Early Warning Of Cyanobacterial Blooms In Drinking Water Sources Of Taihu Lake,Suzhou

Safety of drinking water is of great significance to the sustainable development of economy and society in Taihu Lake Basin.The outbreak of cyanobacterial blooms in Taihu Lake is a prominent problem of water environment,which has an important impact on the safety of drinking water.Taking Yuyangshan drinking water source area of Taihu Lake in Suzhou as the research object,this paper studied and analysed the outbreak situation,characteristics and changing trend of cyanobacterial blooms in Taihu Lake water source area in recent years,monitored the related indicators of water source and water,choosed appropriate predictive indicators of cyanobacterial blooms,screened and determined the corresponding sensitive environmental factors,and established the prediction model of cyanobacterial blooms in Taihu water source area of Suzhou based on the relationship between the two variables.The early warning threshold of cyanobacteria blooms in Taihu Lake was determined and the early warning method of cyanobacteria blooms in Taihu Lake source area in Suzhou was established.Scientific and effective warning of cyanobacterial blooms in water source areas can provide guidance for the prevention and control of cyanobacterial blooms in drinking water source areas of Taihu Lake in Suzhou,which is of great significance to the safety of drinking water.The main contents and conclusions of this paper are as follows:(1)The analysis of the outbreak status and characteristics of cyanobacteria blooms in Taihu Lake reflects that the outbreak degree,frequency and scale of cyanobacteria blooms in Taihu Lake show an upward trend in recent years,and the situation of cyanobacteria blooms in Taihu Lake is not optimistic.In 2016,the number of cyanobacteria in Taihu Lake has exceeded 80 million per liter,and its degree has reached a serious level.From2014 to 2017,there were 387 outbreaks of cyanobacteria blooms,and the proportion structure of outbreaks gradually tended to major cyanobacteria blooms.The largest cyanobacteria blooms(1403 km~2)appeared in May 2017,reaching the major cyanobacteria blooms level,and the outbreaks of the two high-incidence periods gradually expanded each year.(2)The analysis of monitoring data of water quality indicators of Taihu Lake in Suzhou reflects the distribution and dynamic change trend of chlorophyll a,olfactory substances and other water quality indicators.The variation characteristics of chlorophyll a and olfactory substances are consistent with that of cyanobacterial blooms,and the maximum value of chlorophyll a and olfactory substances occurs in the high-incidence period of cyanobacterial blooms.The concentration of chl-a ranged from 10.8 ug/L to 55.6ug/L,the concentration range of GSM was 1.75 ng/L to 5.72 ng/L,the concentration range of 2-MIB was 3.93 ng/L to 11.83 ng/L,and the concentration range of?-Ionone was 4.92ng/L to 13.38 ng/L.Therefore,chl-a and odor substances were used as indicators of cyanobacterial bloom prediction.Other water quality indicators except pH and TP showed a significant trend of change,and some indicators showed a correlation with cyanobacteria bloom.(3)Based on the results of correlation analysis and principal component analysis,the sensitive factors of chl-a and odor substances were screened out.The sensitive factors of chlorophyll a were total phosphorus,total nitrogen,ammonia nitrogen,permanganate index,dissolved oxygen,water temperature and chroma;the sensitive factors of odorant were total phosphorus,total nitrogen,ammonia nitrogen and dissolved oxygen;the sensitive factors of 2-methyl isocamptol were total phosphorus,total nitrogen,ammonia nitrogen,permanganate index,water temperature and dissolved oxygen;the sensitive factors of beta-ionone were total phosphorus,total nitrogen,ammonia nitrogen and permanganate.Index and dissolved oxygen.(4)Establish the prediction model of cyanobacterial blooms based on multiple regression analysis and BP neural network,and test the model,compare and evaluate its applicability.The regression coefficients(Adjustment of R~2)of chl-a,GSM,2-MIB and?-Ionone were 0.932,0.369,0.503 and 0.468,respectively.The relative errors were0.74%~19.27%,8.63%~35.25%,13.29%~30.58%and 10.91%~30.30%,respectively.The relative errors of BP neural network model were 0.66%~10.59%,1.19%~9.10%,0.87%~8.91%and 0.73%~11.58%,respectively.Analysis and comparison showed that the multivariate regression analysis of olfactory substances was poorly effective,the effect of chl-a models is good,BP neural network model had better prediction effect than regression analysis model.Regression analysis model is mainly suitable for the prediction of dynamic change trend,and can be used for qualitative prediction.BP neural network model can not only make effective qualitative prediction,but also make more accurate quantitative prediction..(5)The early warning thresholds of cyanobacteria blooms in Taihu Lake were preliminarily explored.The threshold values of dissolved oxygen for small and medium cyanobacteria blooms were 6.23 mg/L and 3.90 mg/L,respectively.The threshold values of permanganate index for small,medium and large cyanobacteria blooms were 5.118 mg/L,6.017 mg/L and 7.412 mg/L.The threshold values of dissolved oxygen for odorant olfaction were 6.05mg/L;the threshold values of 2-methyl isocamphthenol OLS for dissolved oxygen and ammonia nitrogen were 7.17mg/L,respectively.0.037 mg/L.The theoretical method of cyanobacterial blooms early warning in drinking water source area of Taihu Lake in Suzhou was established,which could provide ideas and theoretical guidance for cyanobacterial blooms early warning in drinking water source area of Taihu Lake in Suzhou in the future.