Distribution of cadmium and lead in soil-rice systems and their environmental driving factors at the island scale

Rat Hepatocytes Protect against Lead-Cadmium-Triggered Apoptosis Based on Autophagy Activation

Lead and cadmium are foodborne contaminants that threaten human and animal health. It is well known that lead and cadmium produce hepatotoxicity; however, defense mechanisms against the co-toxic effects of lead and cadmium remain unknown. We investigated the mechanism of autophagy (defense mechanism) against the co-induced toxicity of lead and cadmium in rat hepatocytes (BRL-3A cells). Cultured rat liver BRL-3A cell lines were co-cultured with 10, 20, 40 μM lead and 2.5, 5, 10 μM cadmium alone and in co-culture for 12 h and exposed to 5 mM 3-Methyladenine (3-MA), 10 μM rapamycin (Rapa), and 50 nM Beclin1 siRNA to induce cellular autophagy. Our results show that treatment of BRL-3A cells with lead and cadmium significantly decreased the cell viability, increased intracellular reactive oxygen species levels, decreased mitochondrial membrane potential levels, and induced apoptosis, which are factors leading to liver injury, and cell damage was exacerbated by co-exposure to lead-cadmium. In addition, the results showed that lead and cadmium co-treatment induced autophagy. We further observed that the suppression of autophagy with 3-MA or Beclin1 siRNA promoted lead-cadmium-induced apoptosis, whereas enhancement of autophagy with Rapa suppressed lead-cadmium-induced apoptosis. These results demonstrated that co-treatment with lead and cadmium induces apoptosis in BRL-3A cells. Interestingly, the activation of autophagy provides cells with a self-protective mechanism against induced apoptosis. This study provides insights into the role of autophagy in lead-cadmium-induced apoptosis, which may be beneficial for the treatment of lead-cadmium-induced liver injury.

Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction

Cultivated soil quality is crucial because it directly affects food safety and human health, and rice is of primary concern because of its centrality to global food networks. However, a detailed understanding of cadmium (Cd) geochemical cycling in paddy soils is complicated by the multiple influencing factors present in many rice-growing areas that overlap with industrial centers. This study analyzed the pollution characteristics and health risks of Cd in paddy soils across Hainan Island and identified key influencing factors based on multi-source environmental data and prediction models. Approximately 27.07% of the soil samples exceeded the risk control standard screening value for Cd in China, posing an uncontaminated to moderate contamination risk. Cd concentration and exposure duration contributed the most to non-carcinogenic and carcinogenic risks to children, teens, and adults through ingestion. Among the nine prediction models tested, Extreme Gradient Boosting (XGBoost) exhibited the best performance for Cd prediction with soil properties having the highest importance, followed by climatic variables and topographic attributes. In summary, XGBoost reliably predicted the soil Cd concentrations on tropical islands. Further research should incorporate additional soil properties and environmental variables for more accurate predictions and to comprehensively identify their driving factors and corresponding contribution rates.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.