Ulva prolifera is the major causative species in the green tide, a serious marine ecological disaster, which bloomed in the Yellow Sea and the Bohai Sea of China. However, it is also a popular edible seaweed and its extracts exerts anti-inflammatory and antioxidant effects. The present study investigated the effects of ethanol extract of U. prolifera (EUP) on insulin sensitivity, inflammatory response, and oxidative stress in high-fat-diet- (HFD-) treated mice. HFD-treated mice obtained drinking water containing 2% or 5% EUP. The results showed that EUP supplementation significantly prevented HFD-induced weight gain of liver and fat. EUP supplementation also improved glucose tolerance and insulin resistance in HFD-treated mice. Moreover, EUP supplementation prevented the increased expression of genes involved in triglyceride synthesis and proinflammatory genes and the decreased expression of genes involved in fatty acid oxidation in liver of HFD-treated mice. Furthermore, EUP supplementation decreased reactive oxygen species content, while increasing glutathione content and glutathione peroxidase activity in HFD-treated mice. In conclusion, our results showed that EUP improved insulin resistance and had antilipid accumulation and anti-inflammatory and antioxidative effects on HFD-treated mice. We suggested that U. prolifera extracts may be regarded as potential candidate for the prevention of nonalcoholic fatty liver disease.

Green tides have increasingly become a nuisance worldwide in recent years, and especially in China. Since 2015, green tides have started recurring in Jinmeng Bay, Qinhuangdao, western Bohai Sea of China, and have severely deteriorated the tourism environment there. In order to investigate the migration process of the green tides in Jinmeng Bay, a hydrodynamic model and a particle-tracking model were applied based on the latest green tide event in August 2021. The hydrodynamic model was applied with triple-level 2DH meshes with different refinements and scales, which provided the hydrodynamics to drive the green macroalgae into the particle-tracking model. From the model results, the semi-enclosed waters surrounded by multiple artificial structures are a low-energy hydrodynamic environment, which is not helpful for water exchange and thus the dispersal of nutrients. The green macroalgae are distributed substantially within the semi-enclosed waters, and few are transported out with low biomass. The effects of wind and artificial structures both increase the coverage of the green macroalgae trajectories; the effect of wind plays a more important role. A sensitivity analysis of the effect of wind showed that 6 m/s wind in ENE led to the maximum coverage of the green macroalgae trajectories in the cases of different magnitudes and directions of winds. This study can provide references for the pre-warning and mitigation of green tides in Jinmeng Bay and other similar places.

Field observations were combined with a coupled hydrodynamic and water quality model to investigate the spatial and temporal variation in Chlorophyll-a (Chl-a) in Jinmeng Bay, China. The relatively high Chl-a values were distributed in the inshore waters, mainly due to the abundant nutrient inflow from the Tanghe River. The model’s results indicate that the Chl-a concentration was much higher in seaweed beds surrounded by artificial islands and reefs under the southeasterly wind, largely due to the fact that pollutants are prone to accumulate in coastal areas where flow is attenuated by the presence of natural and artificial marine structures. It was also found that the southwesterly winds suppress the inflow of nutrients from the Tanghe River to the coastal areas, and, therefore, lower the Chl-a levels. River input and wind forcing are the major factors that influence Chl-a concentrations in the anthropogenically influenced bay. This finding provides useful guidance for the prediction and mitigation of green tides in Jinmeng Bay in the future.