } = 0.378 Z/r – 1.225 0.569 0.498 0.45 7.935 0.030 log {EC 50 } = -0.152 |log(-KOH)| + 1.078 0.542 0.466 0.46 7.099 0.037 log {EC 50 } = 0.1 Z 2 /r – 0.817 0.542 0.466 0.46 7.105 0.037 Hard ions log {EC 50 } = 52.385 AR/AW – 1.093 0.815 0.784 0.52 26.365 0.002 Modeling based on toxicity and characteristics of soft-hard ions Considering that differences in the ionic characteristics of metal(loid)s may lead to differences in toxicity, these elements were separated into two groups according to the HSAB theory: soft and hard (Pearson and Mawby, 1967 ). Correlation and linear regression between toxicity and characteristics of soft-hard ions were further analyzed (Fig. 3 , Fig. S3, Table S3). Correlation analysis (Fig. S3) revealed seven properties (D, Z, IP, Z/AR, Z/r, |log (-KOH)|, Z 2 /r) among the 23 ionic characteristics that were significantly correlated with the toxicity of soft ions ( p < 0.05). Except for D and |log(-KOH)| ( r < 0, p < 0.05), the other characteristics showed positive correlations with log {EC 50 }. With regard to hard ions, AR/AW was strongly positively correlated with log {EC 50 }, with r = 0.903 and p = 0.002 (Fig. S3). At the same significance level ( p < 0.05), the correlation coefficient r between the toxicity of soft-hard ions and characteristics was larger than that when all metal(loid) ions tested were considered, indicating that the relationship between toxicity and ionic characteristics was improved after classifying ions into soft and hard. We established univariate and multivariate linear relationships of soft and hard ions, respectively. (Fig. 3 , Table S3). Univariate linear regression analysis showed that D and AR/AW exhibited the best fitting with the toxicity of soft ions and hard ions, respectively, explaining up to 79.3% of toxicity variation in soft ions ( R 2 Adj = 0.793, p = 0.002) and 78.4% of toxicity variation in hard ions ( R 2 Adj = 0.784, p = 0.002) (Fig. 3 , Table 2 ). The high R 2 Adj values of soft-hard ions indicated that the method of grouping ions following the HSAB theory could significantly improve the linear regression prediction of metal(loid) toxicity to lettuce roots. The multivariate linear relationship for soft ions (Table S3) showed similar results. However, the comparison of R 2 , RMSE , F -statistics and p -values showed no significant difference between univariate and multivariate linear relationships. Considering that the regression relationship based on multiple variables may lead to large errors and autocorrelations, we selected the univariate linear relationship of soft ions with D, and that of hard ions with AR/AW to establish the optimal QICAR prediction equations (Table 2 ). Modeling based on metal(loid) toxicity and ligand-binding constants Ligands in plant roots are critical factors in the harmful effects exerted by metals. Investigating the relationships between toxicity and metal-ligand binding affinity is therefore of great importance. We selected three parameters characterizing ion-ligand binding affinity (log K , σ Con , and HLScale). In general, the larger the log K , the closer the binding between ions and ligands. Similarly, the higher the σ Con value, the stronger the interaction between ions and soft ligands. In the HLScale, 0.0 denotes the mean binding strength, whereas − 1.0 and 1.0 denote 1 standard deviation lower or higher than the mean, respectively. Accordingly, we conducted a correlation analysis and linear regression between the three parameters and log {EC 50 }, as shown in Fig. S4 and Fig. 4 . Ag + was excluded from these relationships because the strong binding of Ag to soft ligands resulted in its deviation in the HLscale (HLscale for Ag + was < -1.0, lower than 1 standard deviation). The three ligand-binding parameters showed different correlations with log {EC 50 }; σ Con showed a significantly negative correlation ( p < 0.05), indicating that log {EC 50 } decreased with the increase of σ Con , whereas, log K and HLScale had no significant correlation with log {EC 50 } with p greater than 0.05 (Fig. S4). Therefore, the univariate linear relationship between σ Con and log {EC 50 } ( R 2 Adj = 0.844, p < 0.001) was selected to establish the QICAR model in the present study. The predictive equation is as follows: l \\(\\text{og {}{\\text{EC}}_{50}\\text{} = -2.098 σ}\\text{Con}\\text{ + 0.399}\\) (3) Validation of QICAR predictive models Corresponding regression equations were established according to the relationships between toxicity and physicochemical properties. By comparing the statistics R 2 Adj , F , p and RMSE , we finally selected the equations established by the soft-hard ion grouping method (soft ions with D, hard ions with AR/AW, Table 2 ) and the σ Con method (Eq. (3)) as the QICAR model for lettuce. To evaluate the accuracy of the model, self-validation and external validation were conducted, respectively (Fig. 5 ). The model based on σ Con had a good effect in predicting log {EC 50 }; except for La 3+ and Sc 3+ , errors between the observed and predicted log {EC 50 } values ​​were within 1.5 orders of magnitude. It is worth noting that the model could predict the {EC 50 } values of As 5+ , Co 2+ , Cd 2+ , Sb 3+ and Ag + well, with differences between observed and predicted log {EC 50 } values of less than 0.20 times. Among them, the prediction of the effect of As 5+ was the best, with a predicted {EC 50 } of 5.51 µM and an observed value of 5.87 µM. Similarly, with regard to the model developed using the soft-hard ion grouping method, the errors between observed and predicted log {EC 50 } were below 1.5 orders of magnitude, and the observed and predicted {EC 50 } values of Cr 6+ were consistent (0.89 µM). The above results revealed that the model based on σ Con and soft-hard ion grouping methods can achieve good predictive effects for metal(loid) ecotoxicity. Discussion The 19 metal(loid) ions tested showed significant differences in toxicity to lettuce roots, with {EC 50 } values in the order of magnitude range of 10 − 2 -10 3 . Some ions, such as Ag + and Se 6+ , were highly toxic to lettuce roots, with {EC 50 } of 0.05 µM and 0.15 µM, respectively. The highly toxic effects of Ag + have been widely reported in aquatic and soil organisms. For example, Morgan and Wood ( 2004 ) and Le et al. ( 2013 ) tested the toxicity of Ag to rainbow trout ( Oncorhynchus mykiss ) and lettuce ( Lactuca sativa ) and found that 0.03 µM and 0.13 µM Ag could inhibit 50% of growth, respectively. High toxicity of Ag to plant roots can be attributed to its strong binding to the cell wall and inhibition of enzymes required for cell expansion to produce rupturing, etc. (Blamey et al. 2010 ). The toxicity of Se 6+ ({EC 50 } = 0.15 µM) was second only to that of highly toxic Ag + ({EC 50 } = 0.05 µM). Since Se can replace S in the amino acid cysteine and methionine, its toxicity in root cells may be related to alterations in protein biosynthesis, structure and function (Van Hoewyk 2013 ). There are often differences in toxicity and toxicity symptoms for different valences of an element. In the present study, we observed differences in the toxic effects and symptoms of different valences of As on lettuce. The toxicity of As 3+ to lettuce roots ({EC 50 } = 2.75 µM) was slightly higher than that of As 5+ ({EC 50 } = 5.87 µM), which was consistent with the results obtained by SEM observation (Fig. 1 b-c). However, Kopittke et al. ( 2012 ) obtained different results in research on the toxicity of different valences of As to cowpea ( Vigna unguiculata ) roots, finding that 3.6 µM As 3+ and 0.9 µM As 5+ could reduce the elongation of cowpea roots by 50%. Difference between the results of the two studies may be due to the different plants tested. Nonetheless, both studies showed differences in the toxicity of the variable-valence element As, which could be attributed to its different phytotoxicity mechanisms. Studies have shown that the phytotoxicity of As 3+ [absorbed through the silicic acid transport system (Asher and Reay 1979 ; Ma et al. 2008 )] is due to its reaction with dithiol groups on proteins and inhibition of enzyme reactions requiring free sulfhydryl groups (Horswell and Speir 2006 ). On the contrary, As 5+ competes with phosphate and is absorbed through phosphate transporters (Asher and Reay 1979 ; Zhao et al. 2009 ). Similarly, the phytotoxicity and toxicity symptoms of different valences of Se were also quite different (Table 1 , Fig. 1 d-e). There is a high affinity between Se 6+ and sulfate transporters, which is conducive to absorption and transport (Zhang et al. 2003 ). However, Se 4+ is transported through the cytoplasm, and phosphate transporters are involved in this process (Hopper and Parker 1999 ; Li et al. 2008 ). Macroelements, such as Ca and Mg, are essential nutrients for plant growth and development and play an important role in a series of physiological and biochemical reactions. These elements are generally not toxic and might even compete with toxic ions for bioactive sites, so as to reduce the toxicity of the toxic ions (Kopittke et al. 2012 ). However, when their concentration is high or exceeds the required concentration for plant growth, these ions will inhibit plant growth and produce toxic effects. Kopittke et al. ( 2011 ) found that approximately 14000 µM Mg 2+ inhibits cowpea root elongation by 50%, suggesting that the toxicity of Mg is much lower than that of other elements such as Ag and Cu. Compared with previous studies (Meng et al. 2019a ), the QICAR equations established in the present study on the basis of relationships between toxicity and ionic characteristics of 19 metal(loid)s have a poor predictive effect on toxicity in lettuce ( R 2 Adj = 0.238–0.503, p < 0.05). This may be due to the large variety of metal(loid)s and the large differences in physicochemical properties of elements, resulting in different dose-responses between organisms and individual elements (Luo et al. 2021 ; Zamil et al. 2009 ). Hence, classification of elements with similar properties may improve the predictive performance of the model. Soft-hard acid-base theory (HSAB) was developed on the basis of Lewis acid-base theory, which divides ions into soft and hard categories according to the properties of elements, where \"soft\" refers to those ions with larger radius, higher polarizability and lower charge density, while hard ions have the opposite properties (Pearson 1963 , 1968 ; Pearson and Mawby 1967 ). On the basis of QICAR, both the relationships between toxicity and ion characteristics and toxicity prediction effects were significantly improved after ions were classified following the HSAB theory. The toxicity of soft ions showed significant correlations with 7 of the 23 physicochemical properties (D, Z, IP, Z/AR, Z/r, |log(-KOH)|, Z 2 /r; p < 0.05); D showed the largest correlation coefficient with log{EC 50 }, making it the best variable for establishing the toxicity prediction equation. D is density, a physical property of metal(loid) ions. In general, periodic variation in density is determined by atomic weight, atomic volume and lattice type (Enache et al. 2003 ). Other properties, such as charge Z, play an important role in the interaction between soft ions and soft ligands as soft receptors usually exhibit low charge (Pearson 1963 , 1968 ). The toxicity of hard ions showed a significant correlation with only one property (AR/AW). AR/AW is the ratio of atomic radius to atomic weight, which characterizes the electron density of metal(loid) ions. Generally, the charge density of