Geochemical characteristics of heavy metals of bedrock, soil, and tea in a metamorphic rock area of Guizhou Province, China

Research on heavy metal enrichment and transportation in tea plant-soil systems of different varieties

This study aimed to investigate heavy metal enrichment in different tea plant varieties and their distribution within different plant parts and to clarify the behavioral characteristics of heavy metals in the tea tree-soil system and their influencing factors. In this study, soil samples were collected from the root zones of 13 tea tree varieties in Guizhou, which had been planted for 10 years. The aim was to compare the physicochemical properties of tea plantation soils under soil-forming matrixes and consistent management. Additionally, the study investigated the enrichment and transportation patterns of Cd, Cr, Cu, Pb, Zn, and Ni in the tea tree-soil systems of different tea tree varieties. The results showed that the planting of tea trees decreased the soil pH by 0.5; soil nutrients decreased; soil Pb, Cr, Ni, Cu, and Zn contents in the root zone increased; and Cd content decreased. Heavy metals were mainly enriched in the roots, and Zn, Cu, Ni, and other elements related to the protein and enzyme synthesis of tea trees could be mostly transported to the stems and leaves. There were significant differences in the enrichment and transportation of heavy metals among the different tea tree varieties. Under consistent soil-forming parent material, soil pH, organic matter, nutrients, and other indices only had a significant effect on heavy metal enrichment in the tea tree roots. Therefore, in areas with high background soil heavy metal contents, the construction of tea plantations should be based on regional soil environmental conditions to choose tea tree varieties with low heavy metal enrichment capacities to avoid the risk of high background soil heavy metals on the safe production of tea for consumers.

Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China

Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were conducted at seven sampling sites along the Yangtze River Estuary (YRE) during summer, autumn, and winter 2021 to analyze the concentrations of seven heavy metals (As, Cd, Cr, Pb, Cu, Ni, Zn) in water and surface sediments. The order of heavy metal concentrations in water samples from highest to lowest was Zn > As > Cu > Ni > Cr > Pb > Cd, while that in surface sediments samples was Zn > Cr > As > Ni > Pb > Cu > Cd. Human health risk assessment of the heavy metals in water samples indicated a chronic and carcinogenic risk associated with As. The risks of heavy metals in surface sediments were evaluated using the geo-accumulation index (Igeo) and potential ecological risk index (RI). Among the seven heavy metals, As and Cd were highly polluted, with Cd being the main contributor to potential ecological risks. Principal component analysis (PCA) was employed to identify the sources of the different heavy metals, revealing that As originated primarily from anthropogenic emissions, while Cd was primarily from atmospheric deposition. To further analyze the influence of water quality indicators on heavy metal pollution, an artificial neural network (ANN) model was utilized. A modified model was proposed, incorporating biochemical parameters to predict the level of heavy metal pollution, achieving an accuracy of 95.1%. This accuracy was 22.5% higher than that of the traditional model and particularly effective in predicting the maximum 20% of values. Results in this paper highlight the pollution of As and Cd along the YRE, and the proposed model provides valuable information for estimating heavy metal pollution in estuarine water environments, facilitating pollution prevention efforts.

Impact of Heavy Metal Pollution in the Environment on the Metabolic Profile of Medicinal Plants and Their Therapeutic Potential

The paper provides a comprehensive examination of heavy metal stress on medicinal plants, focusing on its impact on antioxidant capacity and biosynthetic pathways critical to their therapeutic potential. It explores the complex relationship between heavy metals and the physiological and biochemical responses of medicinal plants, highlighting how metal stress disrupts biosynthetic pathways, altering concentrations of secondary metabolites. This disruption may compromise the overall quality and efficacy of medicinal plants, requiring a holistic understanding of its cumulative impacts. Furthermore, the study discusses the potential of targeted genetic editing to enhance plant resilience against heavy metal stress by manipulating genes associated with antioxidant defenses. This approach represents a promising frontier in safeguarding medicinal plants in metal-contaminated environments. Additionally, the research investigates the role of phytohormone signaling in plant adaptive mechanisms to heavy metal stress, revealing its influence on biochemical and physiological responses, thereby adding complexity to plant adaptation. The study underscores the importance of innovative technologies and global cooperation in protecting medicinal plants' therapeutic potential and highlights the need for mitigation strategies to address heavy metal contamination effectively.

The Distribution Characteristics and Potential Risk Assessment of Lead in the Soil of Tieguanyin Tea Plantations in Anxi County, China

Assessing the distribution and risks associated with the soil lead content in the Tieguanyin tea plantations of Anxi County is critical, given the county's significance as the primary Tieguanyin tea production area in Fujian Province. This study examined the distribution characteristics of soil lead in Anxi County's tea plantations according to the Kriging spatial interpolation of the parameters of the semivariance function of the exponential model. Moreover, the sources of lead content were analyzed, considering geological backgrounds and anthropogenic influences. Ecological risks and the issuance of early warnings were also assessed. The soil lead content in the rocks of the Tieguanyin tea plantations in Anxi County followed the order: andesite > dacite > rhyolite > granite. The soil lead content gradually decreased from the center toward the east and west, forming four distinct north-south parallel zones. High-lead-content areas were identified at the border of Jiandou, Bailai, and Hushang; in the central part of Lutian; and in the southern part of Huqiu. The high levels of soil lead in the tea plantations possibly originated from industrial and mining activities, automobile exhaust, and agricultural activities. The distribution of single-factor pollution indices and potential risk evaluation based on the Soil Environmental Quality Standard, Environmental Technical Conditions for Tea Production Area, and Environmental Technical Conditions for Organic Tea Production Area indicated that the soil in Tieguanyin tea plantations in Anxi County was clean and safe for tea cultivation.

Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation

The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r²) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.