The vadose zone acts as a natural buffer that prevents contaminants such as arsenic (As) from contaminating groundwater resources. Despite its capability to retain As, our previous studies revealed that a substantial amount of As could be remobilized from soil under repeated wet-dry conditions. Overlooking this might underestimate the potential risk of groundwater contamination. This study quantified the remobilization of As in the vadose zone and developed a prediction model based on soil properties. 22 unsaturated soil columns were used to simulate vadose zones with varying soil properties. Repeated wet-dry cycles were conducted upon the As-retaining soil columns. Consequently, 13.9-150.6 mg/kg of As was remobilized from the columns, which corresponds to 37.0-74.6 % of initially retained As. From the experimental results, a machine learning model using a random forest algorithm was established to predict the potential for As remobilization based on readily accessible soil properties, including organic matter (OM) content, iron (Fe) content, uniformity coefficient, D30, and bulk density. Shapley additive explanation analyses revealed the interrelated effects of multiple soil prop-erties. D30, which is inter-related with Fe content, exhibited the highest contribution to As remobilization, fol-lowed by OM content, which was partially mediated by bulk density.

Endocrine-disrupting chemicals (EDCs) are ubiquitous emerging environmental contaminants. However, the comprehensive impact of EDCs on soil ecosystems, particularly on the model organism Eisenia fetida, remains inadequately understood due to disparate experimental and assessment methods. A meta-analysis was conducted to analyze the effects of EDCs on earthworm functional traits, including survival, behavior, growth, reproduction, and cellular responses. The analysis revealed that EDCs significantly impaired earthworm survival (-17.5%, p < 0.05), behavior (- 62.2%, p < 0.001), growth (-11.5%, p < 0.001), and reproduction (- 36.7%, p < 0.001). EDCs induced substantial oxidative stress, evidenced by a 36.5% (p <0.001) increase in reactive oxygen species (ROS) production and elevated oxidative damage. The antioxidant defense system showed compensatory activation, with enhanced superoxide dismutase (10.0%) and catalase (8.90%) activities and glutathione levels (23.3%) (p < 0.001). The present study found chemical-specific toxicity patterns with heavy metals causing the most severe effects on behavior and reproduction. Toxicity profiles varied with exposure concentration and duration, revealing complex dose-response and temporal relationships. These findings provide crucial insights for the ecological risk assessment of EDCs and establish a foundation for developing targeted mitigation strategies. Furthermore, the findings highlight the importance of taking multiple endpoints into account when evaluating the toxicity of EDCs and suggest possible directions for future research.

Corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), is a common herbivore that causes economic damage to agronomic and specialty crops across North America. The interannual abundance of H. zea is closely linked to climactic variables that influence overwintering survival, as well as within-season host plant availability that drives generational population increases. Although the abiotic and biotic drivers of H. zea populations have been well documented, prior temporal H. zea modeling studies have largely focused on mechanistic/simulation approaches, long term distribution characterization, or degree day-based phenology within the growing season. While these modeling approaches provide insight into H. zea population ecology, growers remain interested in approaches that forecast the interannual magnitude of moth flights which is a key knowledge gap limiting early warning before crops are planted. Our study used trap data from 48 site-by-year combinations distributed across North Carolina between 2008 and 2021 to forecast H. zea abundance in advance of the growing season. To do this, meteorological data from weather stations were combined with crop and soil data to create predictor variables for a random forest H. zea forecasting model. Overall model performance was strong (R2 = 0.92, RMSE = 350) and demonstrates a first step toward development of contemporary model-based forecasting tools that enable proactive approaches in support of integrated pest management plans. Similar methods could be applied at a larger spatial extent by leveraging national gridded climate and crop data paired with trap counts to expand forecasting models throughout the H. zea overwintering range.