Using quantitative ion character-activity relationship (QICAR) method in evaluation of metal toxicity toward wheat

Predicting soil ecological criteria of 17 metal(loid)s in China based on quantitative ion character-activity relationship - Species sensitivity distribution (QICAR-SSD) coupled model

Soil pollution caused by metal(loid)s is increasingly serious and poses unexpected risks to terrestrial organisms. Establishing soil quality standards is essential for assessing ecological risks of metal(loid)s and protecting soil ecosystems. However, the limited availability of metal(loid) ecotoxicological data has hampered the development of soil quality standards due to financial and practical constraints on toxicity testing. This study collected 77 normalization equations and 58 cross-species extrapolation equations to calculate the normalized EC10 (the added concentration causing a 10 % inhibition effect) of metal(loid)s under a representative scenario. A set of quantitative ion character-activity relationship (QICAR) models were then constructed using normalized EC10 and nine critical ionic characters (AR, AR/AW, BP, MP, Z/r2, Z/r, Xm, σp, and |Log(KOH)|). Subsequently, these QICAR models were employed to predict ecotoxicological EC10 of 17 metal(loid)s to 12 soil species and coupled with species sensitivity distribution (SSD) to determine Predicted No Effect Concentration (PNEC). The results demonstrated the coupled QICAR-SSD model could effectively derive terrestrial PNEC for data-poor metal(loid)s, with errors between the predicted PNEC and reported soil standards (excluding soil background levels) from different countries mostly <0.3 orders of magnitude. Finally, soil ecological criteria (SEC) for 17 metal(loid)s were calculated using an added risk approach based on PNEC and national soil background concentration. Overall, the coupled model proposed here can provide a valuable supplement to the development of soil quality standards for numerous metal(loid)s in soil components.

Deriving ecological risk thresholds for soil molybdenum in China based on interspecies correlation estimation and quantitative ion character-activity relationship models

Soil molybdenum (Mo) levels can reach ecologically hazardous levels. China has not yet established the relevant thresholds, posing challenges for environmental management. Therefore, we present our data relevant to Mo toxicity for several important species. By normalizing soil properties, we obtained a correlation model of Mo toxicity to Hordeum vulgare, as well as 31 models for the toxicity of other elements including Cu and Ni to invertebrates and microbial processes. Using interspecies correlation estimation (ICE) extrapolation, the sensitivity coefficient (0.12-0.71) for five plants were found. For invertebrates and microbial processes lacking Mo data, we used regression analysis to establish Mo toxicity models based on the soil quantitative ion character-activity relationships (s-QICAR; R2 =0.70-0.95) and known toxicities of other metal elements to invertebrate and microbial processes. Furthermore, combining species sensitivity distribution calculations, the HC5 values for protecting 95% of soil species from Mo in three typical soil scenarios in China were calculated. After correction, the predicted no-effect concentrations were 6.8, 4.8, and 3.4 mg/kg, respectively. This study innovatively combined ICE and s - QICAR to derive soil Mo thresholds. Our results can provide a basis for decision-making in the assessment and management of soil Mo pollution.

Using the quantitative ion character-activity relationships (QICAR) model to predict the solid-liquid release of metals in soil

Dissolved metals in the soil's liquid phase are more easily absorbed by biological tissues, thus posing potential risks to living organisms. Therefore, studying the solid-liquid partition coefficient (Kd) of metals in the soil helps understand their environmental risks. We collected 102 Kd data of metals in soil from the literature to build a quantitative ion character-activity relationship model (s-QICAR) based on the metals' physicochemical properties. Correlation analysis showed ten elemental physicochemical properties related to Kd, including covalent bond index, atomic mass, and first hydrolysis constant, with R2 = 0.502-0.989. Through comparative screening, 39s-QICAR models were finally established, including covalent bond index and first hydrolysis constant. The established s-QICAR model predicted the Kd values of Mo, Sb, and La in the 39 soil samples, ranging from 21 to 19978. Finally, a coupling analysis was conducted between Kd-metal and soil physicochemical properties. The s-QICAR model with covalent bond index as the independent variable was mainly affected by soil pH and cation exchange capacity, while the s-QICAR model with first hydrolysis constant was influenced primarily by clay content. Therefore, this study established a predictive model for soil Kd, providing a basis for soil environmental risk assessment and management. ENVIRONMENTAL IMPLICATION: Dissolved metals present in the soil liquid phase are more easily absorbed by biological tissues, thus posing potential risks to living organisms. Therefore, studying the solid-liquid partition coefficient (Kd) of metals in the soil helps understand their environmental risks. Common Kd values are obtained by measuring the content of soil solid and liquid phase metals. However, this method is time-material consuming. We study the relationship between element properties and their Kd, then establish a model that does not rely on measured data to predict Kd of soil elements, providing a basis for soil environmental risk assessment and management.

Phytotoxicity of trihalomethanes and trichloroacetic acid on Vigna radiata and Allium cepa plant models

Disinfection by-products (DBPs) are a concern due to their presence in chlorinated wastewater, sewage treatment plant discharge, and surface water, and their potential for environmental toxicity. Despite some attention to their ecotoxicity, little is known about the phytotoxicity of DBPs. This study aimed to evaluate the individual and combined phytotoxicity of four trihalomethanes (THMs: trichloromethane (TCM), bromodichloromethane (BDCM), dibromochloromethane (DBCM), and tribromomethane (TBM) and their mixture (THM4)), and trichloroacetic acid (TCAA) using genotoxic and cytotoxic assays. The analysis included seed germination tests using Vigna radiata and root growth tests, mitosis studies, oxidative stress response, chromosomal aberrations (CA), and DNA laddering using Allium cepa. The results showed a progressive increase in root growth inhibition for both plant species as the concentration of DBPs increased. High concentrations of mixtures of four THMs resulted in significant (p < 0.05) antagonistic interactions. The effective concentration (EC50) value for V. radiata was 5655, 3145, 2690, 1465, 3570, and 725 mg/L for TCM, BDCM, DBCM, TBM, THM4, and TCAA, respectively. For A. cepa, the EC50 for the same contaminants was 700, 400, 350, 250, 450, and 105 mg/L, respectively. DBP cytotoxicity was observed through CAs, including C-metaphase, unseparated anaphase, lagging chromosome, sticky metaphase, and bridging. Mitotic depression (MD) increased with dose, reaching up to 54.4% for TCAA (50-500 mg/L). The electrophoresis assay showed DNA fragmentation and shearing, suggesting genotoxicity for some DBPs. The order of phytotoxicity for the tested DBPs was TCAA > TBM > DBCM > BDCM > THM4 > TCM. These findings underscore the need for further research on the phytotoxicity of DBPs, especially given their common use in agricultural practices such as irrigation and the use of sludge as manure.

Quantitative ion character-activity relationship methods for assessing the ecotoxicity of soil metal(loid)s to lettuce

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 18 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 18 ions to lettuce differed by more than four orders of magnitude (0.05-804.44 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 (R2Adj = 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 R2Adj 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 (σCon) was significantly correlated with toxicity and provided the best prediction (R2Adj = 0.844, p < 0.001). We selected QICAR equations based on soft-hard ion classification and σCon 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.