Water level fluctuation (WLF) has a significant effect on aquatic macrophytes, but few experimental studies have examined the effect of WLF on submerged community succession, especially from a large-scale perspective. In this study, field monitoring of WLF and submerged macrophytes was conducted in Yilong Lake (SE China) over two years, and the impacts of WLF on the growth characteristics and the community structure of submerged macrophytes were determined. The results show that the biomass of submerged macrophytes decreased significantly after the water level increased and submerged macrophytes could cope with the adverse environment by adjusting their growth posture, for example, increasing plant length and reducing branch number. However, different submerged plants have different regulatory abilities, which leads to a change in the community structure. Myriophyllum spicatum, Stuckenia pectinata, and Najas marina had better adaptation abilities to WLF than Najas minor and Utricularia vulgaris. Changes in water depth, dissolved oxygen, and transparency significantly contribute to the effect of WLF on submerged plant communities. Therefore, when determining the range of WLF, the above three critical factors and submerged plant species should be considered. WLF changed the spatial distribution of the aquatic plant community. When water levels rose, the density of the submerged macrophyte community in the original growth region reduced as the emergent plants migrated to shallower water, and the seed bank germination was aided by transparent water produced among emergent plants. This can be used as a pioneering measure to restore submerged plants in eutrophic lakes with low transparency. In addition, a suitable water depth created by WLF was conducive to activating the seed bank and improving the diversity of aquatic plants. Finally, a distribution map of aquatic plants in Yilong Lake is drawn.

Beta diversity represents the spatial or temporal variation of species diversity among sampling sites and may be composed of two elements: turnover (Brepl, replacement of species assemblages) and nestedness (Brich, loss or gain of species assemblages). Knowledge of the mechanisms driving beta diversity contributes to an understanding of the variation in aquatic ecosystem community structures. We sampled zooplankton assemblages at 24 sites in 11 rivers in Shanghai City and conducted a nutrient addition experiment to elucidate the effects of various environmental variables on the community structure and beta diversity patterns of the zooplankton. The zooplankton assemblages in the rivers differed significantly at ammonia nitrogen (NH₃-N) concentrations below (Group I) and above (Group II) 1.03 mg·L⁻¹. The nutrient addition experiment further demonstrated that the composition of the zooplankton assemblages changed markedly along an ammonia concentration gradient (0.2 to 5.0 mg N·L⁻¹). The total beta diversity of Group I was considerably higher than that of Group II, indicating that high nutrient (ammonia) pollution led to biotic homogeneity. Overall, turnover was the key factor determining the total beta diversity of the two groups, suggesting the key importance of replacement of zooplankton assemblages. Furthermore, we found a correlation between environmental factors (mainly nitrogen content) and the beta diversity of zooplankton, and beta diversity (Brepl and Btotal) decreased with increasing trophic state. These findings provide further insight into the changing characteristics of the beta diversity patterns of zooplankton in river networks and may help to guide managers dealing with conservation strategies for aquatic biodiversity preservation in urban river ecosystems.

In today’s market, consumers appear to be less interested in promotional strategies, particularly those that rely on text-based advertisements. Graphic storytelling can be seen as providing a more engaging visual approach to attract audiences and is increasingly being used by marketers and food packaging designers. However, the questions of whether and how graphic storytelling influences consumers’ purchase intentions remain underexplored. Based on the Transportation–Imagery Model, two experimental studies were conducted to examine the effect of graphic storytelling on narrative transportation and food purchase intention, and to explore its underlying mechanism from the perspective of cognitive fluency. The results demonstrated the positive effect of graphic storytelling on narrative transportation (Studies 1 and 2), as well as a significant impact on food purchase intention (Study 2). Furthermore, cognitive fluency was identified as a critical factor impacting narrative transportation, facilitated by graphic storytelling (Studies 1 and 2). This study extends the Transportation–Imagery Model by positioning cognitive fluency as an important antecedent of narrative transportation. Practically, the suggestion would be for restaurants and food firms to optimize their advertising by displaying cooking processes, particularly for part-prepared foods.

Black-odor water, which is caused by the excessive accumulation of nitrogen and phosphorus in water, is a significant problem. Immobilized microorganisms are considered to be an effective technical solution, but there are still many key parameters to be determined, such as organic matter dissolution, insufficient stability, and insufficient phosphorus removal capacity, among other problems. In this study, the optimum raw material ratios of immobilized microorganism gel beads were determined by means of a response surface experiment. The optimal ratio of raw materials was 5% polyvinyl alcohol (PVA), 1% sodium alginate (SA), and 6% bacterial powder. In addition, the nitrogen and phosphorus removal performance of the materials was improved by loading inorganic compounds, such as 0.5 wt.% zeolite, 0.5 wt.% iron powder, and 0.2 wt.% activated carbon. Tolerance analysis determined that these gel beads could maintain a good performance in a series of harsh environments, such as during intense agitation, at high temperatures, and at low pH values, etc. The total nitrogen (TN), ammonia nitrogen (NH₃-N), and phosphorus (TP) removal efficiencies were 88.9%, 90%, and 95%.

Air pollution forecasting, particularly of PM_2.5 levels, can be used not only to deliver effective warning information to the public but also to provide support for decisions regarding the control and treatment of air pollution problems. However, there are still some challenging issues in air pollution forecasting that urgently need to be solved, such as how to handle and model outliers, improve forecasting stability, and correct forecasting results. In this context, this study proposes an outlier-robust forecasting system to attempt to tackle the abovementioned issues and bridge the gap in the current research. Specifically, the system developed consists of two parts that deal with point and interval forecasting, respectively. For point forecasting, a data preprocessing module is proposed based on outlier handling and data decomposition to mitigate the negative influences of outliers and noise, which can also help the model capture the main characteristics of the original time series. Meanwhile, an outlier-robust forecasting module is designed for better modeling of the preprocessed data. For the model to further improve its accuracy, a nonlinear correction module based on an error ensemble strategy is developed that can provide more accurate forecasting results. Finally, the interval forecasting part of the system is based on a newly proposed artificial intelligence–based distribution evaluation and the results of the point forecasting part to present the range of future changes. Experimental results and analysis utilizing daily PM_2.5 concentration from two provincial capital cities in China are discussed to verify the superiority and effectiveness of the system developed, which can be considered an effective technique for point and interval forecasting of daily PM_2.5 concentration.