the acceptor and donor is the main factor in the interaction between hard ions and hard ligands (Ahrland 1968 ). However, due to the reduction in the species and number of metal(loid) elements after grouping, higher R 2 values ​​obtained using the soft-hard ion grouping method of QICAR should be further verified using more elements. The soft-hard ion grouping led to a reduction in the number of samples, and we could not establish a unified QICAR method. We therefore further explored the relationships between ion toxicity and the three ligand-binding constants (σ Con , HLScale and log K ) that characterize the binding affinity of metal(loid)s to ligands. Each parameter had different prediction effects on metal(loid) phytotoxicity. σ Con showed a strong correlation with toxicity ( p < 0.001), with the best fitting effect ( R 2 Adj = 0.844), indicating that ion softness is a key factor in evaluating and predicting metal(loid) toxicity to lettuce roots and that interactions between soft ions and soft ligands play a major role in metal(loid) toxicity to lettuce. Similarly, Kinraide ( 2009 ) explored the relationship between metal(loid) toxicity and σ Con and found that the interaction of charge (Z) and σ Con achieved good toxicity prediction effects ( R 2 = 0.923); however, this was limited to low-valent elements (Z ≤ 3). By contrast, toxicity was not significantly correlated with log K or HLScale ( p > 0.05). Customarily, log K is directly proportional to EC value (Meng et al. 2019a ). However, log K values of some ions such as Cu 2+ and Ag + in the present study corresponded to {EC 50 } in the opposite relationship (Table 1 ). A possible reason for this trend was that log K values were obtained from previously published literature, while {EC 50 } values were derived from our experiments; the lettuce varieties used in the studies of these two types of data may be different, thereby resulting in a non-corresponding relationship between log K and toxicity for some elements. However, when Cu 2+ was removed, log K was significantly correlated with toxicity and showed a better toxicity prediction effect ( R 2 Adj = 0.767, p = 0.033). In addition, the log K method currently available in the published literature involves limited metal species and toxicity data. There are few studies on the evaluation and prediction of lettuce toxicity using BLM. We could only obtain log K values of six metal elements (Table 1 ). Therefore, obtaining the log K of more elements will be more meaningful for modeling. A poor correlation was shown between HLScale and toxicity, which may be caused by differences in metal(loid) phytotoxicity mechanism. Metal cations binding to hard ligands, regarded as a common mechanism, results in direct toxicity through inhibiting the controlled loosening of cell walls (Kopittke et al. 2011 ). Nevertheless, this mechanism may not work for all ions; that is to say, the binding strength of the ion to the hard ligand may not be dominant in all ion-induced toxicity. For example, the soft ion Ag + tends to bind strongly to RS- functional groups (soft ligands) in metallothionein to exert toxic effects (Bell et al. 2002 ), independent of the binding strength of hard ions (Kinraide 2009 ). Nevertheless, whether this applies to more plant species or elements requires further study. Conclusion In the current study, we investigated the toxic effects of different metal(loid) ions on lettuce using hydroponic culture and explored the potential predictability of the QICAR model for phytotoxicity using methods such as soft-hard ion grouping and ligand-binding theory (σ Con , HLScale and log K ). The 19 metal(loid) ions tested showed significant toxicity differences to lettuce roots, with differences in {EC 50 } greater than four orders of magnitude (0.05 µM -953.16 µM). Some ions, such as Ag + , were highly toxic to lettuce roots, while others such as Mg 2+ and Sb 5+ showed weaker toxic effects. Correlation and linear regression analysis between metal(loid) toxicity and ionic characteristics revealed that the method of classifying metals into soft and hard ions could significantly improve the interpretation of physicochemical properties to toxicity variation. Of the three ligand-binding constants, σ Con showed the closest relationship with toxicity and provided the best predictive effect. Declarations Author contribution Luo Xiaorong: Methodology, Validation, Formal analysis, Investigation, Data Curation, Visualization, Writing - Original Draft; Wang Xuedong: Conceptualization, Methodology, Formal analysis, Project administration, Writing - Original Draft, Writing - Review & Editing, Funding acquisition; Xia Cunyan: Investigation, Validation; Peng Jing: Investigation; Wang Ying: Writing - Review; Tang Yujie: Investigation; Gao Fan: Investigation; Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Funding The work was financially supported by the National Natural Science Foundation of China (grant number 41877496). Ethics Statement This study did not involve any animals. 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Ecotox Environ Safe 74: 1036–1042. https://doi.org/10.1016/j.ecoenv.2011.01.021 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial20220506.docx 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-1628233\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":104258569,\"identity\":\"4c87b5ac-32fa-4807-9e62-9292ef160bec\",\"order_by\":0,\"name\":\"Xiaorong Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaorong\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"},{\"id\":104258570,\"identity\":\"59c7d66d-fb7c-413c-9e8e-daadf4fccc14\",\"order_by\":1,\"name\":\"Xuedong Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFCCBBBhA2HzkKAljXQth0nQYnA8x/Bzwa/z8uYSCYwP3rYxyJsT0iLZ88ZYembfbcOdMxKYDee2MRjubCCghV8id4M0b8/tBIMbCWzSvG0MCQYHCGhhk8jd/Ju35xxIC/tvorQAbdkmzfPjANgWZqK0SPa8/2bN25BsuOHMw2bJOeckDDcQ0mJwPC35Ns8fO3mD48kHP7wps5EnaAsYMLaByQYgIUGMehD4Q6zCUTAKRsEoGJEAALFxPwXT2ifjAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xuedong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":104258572,\"identity\":\"67568e05-556e-456d-9023-455cfd28e92e\",\"order_by\":2,\"name\":\"Cunyan Xia\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cunyan\",\"middleName\":\"\",\"lastName\":\"Xia\",\"suffix\":\"\"},{\"id\":104258575,\"identity\":\"64813d9d-9885-4a5a-b710-881221fa176e\",\"order_by\":3,\"name\":\"Jing Peng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jing\",\"middleName\":\"\",\"lastName\":\"Peng\",\"suffix\":\"\"},{\"id\":104258576,\"identity\":\"d33f82c5-19c4-4e98-af07-06be0cbe8a3e\",\"order_by\":4,\"name\":\"Ying Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beihang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ying\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":104258581,\"identity\":\"c5fd4f3d-5371-45d0-9c98-3c708b501a74\",\"order_by\":5,\"name\":\"Yujie Tang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yujie\",\"middleName\":\"\",\"lastName\":\"Tang\",\"suffix\":\"\"},{\"id\":104258582,\"identity\":\"ee876e5f-84f8-4d3a-b2ed-de6f11fa4421\",\"order_by\":6,\"name\":\"Fan Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Capital Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fan\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2022-05-06 05:14:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-1628233/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-1628233/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":21290352,\"identity\":\"334a765c-2b55-43ed-b53e-f9268a5e8522\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:49:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":637956,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScanning electron micrographs of lettuce root tips exposed to different treatment solutions. Lettuce roots tips exposed to the control (a), As\\u003csup\\u003e3+\\u003c/sup\\u003e (b), As\\u003csup\\u003e5+\\u003c/sup\\u003e (c), Se\\u003csup\\u003e4+\\u003c/sup\\u003e (d) and Se\\u003csup\\u003e6+\\u003c/sup\\u003e (e) solution, respectively; magnification ´500\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/3de1c8d6d60ee33301a439e0.png\"},{\"id\":21291166,\"identity\":\"24f6d976-100b-49e3-be27-4715e589f3ca\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:54:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":723947,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelations between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and each of 23 ionic characteristics of metal(loid) ions. ** \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01, *\\u003cem\\u003e p\\u003c/em\\u003e \\u0026lt; 0.05; Pearson correlation\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/6e91e4b864fa47e6e7fe327d.png\"},{\"id\":21291168,\"identity\":\"939042d6-d8b9-4312-b503-040edbe233ed\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:54:51\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":33756,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnivariate linear regression relationships between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} of soft–hard ions and their correlated physicochemical properties. (a-c) Seven properties (D, Z, IP, Z/AR, Z/r, |log (-KOH)|, Z\\u003csup\\u003e2\\u003c/sup\\u003e/r) for soft ions. (d) AR/AW for hard ions\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/a537ec5fc79af3eae84352c9.png\"},{\"id\":21291169,\"identity\":\"58597e43-d2cf-4475-a630-5fcf8b8879a8\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:54:51\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":50650,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnivariate linear relationships between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and ligand-binding constants. (a) The logarithm of the conditional binding constant (log \\u003cem\\u003eK\\u003c/em\\u003e) based on the biotic ligand model theory; (b) softness consensus scale (σ\\u003csub\\u003eCon\\u003c/sub\\u003e); (c) normalized hard ligands scale (HLScale). Deep red and light red shadows represent 95% confidence interval and prediction interval bands, respectively. The mM represents toxicity concentrations of micromolar level\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/1b20f1a51a36125618514f05.png\"},{\"id\":21291628,\"identity\":\"702c79b9-168f-4445-8eda-a909d09b734d\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:59:51\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26017,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationships between observed and predicted log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} derived from soft–hard ion grouping and σ\\u003csub\\u003eCon\\u003c/sub\\u003e methods. The black solid line and the purple dashed lines represent the 1:1 line and 1.5 orders of magnitude difference between predicted and observed toxicity values, respectively. Hollow shapes represent external verification ions (n = 18 for σ\\u003csub\\u003eCon\\u003c/sub\\u003e, n = 11 for hard ions and n = 8 for soft ions)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/2d96bab4c1a54ca8ac11efdd.png\"},{\"id\":21843433,\"identity\":\"45f582af-22bd-4248-b5a9-8da4e24354e7\",\"added_by\":\"auto\",\"created_at\":\"2022-05-24 20:29:20\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1872992,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/9f977fc4-20fe-4e33-bb5d-7a2450bd0cba.pdf\"},{\"id\":21290354,\"identity\":\"d1130b25-10ce-4b6e-a758-675ad2eebcf8\",\"added_by\":\"auto\",\"created_at\":\"2022-05-10 14:49:51\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2206887,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial20220506.