This paper proposed a newly explored composite photocatalyst, Chitin/ZnO, prepared via the sol-gel method for exploring its photocatalytic activity in the simulated aquaculture wastewater under UV irradiation. The study mainly involves the application of Chitin/ZnO from three aspects: the structure, the principle and the degradation efficiency. The effects of purification operation factors including mass ratio rate, dosage, calcination temperature, initial NH₄⁺–N concentration and illumination conditions on the NH₄⁺–N removal effectiveness were investigated. Optimum conditions were explored through orthogonal experiments, which revealed that 88.73% NH₄⁺–N removal from 60 mg/L synthetic wastewater was achieved by direct illumination for 120 min. Additionally, Chitin/ZnO photocatalysts (mass ratio of 2:3) at a calcination temperature of 500 °C were favorable for Chitin loaded over a ZnO lattice. The obtained nanoparticles of Chitin/ZnO were characterized using SEM and X-ray diffraction. The purpose of this paper is to grope for an economical and easy method of Chitin/ZnO powder preparation and to provide a practical approach for future research on the photocatalytic purification of aquaculture wastewater.

Triclosan has been widely used as addition ingredient in personal care and medical antibacterial products, and the increasing amounts of triclosan discharged in aquatic environments pose a potential risk to aquatic ecological systems. In this study, we investigated the effects of exposure to varying triclosan concentrations on the growth, chlorophyll fluorescence and antioxidant enzyme activity of Chlorella vulgaris. The results showed that low-concentration triclosan (<0.75 mg/L) can stimulate the growth of Chlorella vulgaris, whereas 1.05 mg/L triclosan exhibited significant inhibition. Low-concentration triclosan (<0.75 mg/L) could improve the tolerance and utilization ability of Chlorella vulgaris in relation to strong light. We observed a significant increase in the malondialdehyde content of Chlorella vulgaris exposed to 1.05 mg/L triclosan. The intracellular superoxide dismutase and catalase (CAT) activities of Chlorella vulgaris exposed to triclosan were higher than the control groups, and the increase in this activity was positively correlated with the concentration of triclosan. The results also showed that excessive H₂O₂ may in turn damage the CAT structure and eventually inactivate CAT activity when Chlorella vulgaris is exposed to 1.05 mg/L triclosan. This study provided a theoretical basis which can be used to evaluate the ecological risk of triclosan in the aquatic environment.

In the article titled “Projected Changes in Precipitation Extremes over Shaanxi Province, China, in the 21st Century” [1], 2 additional references should have been cited included in the text below as references 43 [2] and 44 [3]. Accordingly, the sentence referring to the model output data in Section 2.2, Model and Data, should read as follows.

Global warming has made water resources more uneven in space and time, making water management harder. This study used the HBV-XGBoost model to see how climate change affects runoff in the Xiangxi River Basin. The HBV model simulated water processes, and XGBoost improved predictions by handling complex relationships. This study used the SDSM to create climate data for 2025–2100 and looked at runoff trends under different emission scenarios. The HBV-XGBoost model performed better than the HBV model in simulating runoff. Future predictions showed big differences in runoff trends under various SSP scenarios in the 2040s and 2080s. For example, under SSP585, the ACCESS-CM2 model projected a May runoff increase from 1527.52 m³/s to 2344.42 m³/s by the 2080s, and ACCESS-ESM1-5 projected an increase from 1462.11 m³/s to 2889.58 m³/s. All GCMs predicted a large rise in annual runoff under SSP585 by the 2080s, with FGOALS-g3 showing the highest growth rate of 76.54%. The model accurately simulated runoff changes and provided useful insights for adapting water management to climate change. However, this study has limitations, including uncertainties in machine learning models, potential input data biases, and varying applicability under different conditions. Future work should explore more climate models and downscaling methods to improve accuracy and consider local policies to better address climate impacts on water resources.

This study proposes a multi-objective model (i.e. MOHRA) for groundwater remediation in a petroleum-contaminated site under health risk assessment. The optimization system is designed based on the pump and treat technology considering both cost and human health risk, which can provide reliable groundwater remediation strategies.