The biogeochemistry of multi-elements, such as sulfur (S), phosphorus (P) and arsenic (As), is interlinked especially at interfaces of soil/sediment–water and plant rhizosphere. To explore the biogeochemical behavior of multi-elements such as S-P-As at interfaces, an in situ and high-resolution technology is required. In this study, we developed an in situ probe (LDHs-DGT) based on the diffusive gradients in thin-films technique using a single binding layer to realize the co-measurement of multi-elements including sulfide and oxyanions. Mg-Al layered double hydroxides (LDHs) were synthesized and incorporated into the probe’s binding layer. Laboratorial characterization showed that the LDHs-DGT probe had a high capacity for sulfide, phosphate and arsenate and can effectively determine their levels across a wide range of solution conditions, i.e., pH from 5 to 8 and ionic strengths from 0.005–0.01 mol L⁻¹ NaNO₃. The application potential of the LDH_S-DGT probe in capturing the concentration profiles of sulfide and oxyanions across the soil/sediment–water interface at a centimeter scale was demonstrated. The synchronous co-variations of labile sulfide and phosphate were observed along an intact river sediment core, demonstrating the redox driven behaviors of oxyanions at aerobic–anaerobic transition zones. Moreover, the LDH_S-DGT probe was further used to acquire the dynamic distributions of multi-elements in the plant rhizosphere at a two-dimensional millimeter scale. Compared to treatments of sodium sulfate and mercaptopygorskite fertilization, the addition of elementary S promoted the reduction of sulfate to sulfide along the whole growth stage and thus inhibited the activation of toxic metals in the rice rhizosphere. Collectively, this study provides a tool for convenient measurement of nutrients and metal(loid)s across soil–water/root interfaces at high resolution and thus, a broad application prospect of the tool in sustainable agriculture is expected.

To investigate the effect of high-arsenic (As) soil on the absorption of As by highland barley, 135 pairs of soil–crop samples were collected in the main producing areas of highland barley in the middle reaches of the Yarlung Zangbo River. Eight soil variables, including pH, redox potential (Eh), soil organic matter (SOM), total arsenic (T-As), total iron (T-Fe), total manganese (T-Mn), chemically extractable As (KH₂PO₄-As), and bioavailable As determined by diffusive gradients in thin films (DGT-As), were measured, along with As concentrations in barley grains (HB-As). Machine learning approaches were employed to construct predictive models for HB-As accumulation, and feature influence mechanisms were interpreted using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) analyses. The results showed that: (1) among models constructed using the full feature set, the random forest (RF) model exhibited the best predictive performance for HB-As, with R² values of 0.756 and 0.651 for the training and testing datasets, respectively; (2) SHAP analysis indicated that DGT-As had the greatest contribution to the model (30.5%), followed by T-As and T-Fe/Mn; and (3) significant interaction effects among soil variables jointly influenced HB-As accumulation. This study provides scientific support for agricultural product safety, soil security, and sustainable land use in plateau agroecosystems.