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1628233/v1/e893c5649d96694f757607f5.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"QICAR methods for assessing the ecotoxicity of soil metal(loid)s to lettuce\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eWith industrial and agricultural modernization and urbanization, heavy metal pollution in the soil environment has become increasingly prominent. Pollution elements include not only heavy metals such as lead (Pb), cadmium (Cd) and mercury (Hg) that people often pay attention to, but also some uncommon new metal(loid)s such as lanthanum (La), antimony (Sb) and vanadium (V) (Gong et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Ji et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Nevertheless, there is little information about these new pollution elements, including their ecotoxicity, related risks and control standards, which seriously affects the pollution prevention and control work of the legislative management department. In addition, understanding the toxicity and risk information of elements requires many tests, which involve considerable time and cost. Therefore, it is particularly important to develop an element risk prediction model independent of toxicity tests.\\u003c/p\\u003e \\u003cp\\u003eThe quantitative structure-activity relationships (QSAR) model has been widely favored by scholars as a fast, accurate and independent toxicity test. QSAR modeling uses mathematical statistics to describe the internal relationship between the molecular structure of organic compounds and ecotoxicological effects (Hansch and Gao \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e; Karelson et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). Its basic assumption is that bioactivity depends on structural changes in organic compounds, while the structure of compounds can be characterized by various molecular structure characteristic parameters reflecting the structural characteristics (i.e., molecular structure descriptors) (Suh et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Since the United States Environmental Protection Agency (USEPA) began to apply the QSAR method to estimate the thermodynamic properties and toxicity classification of different organic chemicals in 1978, the QSAR model has made considerable progress in predicting the properties and toxicity of organic compounds (Abramenko et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eQSAR was subsequently applied to inorganic elements and was defined as the quantitative ion character-activity relationship (QICAR) by Newman and McCloskey (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). The QICAR is used for predicting metal ecotoxicity according to the relationships between ionic structure characteristics and activity (Newman et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e; Walker et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Since the QICAR model was proposed, it has been widely used in aquatic environments. For instance, Chen et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) applied QICAR for exploring the relationships between the physicochemical properties of 34 metal ions and their acute toxicity to eight marine organisms, finding that the softness index (σp) can appropriately predict the acute toxicity of metals. Li et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) also found good relationships between the toxicity of metals to \\u003cem\\u003eCypris subglobose\\u003c/em\\u003e in freshwater and physicochemical properties such as electronegativity (Xm) using the QICAR method.\\u003c/p\\u003e \\u003cp\\u003eHowever, recent studies have highlighted that significant differences in metal properties may cause different dose-responses of organisms to individual elements, making it difficult to use one or more properties to quantify the relationships between various elements and their ecotoxicity (Chen and Wang \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Zamil et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). The hard-soft acid-base theory (HSAB) provides an effective way to solve this problem. HSAB theory was proposed by Ralph G. Pearson in 1963 based on Lewis acid-base theory (Pearson \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1963\\u003c/span\\u003e). He believed that Lewis acids and bases can be separated into two categories (i.e., soft and hard) according to different properties. \\\"Soft\\\" refers to those ions with a larger radius, higher polarizability and lower charge density, while hard ions have the opposite properties (Pearson \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1963\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e1968\\u003c/span\\u003e; Pearson and Mawby \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e1967\\u003c/span\\u003e). A QICAR model based on hard-soft grouping has been used to predict metal ecotoxicity (Meng et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2019b\\u003c/span\\u003e; Zamil et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Li et al. (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) classified metals into soft, borderline and hard ions based on HSAB theory and found that QICARs developed by borderline plus soft or plus hard ions could accurately predict ecological risk assessment screening values.\\u003c/p\\u003e \\u003cp\\u003eNevertheless, studies related to soil plants have identified hard ligands such as carboxyl groups and soft ligands such as thiol groups in plant roots, and the ability of these ligands to coordinate metals are different (Kopittke et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Hence, there is difficulty in quantifying metal-ligand binding ability by employing a single property of these elements. Kinraide developed the hard ligand scale (HLScale) in 2007 (Kinraide and Yermiyahu \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) and the softness consensus scale (σ\\u003csub\\u003eCon\\u003c/sub\\u003e) in 2009 (Kinraide \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), respectively. The HLScale is the averaged and normalized binding strengths of metals to 13 hard ligands such as oxalic acid, citric acid and carbonate. Kinraide and Yermiyahu (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) concluded that a majority of cations are inclined to bind to hard ligands, and the strength of binding between them correlates well with ion binding to biomass such as plasma membranes. There are some exceptions to this; the toxicities of soft ions such as Tl\\u003csup\\u003e+\\u003c/sup\\u003e and Ag\\u003csup\\u003e+\\u003c/sup\\u003e correlate poorly with the HLScale, suggesting that their binding ability to hard ligands may not be the main factor causing their toxic effects. Therefore, Kinraide (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) also proposed a consensus scale related to ionic softness, i.e., σ\\u003csub\\u003eCon\\u003c/sub\\u003e; σ\\u003csub\\u003eCon\\u003c/sub\\u003e has a significantly positive correlation with metals binding to soft ligands, and the interaction of σ\\u003csub\\u003eCon\\u003c/sub\\u003e and charge (Z) can predict toxicity well (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.923) (Kinraide \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Although both HLScale and σ\\u003csub\\u003eCon\\u003c/sub\\u003e show good performance in element toxicity prediction, some ions such as Ag\\u003csup\\u003e+\\u003c/sup\\u003e still perform poorly. Moreover, these predictive results also require further verification with different plants.\\u003c/p\\u003e \\u003cp\\u003eRecently, some scholars have also linked QICAR to the binding constant (\\u003cem\\u003eK\\u003c/em\\u003e) of the biotic ligand model (BLM) to explore the relationship between phytotoxicity and metal-ligand binding ability (Li et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Meng et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019a\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhou et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). BLM assumes that bioactive sites are bioligands (BLs), which may be physiologically active sites resulting in direct responses or transport sites resulting in indirect responses. In BLM, toxicity is determined by the affinity of ions binding to the BLs (expressed as conditional binding constant, \\u003cem\\u003eK\\u003c/em\\u003e); that is to say, toxicity will occur when ions binding to BLs reach a certain amount. However, this law between toxicity and \\u003cem\\u003eK\\u003c/em\\u003e has not been verified for additional elements and plant species.\\u003c/p\\u003e \\u003cp\\u003eThe objectives of the present study were to 1) explore the toxicity of a large number of metal(loid) ions to typical crops in hydroponic culture; 2) correlate toxicity with various physicochemical properties of metal(loid)s and quantify the relationships between them, and consider the impact of metal(loid) ion classification on these relationships; 3) investigate the relationships between toxicity and metal-ligand binding affinity (HLScale and σ\\u003csub\\u003eCon\\u003c/sub\\u003e, and BLM-based \\u003cem\\u003eK\\u003c/em\\u003e); 4) develop QICARs for predicting the toxicity threshold of data-poor metal(loid) elements to plants. Our results are of great significance for obtaining ecotoxicity and risk information for elements contaminating the soil.\\u003c/p\\u003e\"},{\"header\":\"Materials And Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eExperimental setup\\u003c/h2\\u003e \\u003cp\\u003eA 4-day acute toxicity test was conducted using lettuce (\\u003cem\\u003eLactuca sativa\\u003c/em\\u003e L., cv. Italian Lettuce) in hydroponic culture with a simplified nutrient solution. Each solution treatment group contained one of 19 metal(loid) ions: Ag\\u003csup\\u003e+\\u003c/sup\\u003e, Cu\\u003csup\\u003e2+\\u003c/sup\\u003e, Zn\\u003csup\\u003e2+\\u003c/sup\\u003e, Ni\\u003csup\\u003e2+\\u003c/sup\\u003e, Co\\u003csup\\u003e2+\\u003c/sup\\u003e, Cd\\u003csup\\u003e2+\\u003c/sup\\u003e, Mg\\u003csup\\u003e2+\\u003c/sup\\u003e, La\\u003csup\\u003e3+\\u003c/sup\\u003e, Sc\\u003csup\\u003e3+\\u003c/sup\\u003e, Cr\\u003csup\\u003e3+\\u003c/sup\\u003e, Cr\\u003csup\\u003e6+\\u003c/sup\\u003e, As\\u003csup\\u003e3+\\u003c/sup\\u003e, As\\u003csup\\u003e5+\\u003c/sup\\u003e, Se\\u003csup\\u003e4+\\u003c/sup\\u003e, Se\\u003csup\\u003e6+\\u003c/sup\\u003e, Sb\\u003csup\\u003e3+\\u003c/sup\\u003e, Sb\\u003csup\\u003e5+\\u003c/sup\\u003e, Ⅴ\\u003csup\\u003e5+\\u003c/sup\\u003e, Mo\\u003csup\\u003e6+\\u003c/sup\\u003e. Each treatment group was set up with seven concentration levels and one control, with three biological replicates per group, yielding a total of 456 treatments. The concentration range of metal(loid) ions in the above solutions was determined using a pre-experiment to ensure non-toxic to lethal concentrations (Table S1, Supplementary material).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTest solution\\u003c/h2\\u003e \\u003cp\\u003eThe basal culture solution for 19 metal(loid)s was 0.2 mM Ca prepared in deionized water, where the basal Ca was supplied as CaCl\\u003csub\\u003e2\\u003c/sub\\u003e for 18 metal(loid)s and as Ca(NO\\u003csub\\u003e3\\u003c/sub\\u003e)\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026sdot;4H\\u003csub\\u003e2\\u003c/sub\\u003eO for Ag. 2-(\\u003cem\\u003eN\\u003c/em\\u003e-Morpholino)-ethanesulfonic acid (MES) was used as a butter to modulate pH to approximately pH 6.0. All solutions were equilibrated for 1 d before use.