太湖蓝藻水华暴发程度年度预测

近年来,尽管太湖主要水质指标有所改善,但蓝藻水华暴发的频次和面积并未明显减少。为了探讨太湖蓝藻水华暴发的环境驱动因子,统计了2012—2020年历年4—10月预警期间的太湖蓝藻水华发生规模与频次,结合同步浮标自动监测数据和实验室分析数据,构建了蓝藻水华预测模型。以太湖蓝藻水华综合指数(Ic)表征蓝藻水华强度,并通过Ic与环境因子的相关性分析,筛选出1月水温、1月电导率、1月生化需氧量和3月总氮浓度4项环境指标,最终构建了以该4项环境指标为自变量、Ic为因变量的太湖年度蓝藻水华强度多元线性回归预测模型。该预测模型的决定系数达到了0.908,平均相对误差为10.35%,预测精度总体表现较好。

Annual Forecast of the Extent of Cyanobacteria Bloom in Taihu Lake

In recent years,the frequency and area of cyanobacteria blooms in Taihu Lake have not been significantly reduced,despite the improvement of the main water quality indicators.In order to investigate the environmental factors driving the cyanobacteria blooms in Taihu Lake,the scale and frequency of cyanobacteria blooms in Taihu Lake during the April-October warning period in the calendar years 2012-2020 were calculated,and a prediction model for cyanobacteria blooms was constructed by combining automatic monitoring data from synchronous buoys and laboratory analysis data.The cyanobacteria bloom intensity was characterized by the cyanobacteria bloom composite index (I_c),and the correlation between I_c and environmental factors was analyzed.Four environmental indicators,namely water temperature in January,conductivity in January,biochemical oxygen demand in January and total nitrogen concentration in March,were selected,and a multiple linear regression prediction model for the annual cyanobacteria bloom intensity in Taihu Lake,with the four environmental indicators as independent variables and I_c as dependent variable,was finally constructed.The results showed that the coefficient of determination of the prediction model reached 0.908,with an average relative error of 10.35%,and the accuracy was generally good.