Predicting the thresholds of metals with limited toxicity data with invertebrates in standard soils using quantitative ion character-activity relationships (QICAR)

Construction of multi-metal interspecies correlation estimation models based on typical soil scenarios

The ecological risk assessment of metals in soils is essential for soil pollution management. However, regional soil heterogeneity and species diversity need to be considered when making these assessments. Therefore, an interspecies correlation estimation (ICE) model was constructed based on typical soil scenarios that could predict metal toxicity across species. A dataset comprising 1017 toxicity data points for 12 metals (including Cu, Zn, and Ni) across eight species and two microbial processes was analyzed. An information gain analysis revealed that soil properties contributed 0.687 to metal toxicity, which was significantly higher than that for metal structural characteristics (0.313). After clustering the soils into three typical scenarios (acidic low-clay, neutral high-clay, and alkaline medium-clay), the influence of soil properties on toxicity prediction decreased to 0.529 (neutral high-clay) and 0.496 (alkaline medium-clay). Hierarchical clustering was used to screen six metal elements with lower toxicity variabilities (inter quartile range: 0.270-169.895) for modeling and 32 optimized ICE models were established (R2 = 90.648-0.895, MSE = 0.183-0.614). Brassica napus was found to be the best surrogate species for predicting metal toxicity in Brassica chinensis L. under alkaline medium-clay soil conditions (R2 = 0.895, MSE = 0.303). This study is the first to systematically integrate soil scenario clustering, metal toxicity variability screening, and machine learning-enhanced ICE modeling and provides a more robust and adaptable framework for ecological risk assessment in heterogeneous soil environments.

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.

Derivation of Soil Criteria of Cadmium for Safe Rice Production Applying Soil–Plant Transfer Model and Species Sensitivity Distribution

Widespread soil contamination is hazardous to agricultural products, posing harmful effects on human health through the food chain. In China, Cadmium (Cd) is the primary contaminant in soils and easily accumulates in rice, the main food for the Chinese population. Therefore, it is essential to derive soil criteria to safeguard rice products by assessing Cd intake risk through the soil–grain–human pathway. Based on a 2-year field investigation, a total of 328 soil–rice grain paired samples were collected in China, covering a wide variation in soil Cd concentrations and physicochemical properties. Two probabilistic methods used to derive soil criteria are soil–plant transfer models (SPT), with predictive intervals, and species sensitivity distribution (SSD), composed of soil type-specific bioconcentration factor (BCF, Cd concentration ratio in rice grain to soil). The soil criteria were back-calculated from the Chinese food quality standard. The results suggested that field data with a proper Cd concentration gradient could increase the model accuracy in the soil–plant transfer system. The derived soil criteria based on soil pH were 0.06–0.11, 0.33–0.59, and 1.51–2.82 mg kg−1 for protecting 95%, 50% and 5% of the rice safety, respectively. The soil criteria with soil pH further validated the soil as being safe for rice grains.