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eToxicity bioassays\\u003c/h2\\u003e \\u003cp\\u003e Lettuce root elongation tests were conducted according to the standardized protocol in ISO 11269-1: 2012. Sterilized and thoroughly rinsed lettuce seeds were transferred to an incubator for 48 h of pre-germination at 25 ℃, 80% humidity, and no light irradiation. Seedlings with a length of approximately 1 cm were transferred to a plastic pot filled with 250 mL of test solution and fixed using a nylon net. Six seedlings were introduced into each pot, and all the culture pots were randomly placed in the incubator chamber (temperature regime of 20/18 ℃ day/night; photoperiod of 16/8 h light/dark; photon flux density of approximately 90 \\u0026micro;mol\\u0026sdot;m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e\\u0026sdot;s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). During the experiment, the test solutions were refreshed every 2 days.\\u003c/p\\u003e \\u003cp\\u003eRoot length of each seedling was measured after 4 days of exposure. The relative net root elongation (RNE %) was calculated as follows (Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)):\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\text{R}\\\\text{N}\\\\text{E (\\\\%) = }\\\\frac{\\\\text{R}{\\\\text{E}}_{\\\\text{t}}\\\\text{-}{\\\\text{RE}}_{\\\\text{0}}}{{\\\\text{RE}}_{\\\\text{c}}\\\\text{-}\\\\text{R}{\\\\text{E}}_{\\\\text{0}}}\\\\text{\\u0026times;10}\\\\text{0}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cem\\u003eRE\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003et\\u003c/em\\u003e\\u003c/sub\\u003e (cm) is the average root elongation of six seedlings exposed to the metal(loid) ion treatment; \\u003cem\\u003eRE\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ec\\u003c/em\\u003e\\u003c/sub\\u003e (cm) is the average root elongation of the control group; \\u003cem\\u003eRE\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e (cm) is the root elongation before exposure (i.e., 1 cm).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMicroscopic examination of root tips\\u003c/h2\\u003e \\u003cp\\u003eRoot tip samples for scanning electron microscope (SEM) examination were prepared following the method of Xu et al. (\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Approximately 1 cm of lettuce root tip was immersed in a cell culture plate filled with \\u003cem\\u003etert\\u003c/em\\u003e-butanol, which was wrapped with sealing film and aluminum foil and transferred to a refrigerator (-20\\u0026deg;C) for 12 h. Subsequently, lettuce root tips were lyophilized at -20\\u0026deg;C, 14 Pa (LGJ-18S; Beijing, China). After being coated with Au using an ion sputterer (JEOL JFC-1100; Tokyo, Japan), lettuce root tips were examined with a field emission SEM (Hitachi S4800, Japan).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChemical measurements\\u003c/h2\\u003e \\u003cp\\u003eTest solution pH was measured using a pHS-3C precision acidity meter (INESA; Shanghai, China). Actual concentrations of metal(loid) ions in the test solutions were determined using an inductively coupled plasma mass spectrometer (ICP-MS, USA-Agilent-Agilent 7700/7800). For quality-control purposes, a single-element standard reagent and a reagent blank were tested every 20 samples, and the internal standard recovery of each element was 100%. Free metal(loid) ion activities in solutions were measured using the speciation software Visual MINTEQ 3.1 (An et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Gustafsson \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) with solution pH, temperature and ion concentrations as input variables.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData source and modeling\\u003c/h2\\u003e \\u003cp\\u003eThe log-logistic function (Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e)) was applied to fit the dose-effect relationship between free metal(loid) ions activity and RNE, which allowed calculation of the dose inhibiting 50% of root elongation (EC\\u003csub\\u003e50\\u003c/sub\\u003e).\\u003cdiv id=\\\"Equ2\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ2\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\text{R}\\\\text{N}\\\\text{E (\\\\%)}\\\\text{ =}\\\\frac{{\\\\text{Y}}_{\\\\text{0}}}{\\\\text{1+}{\\\\text{e}}^{\\\\text{b}\\\\left(\\\\text{X}\\\\text{-}\\\\text{M}\\\\right)}}\\\\text{\\u0026times;100}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e2\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cem\\u003eX\\u003c/em\\u003e is free ion activity of metal(loid)s (\\u0026micro;M), and \\u003cem\\u003eY\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e, \\u003cem\\u003eb\\u003c/em\\u003e and \\u003cem\\u003eM\\u003c/em\\u003e (lg {EC\\u003csub\\u003e50\\u003c/sub\\u003e}) are the fitting constants.\\u003c/p\\u003e \\u003cp\\u003eTwenty-three significant physicochemical properties of metal(loid)s were chosen, as reported in previous studies or found in the \\u003cem\\u003eHandbook of Chemistry and Physics\\u003c/em\\u003e (Le Faucheur et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Rumble \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Walker et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). The values of properties such as boiling point (BP), melting point (MP), density (D) and difference in ionization potentials between the ion oxidation numbers OX and OX\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (ΔIP) (Kaiser \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1980\\u003c/span\\u003e) were obtained from Lide and Haynes (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e); atomic mass (AW), atomic number (AN), atomic radius (AR), Pauling ionic radius (r), ionization potential (IP), electrochemical potential (ΔE\\u003csub\\u003e0\\u003c/sub\\u003e) (Kaiser \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1980\\u003c/span\\u003e; Newman and McCloskey \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e), electronegativity (Xm), and ionic charge (Z) were obtained from Wolterbeek and Verburg (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e); logarithmic of first hydrolysis constant (|log(-K\\u003csub\\u003eOH\\u003c/sub\\u003e)|), covalent radius (CR) and softness index (σp) were from Base and Mesmer (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e1976\\u003c/span\\u003e), Shannon and Prewitt (\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e1970\\u003c/span\\u003e) and Pearson and Mawby (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e1967\\u003c/span\\u003e), respectively; electron density (AR/AW) (Base and Mesmer \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e1976\\u003c/span\\u003e), covalent index (Xm\\u003csup\\u003e2\\u003c/sup\\u003er), polarization force parameters (Z\\u003csup\\u003e2\\u003c/sup\\u003e/r, Z/r\\u003csup\\u003e2\\u003c/sup\\u003e, Z/r), atomic ionization potential (AN/ΔIP) and similar polarization force parameters (Z/AR, Z/AR\\u003csup\\u003e2\\u003c/sup\\u003e) were derived by calculation. Detailed values of these physicochemical properties are shown in Table S2.\\u003c/p\\u003e \\u003cp\\u003eThree constants (HLscale, σ\\u003csub\\u003eCon\\u003c/sub\\u003e and BLM-based log \\u003cem\\u003eK\\u003c/em\\u003e) characterizing the metal(loid)-ligand binding affinity were selected in addition to the above physicochemical properties. Values of log \\u003cem\\u003eK\\u003c/em\\u003e were derived from published studies using analogous test species and experimental conditions to the present study (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e); values of σ\\u003csub\\u003eCon\\u003c/sub\\u003e and HLScale were derived from Kinraide (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eInternal connections between metal(loid) toxicity and ionic characteristics, HLScale, σ\\u003csub\\u003eCon\\u003c/sub\\u003e and log \\u003cem\\u003eK\\u003c/em\\u003e were analyzed on the basis of the QICAR model principle, respectively. These relationships were quantified using univariate linear regression. To lower the self-correlation between metal(loid) ion characteristics, principal component analysis (PCA), a dimension-reduction statistical method, was employed to create a new group of predictive variables, termed principal components (PCs). Generally, relatively few PCs can show most of the total variability of the dataset. Therefore, multivariate linear relationships based on PCA were also established.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eF\\u003c/em\\u003e and \\u003cem\\u003ep\\u003c/em\\u003e values were obtained from an analysis of variance to evaluate the magnitude of correlation between toxicity and characteristics; adjusted determination coefficient (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e) and root-mean-square error (\\u003cem\\u003eRMSE\\u003c/em\\u003e) were used to test the goodness-of-fit of equations. An optimal QICAR equation was selected by considering the highest \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e and \\u003cem\\u003eF\\u003c/em\\u003e-statistic with \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, and the lowest \\u003cem\\u003eRMSE\\u003c/em\\u003e. Correlation statistics and model building were conducted using SPSS version 24.0 (IBM, NY, USA).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eValidation of the QICAR model\\u003c/h2\\u003e \\u003cp\\u003eVerification of the model was performed by self-verification and external verification methods. The external verification data set were not involved in the modeling. Sc\\u003csup\\u003e3+\\u003c/sup\\u003e and Se\\u003csup\\u003e4+\\u003c/sup\\u003e were used for external verification of soft-hard ion grouping modeling, while La\\u003csup\\u003e3+\\u003c/sup\\u003e, Sc\\u003csup\\u003e3+\\u003c/sup\\u003e and Se\\u003csup\\u003e4+\\u003c/sup\\u003e were used for external verification of the σ\\u003csub\\u003eCon\\u003c/sub\\u003e method.