摘要
目的了解郑州市某地表水源藻类与环境因子的关系,并建立预测模型。方法2004年3~10月间,在该水源设2个采样点,采用标准方法监测:水温(WT)、透明度(SD)、水深(WD)、高锰酸盐指数(CODMn)、总氮(TN)、总磷(TP)、叶绿素a(Chla)和藻类细胞密度(ACD)8项指标,应用直线相关分析和多元回归方法探讨Chla和ACD与环境因子之间的关系,并初步建立叶绿素a和藻类细胞密度的预测模型。结果直线相关分析显示,该水源标准化叶绿素a与WT、CODMn、TP呈显著正相关,与SD呈显著负相关;标准化藻类细胞密度与WT、TP呈显著正相关,与SD、WD呈显著负相关。多元回归统计分析显示,WT、TP和CODMn为该水源标准化叶绿素a的显著相关因子,WT和TP为该水源标准化藻类细胞密度的显著相关因子,应用回归方程计算出来的预测值能够较好地与实测值拟合。结论建立的多元回归方程能够较好地预测Chla和ACD的变化趋势;削减TP和CODMn含量在控制Chla和ACD方面具有现实意义。
Objective To analyze the relations between algae and related environmental factors and make the model prediction, Methods Eight indices were determined in two sampling sites by standardized methods from Mar 2004 to Oct 2004 in the water resource. The influence of some environmental factors on chlorophyll- a（Chla）and algae cell density（ACD）was analyzed via correlated analysis and stepwise multiple regression. In addition, the contents of Chla and ACD were predicted by building stepwise multiple regression equations respectively. Results The results of correlated analysis showed that significant positive correlations were found between the standardized Chla and water temperature（WT）, chemical oxygen demand （CODMn）total phosphorus（TP）and between the standardized ACD and WT, TP; significant negative correlations were loud between the standardized Chla and secchi - depth（SD）and between the standardized ACD and SD, water depth（WD）. Furthermore, the results of stepwise multiple regression showed that WT, CODMn and TP were the most significant related factors to the standardized Chla;WT and TP were the most significant related factors to the standardized ACD. The contents of Chla and ACD could be predicted by the equations perfectly. Conclusion The contents of Chla and ACD are predicted by the multiple regression equations perfectly. It is very significant to decrease the contents of TP and CODM^in controlling the contents of Chla and ACD effectively.
出处
《中国公共卫生》
CAS
CSCD
北大核心
2006年第1期58-60,共3页
Chinese Journal of Public Health
基金
河南省医学创新人才工程资助项目(2003.08)
关键词
多元逐步回归
叶绿素A
藻类细胞密度
预测模型
stepwise multiple regression
chlorophyll - a
algae cell density
model prediction
