Analysis of factors affecting microbial degradation of cyanobacterial toxins based on theoretical calculations.

Cyanobacterial toxins are the most common algal toxins, which are highly toxic and can persist in the aquatic environment without easy degradation, posing risks to the ecosystem and human health that cannot be ignored. Although microbiological methods for the removal of cyanobacterial toxins from aqueous environments are highly efficient, their degradation efficiency is susceptible to many abiotic environmental factors. In this paper, Microcystin-LR (MC-LR) and its microbial degrading enzymes were selected to study the effects of common environmental factors (temperature (T), NO3-, NH4+, Cu2+, Zn2+) and their levels during microbial degradation of cyanobacterial toxins in aqueous environments by using molecular docking, molecular dynamics simulation, analytical factor design, and the combined toxicokinetics of TOPKAT (toxicity prediction). It was found that the addition of T, NO3- and Cu2+ to the aqueous environment promoted the microbial degradation of MC-LR, while the addition of NH4+ and Zn2+ inhibited the degradation; The level effect study showed that the microbial degradation of MC-LR was promoted by increasing levels of added T and NO3- in the aqueous environment, whereas it was inhibited and then promoted by increasing levels of NH4+, Cu2+ and Zn2+. In addition, the predicted toxicity of common Microcystins (MCs) showed that MC-LR, Microcystin-RR (MC-RR) and Microcystin-YR (MC-YR) were not carcinogenic, developmentally toxic, mutagenic or ocular irritants in humans. MC-LR and MC-RR are mild skin irritants and MC-YR is not a skin irritant. MC-YR has a higher chronic and acute toxicity in humans than MC-LR and MC-RR. Acute/chronic toxicity intensity for aquatic animals: MC-YR > MC-LR > MC-RR and for aquatic plants: MC-LR > MC-YR > MC-RR. This suggests that MC-YR also has a high environmental health risk. This paper provides theoretical support for optimizing the environmental conditions for microbial degradation of cyanobacterial toxins by studying the effects of common environmental factors and their level effects in the aquatic environment.

Tracing the impacts of ecological water replenishment on the sources and transformation of groundwater nitrate through isotope and microbial analysis.

Ecological water replenishment (EWR) changes the recharge conditions, flow fields, and physicochemical properties of regional groundwater. However, the resulting impacts on mechanisms regulating the sources and transformation of groundwater nitrate remain unclear. This study investigated how EWR influences the sources and transformation processes of groundwater nitrate using an integrated approach of Water chemistry analysis and stable isotopes (δ15N-NO3- and δ18O-NO3-) along with microbial techniques. The results showed that groundwater NO3-N decreased from 12.98 ± 7.39 mg/L to 7.04 ± 8.52 mg/L after EWR. Water chemistry and isotopic characterization suggested that groundwater nitrate mainly originated from sewage and manure. The Bayesian isotope mixing model (MixSIAR) indicated that EWR increased the average contribution of sewage and manure sources to groundwater nitrate from 46 % to 61 %, whereas that of sources of chemical fertilizer decreased from 43 % to 21 %. Microbial community analysis revealed that EWR resulted in a substantial decrease in the relative abundance of Pseudomonas spp denitrificans, from 13.7 % to 0.6 %. Both water chemistry and microbial analysis indicated that EWR weakened denitrification and enhanced nitrification in groundwater. EWR increases the contribution of nitrate to groundwater by promoting the release of sewage and feces in the unsaturated zone. However, the dilution effect caused by EWR was stronger than the contribution of sewage and fecal sources to groundwater nitrate. As a result, EWR helped to reduce groundwater nitrate concentrations. This study showed the effectiveness of integrated isotope and microbial techniques for delineating the sources and transformations of groundwater nitrate influenced by EWR.

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment.

Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both surface water and groundwater within the basin. However, fluctuations in GWLs during the EWR process exhibit high nonlinearity and complexity in their time series, making it challenging for single data-driven models to predict the trend of groundwater level changes under the backdrop of EWR. This study introduced a new GWL prediction strategy based on a hybrid deep learning model, STL-IWOA-GRU. It integrated the LOESS-based seasonal trend decomposition algorithm (STL), improved whale optimization algorithm (IWOA), and Gated recurrent unit (GRU). The aim was to accurately predict GWLs in the context of EWR. This study gathered GWL, precipitation, and surface runoff data from 21 monitoring wells in the Yongding River Basin (Beijing Section) over a period of 731 days. The research results demonstrate that the improvement strategy implemented for the IWOA enhances the convergence speed and global search capabilities of the algorithm. In the case analysis, evaluation metrics including the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) were employed. STL-IWOA-GRU exhibited commendable performance, with MAE achieving the best result, averaging at 0.266. When compared to other models such as Variance Mode Decomposition-Gated Recurrent Unit (VMD-GRU), Ant Lion Optimizer-Support Vector Machine (ALO-SVM), STL-Particle Swarm Optimization-GRU (STL-PSO-GRU), and STL-Sine Cosine Algorithm-GRU (STL-SCA-GRU), MAE was reduced by 18%, 26%, 11%, and 29%, respectively. This indicates that the model proposed in this study exhibited high prediction accuracy and robust versatility, making it a potent strategic choice for forecasting GWL changes in the context of EWR.

Environmental risk of tailings pond leachate pollution: Traceable strategy for leakage channel and influence range of leachate.

Identifying the leakage channel and the influencing range is essential for controlling the environmental risks of leachate from the tailings pond. The investigation of leachate pollution in tailings pond has the defect of focusing only on the scope of tailings pond in recent studies. This study innovatively built a comprehensive investigation and accurate verification system for leachate leakage of tailings pond integrated with the aeromagnetic survey, ground penetrating radar, hydrochemistry and isotope coupling methods. Geophysical exploration found that among the four fault zones, and the F1 was the channel for leachate to recharge the groundwater 2.53 km away from the tailings pond. The fissures inside the tailings pond were connected with the natural fissures outside, forming a leachate migration channel. The hydrochemistry and isotope characteristics showed that the groundwater far away from the tailings pond were polluted by arsenic containing leachate, which verified the geophysical exploration results. The significant correlation between arsenic and SO2-4 concentration indicated that arsenic in leachate originated from the oxidation release of sulfide minerals (i.e., arsenopyrite). This study sheds light on the comprehensive investigation of leachate leakage in the tailings pond. This development method also provides guidance for environmental risk identification of other contaminated sites.