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDose-response relationships\\u003c/h2\\u003e \\u003cp\\u003eThe fitted dose-response curves for 19 metal(loid) exposures are shown in Fig. S1. With an increase of free ion activity, RNE decreased gradually, indicating that these metal(loid) ions produced obvious inhibition of lettuce root growth. The {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values of 19 metal(loid)s to lettuce, fitted by dose-response curves, varied from 0.05 \\u0026micro;M (Ag\\u003csup\\u003e+\\u003c/sup\\u003e) to 953.16 \\u0026micro;M (Sb\\u003csup\\u003e5+\\u003c/sup\\u003e), with a difference of 19063.20 times between the highest and lowest values, greater than four orders of magnitude (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe only ion with {EC\\u003csub\\u003e50\\u003c/sub\\u003e} in the range of 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e \\u0026ndash; 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e was Ag\\u003csup\\u003e+\\u003c/sup\\u003e, indicating that Ag\\u003csup\\u003e+\\u003c/sup\\u003e causes the most obvious inhibition to lettuce roots. Ions with {EC\\u003csub\\u003e50\\u003c/sub\\u003e} in the range 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e \\u0026ndash; 10\\u003csup\\u003e0\\u003c/sup\\u003e comprised Se\\u003csup\\u003e6+\\u003c/sup\\u003e, Cu\\u003csup\\u003e2+\\u003c/sup\\u003e, Ni\\u003csup\\u003e2+\\u003c/sup\\u003e, La\\u003csup\\u003e3+\\u003c/sup\\u003e, Co\\u003csup\\u003e2+\\u003c/sup\\u003e, Sc\\u003csup\\u003e3+\\u003c/sup\\u003e and Cr\\u003csup\\u003e6+\\u003c/sup\\u003e; the lower {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values (0.15 \\u0026micro;M-0.89 \\u0026micro;M) revealed that these ions are also highly toxic to lettuce. Meanwhile, ions with {EC\\u003csub\\u003e50\\u003c/sub\\u003e} in the range 10\\u003csup\\u003e0\\u003c/sup\\u003e \\u0026ndash; 10\\u003csup\\u003e1\\u003c/sup\\u003e accounted for the largest proportion, with nine metal(loid) ions ranked in descending order of their toxicity as follows: Cd\\u003csup\\u003e2+\\u003c/sup\\u003e, Cr\\u003csup\\u003e3+\\u003c/sup\\u003e, Mo\\u003csup\\u003e6+\\u003c/sup\\u003e, Zn\\u003csup\\u003e2+\\u003c/sup\\u003e, As\\u003csup\\u003e3+\\u003c/sup\\u003e, Sb\\u003csup\\u003e3+\\u003c/sup\\u003e, V\\u003csup\\u003e5+\\u003c/sup\\u003e, As\\u003csup\\u003e5+\\u003c/sup\\u003e and Se\\u003csup\\u003e4+\\u003c/sup\\u003e ({EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 1.21 \\u0026micro;M-7.67 \\u0026micro;M). In the range of 10\\u003csup\\u003e2\\u003c/sup\\u003e \\u0026ndash; 10\\u003csup\\u003e3\\u003c/sup\\u003e, the larger {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values of Mg\\u003csup\\u003e2+\\u003c/sup\\u003e (804.44 \\u0026micro;M) and Sb\\u003csup\\u003e5+\\u003c/sup\\u003e (953.16 \\u0026micro;M) indicated lower toxicity than the other ions tested. In sum, there was great variation in the toxicity of 19 metal(loid) ions, indicating that these ions have different toxic effects on lettuce.\\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\\u003e{EC\\u003csub\\u003e50\\u003c/sub\\u003e}, log \\u003cem\\u003eK\\u003c/em\\u003e, σ\\u003csub\\u003eCon\\u003c/sub\\u003e and HLScale values for different metal(loid) ions. {} refers to free ion activity\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIon\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e{EC\\u003csub\\u003e50\\u003c/sub\\u003e} \\u0026micro;M\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003elog \\u003cem\\u003eK\\u003c/em\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eσ\\u003csub\\u003eCon\\u003c/sub\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eHLScale \\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAg\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.05\\u0026nbsp;{0.04\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.06}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.28\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSe\\u003csup\\u003e6+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.15 {0.07\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.38}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e0.19\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCu\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.27 {0.21\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.35}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNi\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.32 {0.21\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.48}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003csup\\u003e*\\u003c/sup\\u003eLa\\u003csup\\u003e3+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.53\\u0026nbsp;{0.34\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.83}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCo\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.74 {0.34\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.60}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003csup\\u003e*\\u003c/sup\\u003eSc\\u003csup\\u003e3+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.81\\u0026nbsp;{0.38\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.72}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCr\\u003csup\\u003e6+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.89 {0.33\\u0026thinsp;\\u0026minus;\\u0026thinsp;2.40}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e0.27\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCd\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.21 {0.74\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.98}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCr\\u003csup\\u003e3+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.80\\u0026nbsp;{1.19\\u0026thinsp;\\u0026minus;\\u0026thinsp;2.74}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMo\\u003csup\\u003e6+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.96 {0.70\\u0026thinsp;\\u0026minus;\\u0026thinsp;5.45}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e0.43\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eZn\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.33 {1.33\\u0026thinsp;\\u0026minus;\\u0026thinsp;4.08}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAs\\u003csup\\u003e3+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.75\\u0026nbsp;{1.90\\u0026thinsp;\\u0026minus;\\u0026thinsp;3.97}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026minus;0.16\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSb\\u003csup\\u003e3+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.44\\u0026nbsp;{2.48\\u0026thinsp;\\u0026minus;\\u0026thinsp;4.79}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026minus;0.04\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eV\\u003csup\\u003e5+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.87 {3.97\\u0026thinsp;\\u0026minus;\\u0026thinsp;8.68}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003cem\\u003e0.13\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAs\\u003csup\\u003e5+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.87\\u0026nbsp;{4.55\\u0026thinsp;\\u0026minus;\\u0026thinsp;7.59}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003cem\\u003e0.16\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e Se\\u003csup\\u003e4+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7.67\\u0026nbsp;{4.48\\u0026thinsp;\\u0026minus;\\u0026thinsp;13.13}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e0.00\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMg\\u003csup\\u003e2+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e804.44{584.22\\u0026thinsp;\\u0026minus;\\u0026thinsp;1107.69}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.86\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026minus;1.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;0.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSb\\u003csup\\u003e5+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e953.16{833.79\\u0026thinsp;\\u0026minus;\\u0026thinsp;1089.62}\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026minus;0.01\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Log \\u003cem\\u003eK\\u003c/em\\u003e was derived from Le et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), Liu et al. (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) and Qiu et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e σ\\u003csub\\u003eCon\\u003c/sub\\u003e values were derived from Kinraide (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), if available. Otherwise, they were calculated using the formula σ\\u003csub\\u003eCon\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.0454D\\u0026thinsp;+\\u0026thinsp;E\\u003csub\\u003e0\\u003c/sub\\u003e * 0.067I\\u003csub\\u003eP\\u003c/sub\\u003e and are printed in italics, where D is density, E\\u003csub\\u003e0\\u003c/sub\\u003e is standard electrode potential and I\\u003csub\\u003ep\\u003c/sub\\u003e is the first ionization potential [Barbalace (2008) Environmental Chemistry \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://environmentalchemistry.com\\u003c/span\\u003e\\u003cspan address=\\\"http://environmentalchemistry.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e].\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e HLScale values were taken from Kinraide (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e These ions were used for external verification.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eToxic symptoms of metal(loid) ions on lettuce roots\\u003c/h2\\u003e \\u003cp\\u003eSince metal(loid) toxicity to lettuce varies greatly, we observed the ultrastructure of lettuce roots treated with these pollution elements to help clarify the reasons for metal(loid) toxicity. We selected element treatment groups with variable valences (As\\u003csup\\u003e3+\\u003c/sup\\u003e, As\\u003csup\\u003e5+\\u003c/sup\\u003e, Se\\u003csup\\u003e4+\\u003c/sup\\u003e and Se\\u003csup\\u003e6+\\u003c/sup\\u003e) that displayed obvious toxicity differences to lettuce and used scanning electron microscopy to examine the damage to root tips in comparison with the control group when these ions inhibited 60% of root elongation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Root tip cells and epidermal cells of control lettuce root samples were intact (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). For variable-valence element As, As\\u003csup\\u003e3+\\u003c/sup\\u003e-treated lettuce root tips were small and loose, whereas As\\u003csup\\u003e5+\\u003c/sup\\u003e-treated root tips were curved with no obvious ruptures (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb-c). The difference in toxicity symptoms of As to lettuce roots corresponded to that in toxicity effects of As on lettuce; that is, the toxicity of As\\u003csup\\u003e3+\\u003c/sup\\u003e to lettuce was stronger than that of As\\u003csup\\u003e5+\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Similarly, Se\\u003csup\\u003e6+\\u003c/sup\\u003e treatment destroyed lettuce root caps while Se\\u003csup\\u003e4+\\u003c/sup\\u003e had no obvious effect on lettuce root tips (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed-e). Overall, these root symptoms confirmed root cell damage caused by metal(loid) ions, while elements with variable valence caused different toxicity symptoms in lettuce roots, suggesting that they have different mechanisms of toxicity to lettuce.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eModeling based on toxicity and ionic characteristics of 19 metal(loid)s\\u003c/h2\\u003e \\u003cp\\u003eThe toxicity of metal(loid)s to lettuce was expressed as log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}. Correlation analysis between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and 23 characteristics identified five properties significantly correlated with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). These characteristics were AR/AW, D, σp, Xm\\u003csup\\u003e2\\u003c/sup\\u003e/r and ΔE\\u003csub\\u003e0\\u003c/sub\\u003e in descending order of correlation (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.732\\u0026thinsp;~\\u0026thinsp;0.538, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001\\u0026thinsp;~\\u0026thinsp;0.032). Among them, D and Xm\\u003csup\\u003e2\\u003c/sup\\u003e/r were negatively correlated with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}, while AR/AW, σp and ΔE\\u003csub\\u003e0\\u003c/sub\\u003e showed positive correlations with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}, indicating that metal(loid) toxicity to lettuce increased with increasing D and Xm\\u003csup\\u003e2\\u003c/sup\\u003e/r, but decreased with increasing AR/AW, σp and ΔE\\u003csub\\u003e0\\u003c/sub\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe univariate linear relationships and regression equations established by log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and the above five characteristics with \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 are shown in Fig. S2 and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, respectively. AR/AW had the best linear relationship with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} but could only explain 50.3% of the toxicity variation (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.503, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eGiven the self-correlation between the ionic characteristics of metal(loid)s, we further investigated the multivariate linear relationships between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and characteristics using the PCA method (Table S3). Results showed that the cumulative contribution rate of new independent variables (PCx) with different combinations of properties created by PCA was more than 80%, with PCx1 involving D and σp showing the best multivariate relationship (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.460, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002). However, by comparing \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e, \\u003cem\\u003eRMSE\\u003c/em\\u003e, \\u003cem\\u003eF-\\u003c/em\\u003estatistics and \\u003cem\\u003ep-\\u003c/em\\u003evalue, there was no significant difference between PCA-based multivariate linear relationships and univariate linear relationships.