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 (K d) of metals in the soil helps understand their environmental risks. We collected 102 K d 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 K d , 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 K d values of Mo, Sb, and La in the 39 soil samples, ranging from 21 to 19978. Finally, a coupling analysis was conducted between K d -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 K d , providing a basis for soil environmental risk assessment and management. 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 (K d) of metals in the soil helps understand their environmental risks. Common K d 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 K d , then establish a model that does not rely on measured data to predict K d of soil elements, providing a basis for soil environmental risk assessment and management. [Display omitted] • 102 sets of soil data with different properties were used for K d prediction model. • Ten elemental physicochemical properties are related to K d. • In total, 39s-QICAR models related to Xm2r and |logKOH| were established. • Soil properties influenced Xm2r and |logKOH| impacts on K d. • The s-QICAR predicted the K d of Mo, Sb, and La in 39 soil types. [ABSTRACT FROM AUTHOR]