Effects of air quality and vegetation on algal bloom early warning systems in large lakes in the middle–lower Yangtze River basin

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM₁₀), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O₃), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH₄⁺ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.