Due to the high altitude, unique geographical location, difficult accessibility and low temperature, the environmental factors influencing phytoplankton composition have rarely been investigated in the Selin Co Lake, which is the largest lake in the Tibetan Plateau. Phytoplankton composition can indicate aquatic ecosystem conditions, which may be sensitive to environmental factors in the Tibetan Plateau. In this study, we investigated the main environmental factors that influence phytoplankton species in the Selin Co Lake by analyzing the spatial distribution and applying statistical analyses. We also compared the influential environmental factors in this lake with other lakes around the world. The results suggest that the eleven environmental variables can explain about 46.78% of the phytoplankton’s composition. DO and fluoride were the most significant environmental variables, followed by arsenic and COD, and the other variables had comparatively smaller and more insignificant influences on phytoplankton composition. There were five dominant phytoplankton species in the Selin Co Lake, namely, Microcystis sp., Navicula spp., Chlorella vulgaris, Ankistrodesmus falcatus, and Westella sp. Some of these dominant species were also found in other tropical lakes, suggesting that the phytoplankton community could adapt to environmental changes. A clear understanding of the influential environmental variables affecting phytoplankton composition could help us to make proper water quality protection strategies in future climate change scenarios.

Paddy runoff pollution is one of the major contributors to limiting the improvement of water quality in Taihu Lake Basin. Grass buffer strips (GBSs) are an effective measure to control paddy runoff pollution. However, most studies only consider a single inflow condition, and few studies have considered the effect of high-frequency rainfall. In this study, a field runoff simulation experiment was constructed to simulate the effect of GBSs on runoff nitrogen and phosphorus removal at different inflow volumes, inflow velocities, inflow concentrations, and rainfall frequencies. Results demonstrated that the larger the inflow volume, the faster the inflow velocity, and the lower the inflow concentration, the higher the runoff pollutant interception rate that occurred in GBSs, and the interception rate improved significantly with increasing GBS widths. The peak change point of removal rate occurred at a width of 15 m for NO₃⁻-N and TP and at a 25 m width for TN and NH₄⁺-N. The cumulative removal rate increased slowly after the change point. Although the peak cumulative removal rate appeared at a GBS width of 35~45 m. Considering the pollutants intercepted by GBSs and the emerging demand for land in this basin, 25 m was recommended as the optimum width to remove runoff pollutants.