Influence of vegetation on heavy metal Cr release process from bottom sediment under unidirectional flows and regular waves.

As human activities become more intensive, a substantial number of heavy metals are discharged into estuarine or wetland environments. Due to the poor degradability, heavy metals are prone to adsorption and deposition on suspended particles in bottom sediments. Subsequently, under the influence of disturbances, there is a potential for their re-release, causing secondary pollution. To investigate the release process of the heavy metal Cr from sediment, laboratory experiments were conducted under both unidirectional flow and regular wave conditions. At the initial stage, the temporal trends of particulate (CrP) and dissolved (CrD) Chromium concentrations were both characterized by initial increments followed by stabilization and continuous escalation. Vertically, the stable concentrations of CrP and CrD increased with the presence of vegetation and the enhancement of hydrodynamics. The Elovich equation, pseudo-second-order kinetic equation, Double constant equation (Freundlich model), and parabolic diffusion equation were employed to predict the release process of CrD from bottom sediment. The Elovich equation proved most suitable for describing the release process of CrD, with an R2 exceeding 0.9. In order to assess the influence of vegetation on the Cr release process, the Stem-Reynolds were introduced to modify the Elovich equation. The final maximum error was 12 % (excluding the initial stage), which was much lower than that using the original Elovich equation (maximum error of 32 %). The study findings provide practical support for estuarine and wetland managers to formulate effective heavy metal management measures, which contribute to the conservation and sustainable management of aquatic ecosystems.

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.

Typical antibiotic resistance genes and their association with driving factors in the coastal areas of Yangtze River Estuary.

The massive use of antibiotics has led to the escalation of microbial resistance in aquatic environment, resulting in an increasing concern regarding antibiotic resistance genes (ARGs), posing a serious threat to ecological safety and human health. In this study, surface water samples were collected at eight sampling sites along the Yangtze River Estuary. The seasonal and spatial distribution patterns of 10 antibiotics and target genes in two major classes (sulfonamides and tetracyclines) were analyzed. The findings indicated a high prevalence of sulfonamide and tetracycline resistance genes along the Yangtze River Estuary. Kruskal-Wallis analysis revealed significant seasonal variations in the abundance of all target genes. The accumulation of antibiotic resistance genes in the coastal area of the Yangtze River Estuary can be attributed to the influence of urban instream runoff and the discharge of effluents from wastewater treatment plants. ANISOM analysis indicated significant seasonal differences in the microbial community structure. VPA showed that environmental factors contribute the most to ARG variation. PLS-PM demonstrate that environmental factors and microbial communities pose direct effect to ARG variation. Analysis of driving factors influencing ARGs in this study may shed new insights into the mechanism of the maintenance and propagation of ARGs.

Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary.

Due to the degradation-resistant and strong toxicity, heavy metals pose a serious threat to the safety of water environment and aquatic ecology. Rapid acquisition and prediction of heavy metal concentrations are of paramount importance for water resource management and environmental preservation. In this study, heavy metal concentrations (Cr, Ni, Cu, Pb, Zn, Cd) and physicochemical parameters of water quality including Temperature (Temp), pH, Oxygen redox potential (ORP), Dissolved oxygen (DO), Electrical conductivity (EC), Electrical resistivity (RES), Total dissolved solids (TDS), Salinity (SAL), Cyanobacteria (BGA-PE), and turbidity (NTU) were measured at seven stations in the Yangtze river estuary. Principal Component Analysis (PCA) and Spearman correlation analysis were employed to analyze the main factors and sources of heavy metals. Results of PCA revealed that the main sources of Cr, Ni, Zn, and Cd were steel industry wastewater, domestic and industrial sewage, whereas shipping and vessel emissions were typically considered sources of Pb and Cu. Spearman correlation analysis identified Temp, pH, ORP, EC, RES, TDS, and SAL as the key physicochemical parameters of water quality, exhibiting the strongest correlation with heavy metal concentrations in sediment and water samples. Based on these results, multiple linear regression as well as non-linear models (SVM and RF) were constructed for predicting heavy metal concentrations. The results showed that the results of the nonlinear model were more suitable for predicting the concentrations of most heavy metals than the linear model, with average R values of the SVM test set and RF test set being 0.83 and 0.90. The RF model showed better applicability for simulating the concentration of heavy metals along the Yangtze river estuary. It was demonstrated that non-linear research methods provided efficient and accurate predictions of heavy metal concentrations in a simple and rapid manner, thereby offering decision-making support for watershed managers.

Spatiotemporal distributions of sulfonamide and tetracycline resistance genes and microbial communities in the coastal areas of the Yangtze River Estuary.

In this paper, water and sediments were sampled at eight monitoring stations in the coastal areas of the Yangtze River Estuary in summer and autumn 2021. Two sulfonamide resistance genes (sul1 and sul2), six tetracycline resistance genes (tetM, tetC, tetX, tetA, tetO, and tetQ), one integrase gene (intI1), 16 S rRNA genes, and microbial communities were examined and analyzed. Most resistance genes showed relatively higher abundance in summer and lower abundance in autumn. One-way analysis of variance (ANOVA) showed significant seasonal variation of some ARGs (7 ARGs in water and 6 ARGs in sediment). River runoff and WWTPs are proven to be the major sources of resistance genes along the Yangtze River Estuary. Significant and positive correlations between intI1 and other ARGs were found in water samples (P < 0.05), implying that intI1 may influence the spread and propagation of resistance genes in aquatic environments. Proteobacteria was the dominant phylum along the Yangtze River Estuary, with an average proportion of 41.7%. Redundancy analysis indicated that the ARGs were greatly affected by temperature, dissolved oxygen, and pH in estuarine environments. Network analysis showed that Proteobacteria and Cyanobacteria were the potential host phyla for ARGs in the coastal areas of the Yangtze River Estuary.