Ecological Risks of Antibiotics in Urban Wetlands on the Qinghai-Tibet Plateau, China

Although the ecological risks of antibiotics have been extensively researched globally, fewer studies have been conducted in sensitive and fragile plateau wetland ecosystems. To evaluate the ecological risk of antibiotics in plateau urban wetlands, 18 water samples, 10 plant samples, and 8 sediment samples were collected in March 2022 in the Xining urban wetlands on the Qinghai-Tibet Plateau. The liquid chromatography-electrospray ionization tandem mass spectrometry method was utilized to measure the concentrations of 15 antibiotics in three categories in three types of environmental media. Risk quotients were adopted to assess the ecological risk of antibiotics, and the principal component analysis-multiple linear regression model was used to analyze the source of antibiotics. The results showed that (1) the maximum concentrations of antibiotics in water samples, plants, and sediments reached 1220.86 ng/L, 78.30 ng/g, and 5.64 ng/g, respectively; (2) Tylosin (TYL), norfloxacin (NFX), ofloxacin (OFX), and ciprofloxacin (CFX) in water were at medium and high-risk levels, and OFX had the highest risk value, of 108.04; and (3) the results of source apportionment indicate that 58.94% of the antibiotics came from the Huangshui river and wastewater treatment plant (WWTP) near the wetlands. The current study may provide a reference for the risks and management of antibiotics in plateau urban wetlands.

Effects of climate changes on net primary productivity variation in the marsh area of the Sanjiang Plain

The Sanjiang Plain includes the largest freshwater marsh in China, playing an important role in regional carbon cycle. As an important indicator of carbon cycle, the net primary productivity (NPP) is a crucial index for estimating the carbon storage of marshy wetlands. Investigating the association between climate factors and NPP variation quantitatively is of great significance for estimating carbon sequestration of marsh. Based on NPP data and climatic data from 1954 to 2014, the spatiotemporal change of NPP in marsh area was analyzed and its association with climate factors was investigated in the Sanjiang Plain in this study. The results indicated that the NPP showed an increase trend in the marsh area of the Sanjiang Plain in the past six decades. Temperate growth made the largest contribution to the NPP increase among the main climate factors in the last six decades, followed by CO2 concentration. Solar Radiation had the largest explanatory power on the spatial distribution of NPP among three climate factors before 1985. After 1985, temperature played an important role in leading the NPP distribution. Results also showed that the explanatory power of interactions between climate factors was stronger than that of single factor. Our results highlight the asymmetric effects of interactions between climate factors on marsh vegetation, which should be adequately considered in estimating carbon sequestration in marsh area in the Sanjiang Plain.

Long Short-Term Memory-based simulation study of river happiness evaluation - A case study of Jiangsu section of Huaihe River Basin in China

Real-time prediction of the state of the river itself and the degree of its benefit to the people is the leading way to achieve human-water harmony. Using the indicator scoring method as the evaluation method, we used the river evaluation data and results with time series characteristics as features and labels and applied the concept of transfer learning to Long Short-Term Memory to establish six subsystems, including water safety, water quality, economic contribution, water ecology, water management and water culture, to conduct a real-time rolling evaluation simulation study on the degree of river happiness in the Jiangsu section of the Huaihe River Basin in China. The empirical results show that the maximum Root Mean Square Error (RMSE) of the training set and test set of each system is 0.0226, and the lowest coefficient of determination R2 is 0.9011, which proves that the model fits well, according to which the relevant data of the watershed in June 2022 are brought in, and the evaluation result is obtained as 89.77 points. The overall trend is good, but a certain tendency to fall back at the level of economic contribution can be found, and the reasons are analyzed objectively.