\\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\\u003eUnivariate linear regression equations between log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} and physicochemical properties with \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e is the coefficient of determination, \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e is the adjusted coefficient of determination, \\u003cem\\u003eRMSE\\u003c/em\\u003e and \\u003cem\\u003ep\\u003c/em\\u003e are the root-mean-square error and the statistical level of significance, respectively\\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\\u003eType\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRegression equation\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAdjR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eRMSE\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eF\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ep\\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 \\u003cp\\u003eMetal(loid) ions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 46.750 AR/AW \\u0026ndash; 0.996\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.503\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e16.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = -0.295 D\\u0026thinsp;+\\u0026thinsp;2.283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.492\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.456\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13.563\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 21.682 σp \\u0026ndash; 2.416\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.334\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.517\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = -0.605 Xm\\u003csup\\u003e2\\u003c/sup\\u003e/r\\u0026thinsp;+\\u0026thinsp;1.581\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.292\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.241\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.765\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.031\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.700 ΔE\\u003csub\\u003e0\\u003c/sub\\u003e \\u0026ndash; 0.316\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.289\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.238\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.692\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.032\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoft ions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003elog {EC\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e50\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e} = -0.371 D\\u0026thinsp;+\\u0026thinsp;2.899\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.822\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.793\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.29\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e27.756\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.843 Z \\u0026ndash; 1.925\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.734\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.690\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e16.566\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.081 IP \\u0026ndash; 1.670\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.635\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.574\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10.418\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.916 Z/AR \\u0026minus;\\u0026thinsp;1.431\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.571\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.030\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.378 Z/r \\u0026ndash; 1.225\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.569\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.498\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.030\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = -0.152 |log(-KOH)| + 1.078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.542\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.466\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.099\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003elog {EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.1 Z\\u003csup\\u003e2\\u003c/sup\\u003e/r \\u0026ndash; 0.817\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.542\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.466\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.105\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHard ions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003elog {EC\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e50\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e} = 52.385 AR/AW \\u0026ndash; 1.093\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.815\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.784\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.52\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e26.365\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eModeling based on toxicity and characteristics of soft-hard ions\\u003c/span\\u003e \\u003c/p\\u003e \\u003cp\\u003eConsidering that differences in the ionic characteristics of metal(loid)s may lead to differences in toxicity, these elements were separated into two groups according to the HSAB theory: soft and hard (Pearson and Mawby, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e1967\\u003c/span\\u003e). Correlation and linear regression between toxicity and characteristics of soft-hard ions were further analyzed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Fig. S3, Table S3).\\u003c/p\\u003e \\u003cp\\u003eCorrelation analysis (Fig. S3) revealed seven properties (D, Z, IP, Z/AR, Z/r, |log (-KOH)|, Z\\u003csup\\u003e2\\u003c/sup\\u003e/r) among the 23 ionic characteristics that were significantly correlated with the toxicity of soft ions (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Except for D and |log(-KOH)| (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), the other characteristics showed positive correlations with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}. With regard to hard ions, AR/AW was strongly positively correlated with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}, with \\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.903 and \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002 (Fig. S3). At the same significance level (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), the correlation coefficient \\u003cem\\u003er\\u003c/em\\u003e between the toxicity of soft-hard ions and characteristics was larger than that when all metal(loid) ions tested were considered, indicating that the relationship between toxicity and ionic characteristics was improved after classifying ions into soft and hard.\\u003c/p\\u003e \\u003cp\\u003eWe established univariate and multivariate linear relationships of soft and hard ions, respectively. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Table S3). Univariate linear regression analysis showed that D and AR/AW exhibited the best fitting with the toxicity of soft ions and hard ions, respectively, explaining up to 79.3% of toxicity variation in soft ions (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.793, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002) and 78.4% of toxicity variation in hard ions (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.784, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The high \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e values of soft-hard ions indicated that the method of grouping ions following the HSAB theory could significantly improve the linear regression prediction of metal(loid) toxicity to lettuce roots.\\u003c/p\\u003e \\u003cp\\u003eThe multivariate linear relationship for soft ions (Table S3) showed similar results. However, the comparison of \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003eRMSE\\u003c/em\\u003e, \\u003cem\\u003eF\\u003c/em\\u003e-statistics and \\u003cem\\u003ep\\u003c/em\\u003e-values showed no significant difference between univariate and multivariate linear relationships. Considering that the regression relationship based on multiple variables may lead to large errors and autocorrelations, we selected the univariate linear relationship of soft ions with D, and that of hard ions with AR/AW to establish the optimal QICAR prediction equations (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003eModeling based on metal(loid) toxicity and ligand-binding constants\\u003c/h2\\u003e \\u003cp\\u003eLigands in plant roots are critical factors in the harmful effects exerted by metals. Investigating the relationships between toxicity and metal-ligand binding affinity is therefore of great importance. We selected three parameters characterizing ion-ligand binding affinity (log \\u003cem\\u003eK\\u003c/em\\u003e, σ\\u003csub\\u003eCon\\u003c/sub\\u003e, and HLScale). In general, the larger the log \\u003cem\\u003eK\\u003c/em\\u003e, the closer the binding between ions and ligands. Similarly, the higher the σ\\u003csub\\u003eCon\\u003c/sub\\u003e value, the stronger the interaction between ions and soft ligands. In the HLScale, 0.0 denotes the mean binding strength, whereas \\u0026minus;\\u0026thinsp;1.0 and 1.0 denote 1 standard deviation lower or higher than the mean, respectively. Accordingly, we conducted a correlation analysis and linear regression between the three parameters and log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}, as shown in Fig. S4 and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Ag\\u003csup\\u003e+\\u003c/sup\\u003e was excluded from these relationships because the strong binding of Ag to soft ligands resulted in its deviation in the HLscale (HLscale for Ag\\u003csup\\u003e+\\u003c/sup\\u003e was \\u0026lt; -1.0, lower than 1 standard deviation).