Polyaluminum coagulant with a content of 76.8% of Al30 (PACAl30) was prepared. Its coagulation behaviors were compared with high Al13 content polyaluminum coagulant (PACAl13) and AlCl3. The species stability was studied using Al-Ferron method and 27Al NMR. The coagulation performances were investigated by studying the rate of flocs development, the turbidity removal efficiency and charge neutralization capacity under fixed pH conditions and uncontrolled pH conditions. The effect of pH on coagulation was also studied. The results show that PACAl30 are stable for using as coagulant. PACAl30 causes less pH depression than PACAl13. The charge neutralization capacity of PACAl30 is slightly lower than that of PACAl13 at pH6.8 and higher at pH 6.5. PACAl30 achieves the most effective turbidity removal in these three coagulants. And it acts effectively within a much broader dosage range and a wider pH range when compared with PACAl13. PACAl30 achieves the highest turbidity removal due to its strong flocs formation capacity. The results verify that Al30 is another highly active coagulation/flocculation species for turbidity removal. 相似文献
The problem of algal bloom caused by eutrophication has attracted global attention. Many scholars have studied the problem associated with algae bloom, but few have carried out dynamic monitoring, instead focusing on the formation mechanism of cyanobacteria. For our study of the Taihu Lake in China, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat remote sensing image data from 2017 to establish a prediction model. First, we used MODIS data to retrieve the concentration of N, P, and chlorophyll a in water. Then, we applied the analytic hierarchy process (AHP) model to the inversion results to construct the diffusion potential index. Finally, we used C# to compile the cellular automata (CA) model. We found that the distribution of cyanobacteria predicted by our method was consistent with the algal bloom situation of Taihu Lake in 2017. The results showed that the method effectively predicts the dynamic transfer of cyanobacteria from outbreak to diffusion in a short period of time, which can help decision-makers monitor lake health.