A cumulative eutrophication risk evaluation method based on a bioaccumulation model

Response of sulfate concentration to eutrophication on spatio-temporal scale in freshwater lakes

The dramatical increase of sulfur concentration in eutrophic lakes, especially sulfate (SO42-), has brought attention to the impact on the lake ecosystem; however, the mechanisms driving the intensification of eutrophication and the role of SO₄2- concentrations remain poorly understood. To assess the impact of eutrophication on SO42- dynamics in lakes, this study monitored SO42- concentrations in water and sediments across seven lakes with varying trophic statuses on a spatial scale, and in the eutrophic Lake Taihu over one year on a temporal scale, as well as a series of microcosms with different initial SO42- concentrations. Exogenous sulfur input is the primary driver of increased SO42- concentrations in lakes, the highest SO42- concentration in overlying water was 100 mg/L, as well as which reached 310.9 mg/L in sediment. The concurrent input of nutrients such as nitrogen and phosphorus exacerbated eutrophication, resulting in the destabilization of the sulfur cycle. Eutrophication promoted the SO42- concentration on the spatio-temporal scale, especially in sediment, and trophic lake index (TLI) showed a positive correlation with the SO42- in sediments (R2 = 0.99; 0.88). The SO42- concentration in water and TLI showed a nonlinear correlation on the temporal scale (R2 = 0.44), and showed a positive correlation on the spatial scale (R2 = 0.49). Microscopic experiments demonstrate that the anaerobic environment created by cyanobacteria decomposition induced sulfate reduction and significantly reduces SO42- concentrations. Concurrently, the anaerobic environment facilitates the coupling of iron reduction with sulfate reduction, leading to a substantial increase in Acid Volatile Sulfides (AVS) in the sediment. These findings reveal that eutrophication has a dual effect on the dynamic change of SO42- concentrations in overlying water, which is helpful to accurately evaluate and predict the change of SO42- concentrations in lakes.

Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China

Algal blooms caused by climate change and human activities have received considerable attention in recent years. Since chlorophyll a (Chl-a) can be used as an indicator of phytoplankton biomass, it has been selected as a direct indicator for monitoring and early warning of algal blooms. With the development of artificial intelligence, data-driven approaches with small sample data and high accuracy prediction have been gradually applied to water quality prediction. This study aimed at using environment factors (water quality and meteorological data) to assist the prediction of Chl-a concentration based on the optimization support vector machine (SVM) model. The most relevant environment factors were extracted from the commonly used environment factors according to the method of cosine similarity. The traditional particle swarm optimization (PSO) algorithm was adopted to optimize the ANN and SVM models, respectively. Then, the better prediction model PSO-SVM can be obtained according to the results of three scientific evaluation indicators. The latest optimization algorithm of grey wolf optimizer (GWO) was also proposed to optimize the SVM to realize high-accuracy Chl-a concentration predication. The GWO-SVM model achieved higher accuracy than the other models both in training and validation processes. Therefore, the dimension of the input vector could be reduced with using the cosine similarity method, and the prediction of Chl-a concentration in high accuracy and the early warning of algal blooms in the study area of this paper could also achieved.

The architecture and application of an automatic operational model system for basin scale water environment management and design making supporting

An advanced framework for automatic water quality forecasting and water quality management design supporting was put forward. The system is designed as a flexible and extensible service-oriented architecture with data center, system control center, model center and client center. Two operational running modes, one for water environment automatic assessment and forecast and the other for situational analysis, were set to satisfy water quality management requirements. With loosely-coupled air-land-water numerical models, the weather, pollutants sources, hydrodynamic and water quality are automatically forecasted. According to philosophy of the framework, a one-stop platform with four different subsystems for the Three Gorges Reservoir Basin (TGRB) was developed and has been in operational running for more than two years. The system can accurately assessed, forecasted and perfectly displayed the current status and future character of TGRB in air, land and water environment.

Adsorption and regeneration characteristics of phosphorus from sludge dewatering filtrate by magnetic anion exchange resin

Removal and recovery of phosphorus (P) from sewage are essential for sustainable development of P resource. Based on the water quality determination of sludge dewatering filtrate from a wastewater treatment plant in Beijing, this study investigated the adsorption and regeneration characteristics of P by magnetic anion exchange resin (MAEX). The experiments showed that the P adsorption capacity of MAEX could reach a maximum of 2.74 mg/mL when initial P concentration was 25 mg/L and dosage of MAEX was 8 mL/L. The P adsorption on MAEX resin was suitable for large temperature range (283-323 K). However, the adsorption capacity was reduced in various degrees due to the interference of different anions (Br^-, SO₄^2-, Cl^-, NO₃^-, HCO₃^-, CO₃^2-) and organic compounds (bovine serum albumin, humic acid). Kinetics studies indicated that the P adsorption process followed the pseudo-second-order model. The MAEX resin had a rapid P adsorption rate and the P adsorption capacity at 30 min could reach 97.7-99.3% of q_e. Increase of temperature was favorable to P adsorption on MAEX, and the adsorption isotherm data fitted to Langmuir model more than Freundlich model. Meanwhile, the thermodynamics parameters were calculated; it was shown that the adsorption process was an endothermic reaction. Desorption and regeneration experiments showed that NaHCO₃ was a suitable regenerant, and the P adsorption capacity could reach 90.51% of the original capacity after 10 times of adsorption-desorption cycles; this indicated that MAEX resin has an excellent regeneration performance and thus has a very good application prospect of P removal and recovery. Fourier transform infrared spectroscopy (FTIR) analysis confirmed that ion exchange, charge attraction, and hydrogen bonding affected the removal of P by the MAEX resin. The vibrating sample magnetometer (VSM) analysis revealed that MAEX resin was a kind of soft magnetic materials with good magnetism.