【摘要】 目的了解郑州市某地面水源不同季节藻类分布,并建立蓝藻细胞密度的预测模型。方法2004年3月到10月间,在该水源设2个采样点,采用标准方法监测水温(tW)、透明度、水深、总氮、总磷(TP)和各藻门细胞密度等指标;采用描述统计方法了解不同季节藻类分布,应用直线相关分析和多元回归方法探讨蓝藻细胞密度与环境因子之间的关系,并初步建立蓝藻细胞密度的预测模型。结果春季优势藻门是绿藻和硅藻,夏、秋季优势藻门是蓝藻和绿藻。标准化蓝藻细胞密度(ln(CCD+1))与tW、TP呈正相关,与透明度、水深呈负相关;蓝藻细胞密度的预测模型为ln(CCD+1)=1.004+0.232tW+22.364TP(R2=0.842,F=58.773,P<0.01),应用该方程计算出的预测值能够较好地与实测值拟合。结论环境条件不同将导致藻类生长情况不同;削减水中TP含量在控制蓝藻细胞密度方面具有现实意义。更多还原

【Abstract】 Aim: To research the distribution of algae and establish prediction model of cyanophyta cell density(CCD) in some surface water source of Zhengzhou. Methods: Water temperature(t_W),secchi-depth(SD), water depth(WD), total nitrogen( TN), and total phosphorus(TP) were determined in two sampling stations of the water source by standardized methods from Mar. 2004 to Oct. 2004. The distribution of algae was analyzed by descriptive statistics. The influence of some environmental factors on CCD was analyzed by correlation analysis and stepwise multiple regression. CCD was predicted by establishing a stepwise multiple regression equation. Results: The dominant algae were chlorophyta and bacillariophyta in spring, cyanophyta and chlorophyta in summer and autumn. Significant positive correlations were found between the standardized CCD and t_W, TP. Significant negative correlations were found between the standardized CCD and SD, WD. The equation was ln(CCD+1)=1.004+0.232t_W+22.364TP(R2=0.842, F=58.773,P<0.01), and the content of CCD could be predicted perfectly. Conclusion: The growth of algae is different under different environmental condition. It is of significance to decrease the content of TP so as to control CCD effectively.更多还原