Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM₁₀ concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM₁₀ concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM₁₀ concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM₁₀ concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM₁₀ concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM₁₀ concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM₁₀ concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM₁₀ concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

The dynamics of cyanobacteria bloom in Lake Taihu, China, are subject to rapid fluctuations under the influence of various factors, with meteorological conditions being particularly influential. In this study, monitoring data on the surface area of cyanobacteria bloom in Lake Taihu and observational data from automatic meteorological stations around Lake Taihu from 2016 to 2022 were utilized. Meteorological sub-indices were constructed based on the probability density distributions of meteorological factors in different areas of cyanobacterial bloom. A stacked ensemble model utilizing various machine learning algorithms was developed. This model was designed to forecast the cyanobacterial bloom area index in Lake Taihu based on meteorological data. This model has been deployed with real-time gridded forecasts from the China Meteorological Administration (CMA) to predict changes in the cyanobacteria bloom area index in Lake Taihu over the next 7 days. The results demonstrate that utilizing meteorological sub-indices, rather than traditional meteorological elements, provides a more effective reflection of changes in cyanobacteria bloom area. Key meteorological sub-indices were identified through recursive feature elimination, with wind speed variance and wind direction variance highlighted as especially important factors. The real-time forecasting system operated over a 2.5-year period (2023 to July 2025). Results demonstrate that for cyanobacteria bloom areas exceeding 100 km², the 1-day lead-time forecast hit rate exceeded 72%, and the 3-day forecast hit rate remained above 65%. These findings significantly enhance forecasting capability for cyanobacterial blooms in Lake Taihu, offering critical support for sustainable water management practices in one of China’s most important freshwater systems.