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首页> 外文期刊>Environmental Research Journal >Application of Multivariate Statistical Methods to Assessment of Water Quality in Selected Locations of the Lagos Lagoon, Nigeria
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Application of Multivariate Statistical Methods to Assessment of Water Quality in Selected Locations of the Lagos Lagoon, Nigeria

机译:多元统计方法在尼日利亚拉各斯泻湖部分地区水质评估中的应用

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摘要

Multivariate statistical methods, i.e., Cluster Analysis (CA), Principal Component Analysis (PCA) and Discriminant Analysis (DA) were used to assess temporal and spatial variations in the water quality of the Lagos Lagoon during the wet period (July 2007 and 2008) anddry seasons (February 2008 and 2009). The study was focused on nine locations of the lagoon, specifically to describe the distribution of water physicochemical parameters and identify the parameter (s) that most influence the distributions observed. Physicochemical parameters (pH, EC, salinity, turbidity, DO, BOD5, COD, TSS, TDS, alkalinity, NO3, PO4 and SO4) were used to study spatial and temporal variations in water quality of these locations. The descriptive statistics of the average values obtained for each location during the period of study were discussed. The results obtained from the detailed chemical analysis of water from the different sections of the lagoon confirmed the dynamic nature and diverse chemistry of the water. Multivariate analysis of obtained data during the periods of study further reflects this diversity during each of the periods samples were collected. The loading pattern of principal components showed some variations during each of the period of sample collection. The processes or sources associated with the principal components obtained during the different sampling periods are highly localized and contributed mainly by anthropogenic sources. Hierarchical CA grouped the nine locations into three based on the water characteristics during each period of sample collection. Hierarchical CA and PCA did not give a clear trend in temporal distribution of the parameters. As a result it was difficult to determine a constant similarity between locations during these periods however, DA showed EC and TDS were the only good predictors or discriminant variables in all the locations during the period of investigation.
机译:多变量统计方法,即聚类分析(CA),主成分分析(PCA)和判别分析(DA)用于评估湿润时期(2007年7月和2008年)拉各斯泻湖水质的时空变化干季(2008年2月和2009年2月)。该研究集中在泻湖的九个位置,专门描述水的理化参数分布并确定对观察到的分布影响最大的参数。使用理化参数(pH,EC,盐度,浊度,DO,BOD5,COD,TSS,TDS,碱度,NO3,PO4和SO4)研究这些位置水质的时空变化。讨论了研究期间每个位置获得的平均值的描述性统计。从泻湖不同区域对水进行详细的化学分析得到的结果证实了水的动态性质和多种化学性质。在研究期间对获得的数据进行多变量分析,进一步反映了在每个时期收集样本的这种多样性。在每个样本采集期间,主成分的加载模式均显示出一些变化。在不同采样期间获得的与主要成分相关的过程或来源高度局限,主要由人为来源引起。分层CA根据每个样本收集期间的水质将九个位置分为三个位置。分层CA和PCA在参数的时间分布方面没有给出明确的趋势。结果,很难确定这些时期内各地点之间的恒定相似性,但是,DA显示,在调查期间,EC和TDS是所有地点中唯一的良好预测指标或判别变量。

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