\\u003c/p\\u003e \\u003cp\\u003eThe three ligand-binding parameters showed different correlations with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}; σ\\u003csub\\u003eCon\\u003c/sub\\u003e showed a significantly negative correlation (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), indicating that log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} decreased with the increase of σ\\u003csub\\u003eCon\\u003c/sub\\u003e, whereas, log \\u003cem\\u003eK\\u003c/em\\u003e and HLScale had no significant correlation with log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} with \\u003cem\\u003ep\\u003c/em\\u003e greater than 0.05 (Fig. S4). Therefore, the univariate linear relationship between σ\\u003csub\\u003eCon\\u003c/sub\\u003e and log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.844, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was selected to establish the QICAR model in the present study. The predictive equation is as follows:\\u003c/p\\u003e \\u003cp\\u003el\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\text{og {}{\\\\text{EC}}_{50}\\\\text{} = -2.098 \\u0026sigma;}\\\\text{Con}\\\\text{ + 0.399}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (3)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003eValidation of QICAR predictive models\\u003c/h2\\u003e \\u003cp\\u003eCorresponding regression equations were established according to the relationships between toxicity and physicochemical properties. By comparing the statistics \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e, \\u003cem\\u003eF\\u003c/em\\u003e, \\u003cem\\u003ep\\u003c/em\\u003e and \\u003cem\\u003eRMSE\\u003c/em\\u003e, we finally selected the equations established by the soft-hard ion grouping method (soft ions with D, hard ions with AR/AW, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) and the σ\\u003csub\\u003eCon\\u003c/sub\\u003e method (Eq.\\u0026nbsp;(3)) as the QICAR model for lettuce. To evaluate the accuracy of the model, self-validation and external validation were conducted, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The model based on σ\\u003csub\\u003eCon\\u003c/sub\\u003e had a good effect in predicting log {EC\\u003csub\\u003e50\\u003c/sub\\u003e}; except for La\\u003csup\\u003e3+\\u003c/sup\\u003e and Sc\\u003csup\\u003e3+\\u003c/sup\\u003e, errors between the observed and predicted log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values ​​were within 1.5 orders of magnitude. It is worth noting that the model could predict the {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values of As\\u003csup\\u003e5+\\u003c/sup\\u003e, Co\\u003csup\\u003e2+\\u003c/sup\\u003e, Cd\\u003csup\\u003e2+\\u003c/sup\\u003e, Sb\\u003csup\\u003e3+\\u003c/sup\\u003e and Ag\\u003csup\\u003e+\\u003c/sup\\u003e well, with differences between observed and predicted log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values of less than 0.20 times. Among them, the prediction of the effect of As\\u003csup\\u003e5+\\u003c/sup\\u003e was the best, with a predicted {EC\\u003csub\\u003e50\\u003c/sub\\u003e} of 5.51 \\u0026micro;M and an observed value of 5.87 \\u0026micro;M. Similarly, with regard to the model developed using the soft-hard ion grouping method, the errors between observed and predicted log {EC\\u003csub\\u003e50\\u003c/sub\\u003e} were below 1.5 orders of magnitude, and the observed and predicted {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values of Cr\\u003csup\\u003e6+\\u003c/sup\\u003e were consistent (0.89 \\u0026micro;M). The above results revealed that the model based on σ\\u003csub\\u003eCon\\u003c/sub\\u003e and soft-hard ion grouping methods can achieve good predictive effects for metal(loid) ecotoxicity.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe 19 metal(loid) ions tested showed significant differences in toxicity to lettuce roots, with {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values in the order of magnitude range of 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e-10\\u003csup\\u003e3\\u003c/sup\\u003e. Some ions, such as Ag\\u003csup\\u003e+\\u003c/sup\\u003e and Se\\u003csup\\u003e6+\\u003c/sup\\u003e, were highly toxic to lettuce roots, with {EC\\u003csub\\u003e50\\u003c/sub\\u003e} of 0.05 \\u0026micro;M and 0.15 \\u0026micro;M, respectively. The highly toxic effects of Ag\\u003csup\\u003e+\\u003c/sup\\u003e have been widely reported in aquatic and soil organisms. For example, Morgan and Wood (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e) and Le et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) tested the toxicity of Ag to rainbow trout (\\u003cem\\u003eOncorhynchus mykiss\\u003c/em\\u003e) and lettuce (\\u003cem\\u003eLactuca sativa\\u003c/em\\u003e) and found that 0.03 \\u0026micro;M and 0.13 \\u0026micro;M Ag could inhibit 50% of growth, respectively. High toxicity of Ag to plant roots can be attributed to its strong binding to the cell wall and inhibition of enzymes required for cell expansion to produce rupturing, etc. (Blamey et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). The toxicity of Se\\u003csup\\u003e6+\\u003c/sup\\u003e ({EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.15 \\u0026micro;M) was second only to that of highly toxic Ag\\u003csup\\u003e+\\u003c/sup\\u003e ({EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 0.05 \\u0026micro;M). Since Se can replace S in the amino acid cysteine and methionine, its toxicity in root cells may be related to alterations in protein biosynthesis, structure and function (Van Hoewyk \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThere are often differences in toxicity and toxicity symptoms for different valences of an element. In the present study, we observed differences in the toxic effects and symptoms of different valences of As on lettuce. The toxicity of As\\u003csup\\u003e3+\\u003c/sup\\u003e to lettuce roots ({EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 2.75 \\u0026micro;M) was slightly higher than that of As\\u003csup\\u003e5+\\u003c/sup\\u003e ({EC\\u003csub\\u003e50\\u003c/sub\\u003e} = 5.87 \\u0026micro;M), which was consistent with the results obtained by SEM observation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb-c). However, Kopittke et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) obtained different results in research on the toxicity of different valences of As to cowpea (\\u003cem\\u003eVigna unguiculata\\u003c/em\\u003e) roots, finding that 3.6 \\u0026micro;M As\\u003csup\\u003e3+\\u003c/sup\\u003e and 0.9 \\u0026micro;M As\\u003csup\\u003e5+\\u003c/sup\\u003e could reduce the elongation of cowpea roots by 50%. Difference between the results of the two studies may be due to the different plants tested. Nonetheless, both studies showed differences in the toxicity of the variable-valence element As, which could be attributed to its different phytotoxicity mechanisms. Studies have shown that the phytotoxicity of As\\u003csup\\u003e3+\\u003c/sup\\u003e [absorbed through the silicic acid transport system (Asher and Reay \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e; Ma et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e)] is due to its reaction with dithiol groups on proteins and inhibition of enzyme reactions requiring free sulfhydryl groups (Horswell and Speir \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). On the contrary, As\\u003csup\\u003e5+\\u003c/sup\\u003e competes with phosphate and is absorbed through phosphate transporters (Asher and Reay \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e; Zhao et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Similarly, the phytotoxicity and toxicity symptoms of different valences of Se were also quite different (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed-e). There is a high affinity between Se\\u003csup\\u003e6+\\u003c/sup\\u003e and sulfate transporters, which is conducive to absorption and transport (Zhang et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). However, Se\\u003csup\\u003e4+\\u003c/sup\\u003e is transported through the cytoplasm, and phosphate transporters are involved in this process (Hopper and Parker \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Li et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMacroelements, such as Ca and Mg, are essential nutrients for plant growth and development and play an important role in a series of physiological and biochemical reactions. These elements are generally not toxic and might even compete with toxic ions for bioactive sites, so as to reduce the toxicity of the toxic ions (Kopittke et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). However, when their concentration is high or exceeds the required concentration for plant growth, these ions will inhibit plant growth and produce toxic effects. Kopittke et al. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) found that approximately 14000 \\u0026micro;M Mg\\u003csup\\u003e2+\\u003c/sup\\u003e inhibits cowpea root elongation by 50%, suggesting that the toxicity of Mg is much lower than that of other elements such as Ag and Cu.\\u003c/p\\u003e \\u003cp\\u003eCompared with previous studies (Meng et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019a\\u003c/span\\u003e), the QICAR equations established in the present study on the basis of relationships between toxicity and ionic characteristics of 19 metal(loid)s have a poor predictive effect on toxicity in lettuce (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.238\\u0026ndash;0.503, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). This may be due to the large variety of metal(loid)s and the large differences in physicochemical properties of elements, resulting in different dose-responses between organisms and individual elements (Luo et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Zamil et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Hence, classification of elements with similar properties may improve the predictive performance of the model. Soft-hard acid-base theory (HSAB) was developed on the basis of Lewis acid-base theory, which divides ions into soft and hard categories according to the properties of elements, where \\\"soft\\\" refers to those ions with larger radius, higher polarizability and lower charge density, while hard ions have the opposite properties (Pearson \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1963\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e1968\\u003c/span\\u003e; Pearson and Mawby \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e1967\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOn the basis of QICAR, both the relationships between toxicity and ion characteristics and toxicity prediction effects were significantly improved after ions were classified following the HSAB theory. The toxicity of soft ions showed significant correlations with 7 of the 23 physicochemical properties (D, Z, IP, Z/AR, Z/r, |log(-KOH)|, Z\\u003csup\\u003e2\\u003c/sup\\u003e/r; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05); D showed the largest correlation coefficient with log{EC\\u003csub\\u003e50\\u003c/sub\\u003e}, making it the best variable for establishing the toxicity prediction equation. D is density, a physical property of metal(loid) ions. In general, periodic variation in density is determined by atomic weight, atomic volume and lattice type (Enache et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Other properties, such as charge Z, play an important role in the interaction between soft ions and soft ligands as soft receptors usually exhibit low charge (Pearson \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1963\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e1968\\u003c/span\\u003e). The toxicity of hard ions showed a significant correlation with only one property (AR/AW). AR/AW is the ratio of atomic radius to atomic weight, which characterizes the electron density of metal(loid) ions. Generally, the charge density of the acceptor and donor is the main factor in the interaction between hard ions and hard ligands (Ahrland \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1968\\u003c/span\\u003e). However, due to the reduction in the species and number of metal(loid) elements after grouping, higher \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e values ​​obtained using the soft-hard ion grouping method of QICAR should be further verified using more elements.