Stable isotopic signature of cadmium in tracing the source, fate, and translocation of cadmium in soil: A review.

Cadmium (Cd), one of the most severe environmental pollutants in soil, poses a great threat to food safety and human health. Understanding the potential sources, fate, and translocation of Cd in soil-plant systems can provide valuable information on Cd contamination and its environmental impacts. Stable Cd isotopic ratios (δ114/110Cd) can provide "fingerprint" information on the sources and fate of Cd in the soil environment. Here, we review the application of Cd isotopes in soil, including (i) the Cd isotopic signature of soil and anthropogenic sources, (ii) the interactions of Cd with soil constituents and associated Cd isotopic fractionation, and (iii) the translocation of Cd at soil-plant interfaces and inside plant bodies, which aims to provide an in-depth understanding of Cd transport and migration in soil and soil-plant systems. This review would help to improve the understanding and application of Cd isotopic techniques for tracing the potential sources and (bio-)geochemical cycling of Cd in soil environment.

Mechanistic and data-driven perspectives on plant uptake of organic pollutants.

Establishing reliable predictive models for plant uptake of organic pollutants is crucial for environmental risk assessment and guiding phytoremediation efforts. This study compiled an expanded dataset of plant cuticle-water partition coefficients (Kcw), a useful indicator for plant uptake, for 371 data points of 148 unique compounds and various plant species. Quantum/computational chemistry software and tools were utilized to compute various molecular descriptors, aiming to comprehensively characterize the properties and structures of each compound. Three types of models were developed to predict Kcw: a mechanism-driven pp-LFER model, a data-driven machine learning model, and an integrated mechanism-data-driven model. The mechanism-data-driven GBRT-ppLFER model exhibited superior performance, achieving RMSEtrain = 0.133 and RMSEtest = 0.301 while maintaining interpretability. The Shapley Additive Explanation analysis indicated that pp-LFER parameters, ESPI, FwRadicalmax, ExtFP607, and RDF70s are the key factors influencing plant uptake in the GBRT-ppLFER model. Overall, pp-LFER parameter, ESPI, and ExtFP607 show positive effects, while the remaining factors exhibit negative effects. Partial dependency analysis further indicated that plant uptake is not solely determined by individual factors but rather by the combined interactions of multiple factors. Specifically, compounds with ppLFER parameter >4, ESPI > -25.5, 0.098 < FwRadicalmax <0.132, and 2 < RFD70s < 3, are generally more readily taken up by plants. Besides, the predicted Kcw values from the GBRT-ppLFER model were effectively employed to estimate the plant-water partition coefficients and bioconcentration factors across different plant species and growth media (water, sand, and soil), achieving an outstanding performance with an RMSE of 0.497. This study provides effective tools for assessing plant uptake of organic pollutants and deepens our understanding of plant-environment-compound interactions.