Water level is an important indicator of lake hydrology characteristics, and its fluctuation significantly affects lake ecosystems. In recent years, deep learning models have shown their superiority in the long-time range prediction of hydrology processes, while the application of deep learning models with the attention mechanism for lake water level prediction is very rare. In this paper, taking Poyang Lake as a case study, the transformer neural network model is applied to examine the model performance in lake water level prediction, to explore the effects of the Yangtze River on lake water level fluctuations, and to analyze the influence of hyper-parameters (window size and model layers) and lead time on the model accuracy. The result indicated that the transformer model performs well in simulating the lake water level variations and can reflect the temporal water level variation characteristics in Poyang Lake. In the testing stage, the RMSE values were recorded in the range of 0.26–0.70 m, and the NSE values are higher than 0.94. Moreover, the Yangtze River inflow has a great influence on the lake water level fluctuation of Poyang Lake, especially in flood and receding periods. The contribution rate of the Yangtze River in RMSE and NSE is higher than 80% and 270%, respectively. Additionally, hyper-parameters, such as window size and model layers, significantly influence the transformer model simulation accuracy. In this study, a window size of 90 d and a model layer of 6 are the most suitable hyper-parameters for water level prediction in Poyang Lake. Additionally, lead time may affect the model accuracy in lake water level prediction. With the lead time varied from one to seven days, the model accuracy was high and RMSE values were in the range of 0.46–0.73 m, while the RMSE value increased to 1.37 m and 1.82 m with the lead time of 15 and 30 days, respectively. The transformer neural network model constructed in this paper was the first to be applied to lake water forecasting and showed high efficiency in Poyang Lake. However, few studies have tried to use transformer model coupling with the attention mechanism for forecasting hydrological processes. It is suggested that the model can be used for long sequence time-series forecasting in hydrological processes in other lakes to test its performance, providing further scientific evidence for the control of lake floods and management of lake resources.

Nanoflower-like MoS₂ deposited on the surface of rectangular CaTiO₃(CTO) was designed and synthesized via a simple template-free strategy. Through SEM, TEM, and other characterization methods, the MoS₂ nanoflowers were confirmed to be well deposited on the surface of CTO. LED was used as the visible light source, and rhodamine B (RhB) in an aqueous solution was used as the model pollutant to assess the photodegradation activity of the samples. The results showed that the MoS₂/CaTiO₃(MCTO) composite significantly improved the photocatalytic degradation of rhodamine B (RhB) in water, compared with a single CTO, and with the MCTO-2 composite photocatalysts, 97% degradation of RhB was achieved in 180 min, and its photocatalytic activity was about 5.17 times higher than that of the bare CTO. The main reasons for enhancing photocatalytic performance are the strong interaction between the nanoflower-like MoS₂ and rectangular CTO, which can lead to the effective separation of electron transfer and photoexcited electron–hole pairs in MCTO composites. This work provides a new notion for researching an effective method of recycling catalytic materials.

Controlling the concentration of free radicals in polymerization systems is advantageous for preparing cationic polyacrylamide (CPAM) with a high molecular weight and acceptable dissolvability. In this study, a novel ultraviolet (UVA)-initiated system characterized by an adjustable light intensity and a redox-azo complex initiator was used to synthesize a CPAM flocculant named NP. Comparatively, another CPAM flocculant named SP with a stable UVA light intensity and a single initiator was prepared. Their chemical structure, morphology, and thermal stability were analyzed through instrumental analysis. ¹H NMR indicated that NP was successfully prepared, and a small fraction of a cationic homopolymer was mixed in the product. Polymerization conditions of NP were optimized, and polymerization mechanism was determined by investigating the effects of various parameters on intrinsic viscosity, conversion, and dissolvability. Results showed that the best performance was obtained at indices of 0.45 wt‰ redox initiator concentration, 0.2 wt‰ azo initiator concentration, 40.0 wt% cationic monomer, 3 wt‰ urea, and first- and second-stage light intensities of 8.5 and 13 mW/cm², respectively. The sludge conditioning performances of NP and SP were comparatively evaluated, and the mechanism was determined by examining sedimentation behavior and floc size distribution. High intrinsic viscosity, porous morphology structure, and mixed NP cationic homopolymer contributed to the improvement of sludge conditioning performance.

Changes in land use types can alter the soil and environmental characteristics of wetlands, which in turn influence the magnitude of greenhouse gas production by soil microbes. However, the effects of land use change on the production potential of methane (CH₄) and carbon dioxide (CO₂) in subtropical wetland soils and the underlying controls are still largely unknown. In this study, we examined the soil CH₄ and CO₂ production potentials under five different land use types (natural mangrove, Gei Wai water channel, Gei Wai forest, reedbed, and freshwater pond) and their relationships with soil physico-chemical properties in a subtropical wetland in Hong Kong using aerobic and anaerobic laboratory incubation experiments. Our results showed an overall decreasing trend of CH₄ and CO₂ production potentials down the soil profile at all sites, which could be attributed to a reduction in the concentrations of soil organic matter (SOM), total Kjeldahl nitrogen (TKN) and ammonium nitrogen (NH₄⁺-N). Moreover, the soil CH₄ and CO₂ production potentials varied significantly in the surface soils among land use types, but were more similar across the sites in the deeper soils. The conversion of natural mangrove to other land use types significantly reduced both the aerobic and anaerobic CO₂ production potentials in the top 10 cm soils, except for Gei Wai forest, which demonstrated significantly higher CO₂ production rates (61.15–97.91 μg g⁻¹ day⁻¹). Meanwhile, the mean CH₄ production potential in the surface soils of natural mangrove (0.05 μg g⁻¹ d⁻¹) was significantly lower than that in the Gei Wai forest and Gei Wai channel (0.26–0.27 μg g⁻¹ day⁻¹) but slightly higher than that in the freshwater pond and reedbed (0.00–0.02 μg g⁻¹ day⁻¹). The high soil CH₄ and CO₂ production potentials observed in the Gei Wai forest could be explained by the high soil concentrations of SOM, TKN and NH₄⁺-N. On the other hand, the lower anaerobic CH₄ and aerobic CO₂ productions observed in the reedbed could be attributed to the lower concentrations of NH₄⁺-N and available phosphorus. Our findings highlighted the significant impacts of land use types on the CH₄ and CO₂ production potentials of subtropical wetland soils, which had practical implications for wetland management for climate change mitigation.

Thermal stratification is a common phenomenon in lakes and reservoirs. It has a significant influence on water quality dynamics. The destruction of the thermal stratification of lakes and reservoirs can affect the water environment, improve the water quality and the water environment quality and prevent the occurrence of eutrophication. In this study, computational fluid dynamics (CFD) combined with a Eulerian two-phase flow model is used to predict the damage caused by an airlift device to the thermal stratification phenomenon of lake water. The results show that the two devices with different sizes can contribute to a certain exchange of kinetic and thermal energy, affecting the liquid velocity and temperature in the lake water under the condition of different gas velocities. Furthermore, the degree of damage to the thermal stratification phenomenon of lake reservoir is small. However, with the same gas velocity, the device with a guide plate can have a greater impact on the liquid velocity and temperature in the lake water. Further prediction results show that the airlift installed with a guide plate can affect the heat transfer of liquid in the lake and reservoir better and destroy the thermal stratification phenomenon effectively. The quantitative results of model prediction can provide an effective basis for future field scale-up experiments.