\\u003c/p\\u003e \\u003cp\\u003eThe soft-hard ion grouping led to a reduction in the number of samples, and we could not establish a unified QICAR method. We therefore further explored the relationships between ion toxicity and the three ligand-binding constants (σ\\u003csub\\u003eCon\\u003c/sub\\u003e, HLScale and log \\u003cem\\u003eK\\u003c/em\\u003e) that characterize the binding affinity of metal(loid)s to ligands. Each parameter had different prediction effects on metal(loid) phytotoxicity. σ\\u003csub\\u003eCon\\u003c/sub\\u003e showed a strong correlation with toxicity (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with the best fitting effect (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.844), indicating that ion softness is a key factor in evaluating and predicting metal(loid) toxicity to lettuce roots and that interactions between soft ions and soft ligands play a major role in metal(loid) toxicity to lettuce. Similarly, Kinraide (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) explored the relationship between metal(loid) toxicity and σ\\u003csub\\u003eCon\\u003c/sub\\u003e and found that the interaction of charge (Z) and σ\\u003csub\\u003eCon\\u003c/sub\\u003e achieved good toxicity prediction effects (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.923); however, this was limited to low-valent elements (Z\\u0026thinsp;\\u0026le;\\u0026thinsp;3).\\u003c/p\\u003e \\u003cp\\u003eBy contrast, toxicity was not significantly correlated with log \\u003cem\\u003eK\\u003c/em\\u003e or HLScale (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Customarily, log \\u003cem\\u003eK\\u003c/em\\u003e is directly proportional to EC value (Meng et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019a\\u003c/span\\u003e). However, log \\u003cem\\u003eK\\u003c/em\\u003e values of some ions such as Cu\\u003csup\\u003e2+\\u003c/sup\\u003e and Ag\\u003csup\\u003e+\\u003c/sup\\u003e in the present study corresponded to {EC\\u003csub\\u003e50\\u003c/sub\\u003e} in the opposite relationship (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). A possible reason for this trend was that log \\u003cem\\u003eK\\u003c/em\\u003e values were obtained from previously published literature, while {EC\\u003csub\\u003e50\\u003c/sub\\u003e} values were derived from our experiments; the lettuce varieties used in the studies of these two types of data may be different, thereby resulting in a non-corresponding relationship between log \\u003cem\\u003eK\\u003c/em\\u003e and toxicity for some elements. However, when Cu\\u003csup\\u003e2+\\u003c/sup\\u003e was removed, log \\u003cem\\u003eK\\u003c/em\\u003e was significantly correlated with toxicity and showed a better toxicity prediction effect (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.767, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.033). In addition, the log \\u003cem\\u003eK\\u003c/em\\u003e method currently available in the published literature involves limited metal species and toxicity data. There are few studies on the evaluation and prediction of lettuce toxicity using BLM. We could only obtain log \\u003cem\\u003eK\\u003c/em\\u003e values of six metal elements (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Therefore, obtaining the log \\u003cem\\u003eK\\u003c/em\\u003e of more elements will be more meaningful for modeling.\\u003c/p\\u003e \\u003cp\\u003eA poor correlation was shown between HLScale and toxicity, which may be caused by differences in metal(loid) phytotoxicity mechanism. Metal cations binding to hard ligands, regarded as a common mechanism, results in direct toxicity through inhibiting the controlled loosening of cell walls (Kopittke et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Nevertheless, this mechanism may not work for all ions; that is to say, the binding strength of the ion to the hard ligand may not be dominant in all ion-induced toxicity. For example, the soft ion Ag\\u003csup\\u003e+\\u003c/sup\\u003e tends to bind strongly to RS- functional groups (soft ligands) in metallothionein to exert toxic effects (Bell et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), independent of the binding strength of hard ions (Kinraide \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Nevertheless, whether this applies to more plant species or elements requires further study.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn the current study, we investigated the toxic effects of different metal(loid) ions on lettuce using hydroponic culture and explored the potential predictability of the QICAR model for phytotoxicity using methods such as soft-hard ion grouping and ligand-binding theory (σ\\u003csub\\u003eCon\\u003c/sub\\u003e, HLScale and log \\u003cem\\u003eK\\u003c/em\\u003e). The 19 metal(loid) ions tested showed significant toxicity differences to lettuce roots, with differences in {EC\\u003csub\\u003e50\\u003c/sub\\u003e} greater than four orders of magnitude (0.05 \\u0026micro;M -953.16 \\u0026micro;M). Some ions, such as Ag\\u003csup\\u003e+\\u003c/sup\\u003e, were highly toxic to lettuce roots, while others such as Mg\\u003csup\\u003e2+\\u003c/sup\\u003e and Sb\\u003csup\\u003e5+\\u003c/sup\\u003e showed weaker toxic effects. Correlation and linear regression analysis between metal(loid) toxicity and ionic characteristics revealed that the method of classifying metals into soft and hard ions could significantly improve the interpretation of physicochemical properties to toxicity variation. Of the three ligand-binding constants, σ\\u003csub\\u003eCon\\u003c/sub\\u003e showed the closest relationship with toxicity and provided the best predictive effect.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLuo Xiaorong:\\u003c/strong\\u003e Methodology, Validation, Formal analysis, Investigation, Data Curation, Visualization, Writing - Original Draft; \\u003cstrong\\u003eWang Xuedong:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Methodology, Formal analysis, Project administration, Writing - Original Draft, Writing - Review \\u0026amp; Editing, Funding acquisition;\\u003cstrong\\u003e\\u0026nbsp;Xia Cunyan:\\u003c/strong\\u003e Investigation, Validation; \\u003cstrong\\u003ePeng Jing:\\u003c/strong\\u003e Investigation; \\u003cstrong\\u003eWang Ying:\\u003c/strong\\u003e Writing - Review;\\u003cstrong\\u003e\\u0026nbsp;Tang Yujie:\\u003c/strong\\u003e Investigation; \\u003cstrong\\u003eGao\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eFan:\\u003c/strong\\u003e Investigation;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe work was financially supported by the National Natural Science Foundation of China (grant number 41877496).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Statement\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study did not involve any animals.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbramenko N, Kustov L, Metelytsia L, Kovalishyn V, Tetko I, Peijnenburg W (2020) A review of recent advances towards the development of QSAR models for toxicity assessment of ionic liquids. J Hazard Mater 384: 121429. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jhazmat.2019.121429\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jhazmat.2019.121429\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAhrland S (1968) Thermodynamics of complex formation between hard and soft acceptors and donors. 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Ecotox Environ Safe 74: 1036\\u0026ndash;1042. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ecoenv.2011.01.021\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ecoenv.2011.01.021\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Metal, Quantitative ion character–activity relationship (QICAR), Toxicity, lettuce \",\"lastPublishedDoi\":\"10.21203/rs.3.rs-1628233/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-1628233/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eNew pollution elements introduced by the rapid development of modern industry and agriculture may pose a serious threat to the soil ecosystem. To explore the ecotoxicity and risk of these elements, we systematically studied the acute toxicity of 19 metal(loid)s toward lettuce using hydroponic experiments and quantitative relationships between element toxicity and ionic characteristics using ion-grouping and ligand-binding theory methods, thereby establishing a quantitative ion character-activity relationship (QICAR) model for predicting the phytotoxicity threshold of data-poor elements. The toxicity of 19 ions to lettuce differed by more than four orders of magnitude (0.05 \\u0026micro;M-953.16 \\u0026micro;M). Correlation and linear regression analysis showed that the ionic characteristics significantly associated with this toxicity explained only 23.8%-50.3% of the toxicity variation (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.238\\u0026ndash;0.503, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Relationships between toxicity and ionic properties significantly improved after separating metal(loid) ions into soft and hard, with \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e of 0.793 and 0.784 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), respectively. Three ligand-binding parameters showed different predictive effects on lettuce metal(loid) toxicity. Compared with the binding constant of the biotic ligand model (log \\u003cem\\u003eK\\u003c/em\\u003e) and the hard ligand scale (HLScale) (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), the softness consensus scale (σ\\u003csub\\u003eCon\\u003c/sub\\u003e) was significantly correlated with toxicity and provided the best prediction (\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003csub\\u003e\\u003cem\\u003eAdj\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.844, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). We selected QICAR equations based on soft-hard ion classification and σ\\u003csub\\u003eCon\\u003c/sub\\u003e methods to predict phytotoxicity of metal(loid)s, which can be used to derive ecotoxicity for data-poor metal(loid)s, providing preliminary assessment of their ecological risks.\\u003c/p\\u003e\",\"manuscriptTitle\":\"QICAR methods for assessing the ecotoxicity of soil metal(loid)s to lettuce\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-05-10 14:49:49\",\"doi\":\"10.21203/rs.3.rs-1628233/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"63bd5812-3661-4979-a900-7f49dcc93b0a\",\"owner\":[],\"postedDate\":\"May 10th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2022-05-24T20:29:10+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2022-05-10 14:49:49\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-1628233\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-1628233\",\"identity\":\"rs-1628233\",\"version\":[\"v1\"]},\"buildId\":\"_2-kVJe1T_tPrBINL-cwx\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}