JWRR  >> Vol. 5 No. 1 (February 2016)

    深圳某水库水华藻类及相关环境因子逐步回归统计分析
    Statistical Regression Analysis of Water-Bloom Algaes and Related Environment Factors of a Reservoir in Shenzhen City

  • 全文下载: PDF(551KB) HTML   XML   PP.33-39   DOI: 10.12677/JWRR.2016.51004  
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作者:  

杨 滢,王 艳,黄文辉,王炜铭:武汉大学资源与环境科学学院,湖北 武汉

关键词:
藻类生物量多元逐步回归环境因子预测Algae Biomass Multi-Statistic Method of Stepwise Environmental Factor Prediction

摘要:

以深圳市茜坑水库5个监测点连续5年的监测资料为基础,选择总磷、水深等环境理化因子,以及藻类生物量、叶绿素a和蓝藻生物量等生物因素,利用SPSS软件,进行多元逐步回归分析,分别研究与生物因素显著相关的环境理化因子。并在此基础上,采用逐步回归统计方法,利用与生物因素显著相关的环境因子,建立了茜坑水库藻类生物量变化的初步预测模式。结果表明,剔除次要环境因子后,水库藻类生物量变化主要受水库的水深,水温及总磷的影响,但不同监测点的显著因子有差异,说明藻类生长因环境不同,限制因子也不同。逐步回归统计模型,能够寻找影响藻类生物量的关键性因子,为准确预测提供参考,但尚不能对藻类爆发性生长做出精确的预测。

Based on five-year monitoring data of Xikeng reservoir in Shenzhen City from 2000 to 2004, data of 11 environment factors (Total phosphorus, water depth, etc.) and 3 biotic factors (algae biomass, Chl-a and blue algae biomass) were employed, and the correlations between these factors were analyzed using multi-statistic method of stepwise based on SPSS. On the basis of the analysis, considering the factors which were significantly related to blue algae biomass, a prediction model of algae biomass was developed. The results showed that algae biomass was mainly affected by water temperature, water depth and total phosphorus concentration. However, the significant factors for different sites were different, which mean the limited factors of algae growth were different in different environment. Multi-statistic method could be helpful to identify key factors but could not supply good prediction results.

文章引用:
杨滢, 王艳, 黄文辉, 王炜铭. 深圳某水库水华藻类及相关环境因子逐步回归统计分析[J]. 水资源研究, 2016, 5(1): 33-39. http://dx.doi.org/10.12677/JWRR.2016.51004

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