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Water Quality Assessment of the Huaihe River Segment of Bengbu (China) Using Multivariate Statistical Techniques

机译：多元统计技术在蚌埠市淮河段水质评价中的应用

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In the study, multivariate statistical methods including principal component analysis (PCA)/factor analysis (FA) and cluster analysis (CA) were applied to analyze surface water quality data sets obtained from the Huaihe River segment of Bengbu (HRSB) and generated during 2 years (2011-2012) monitoring of 19 parameters at 7 sampling sites. The results of PCA for 7 sampling sites revealed that the first four components of PCA showed 94.89% of the total variance in the data sets of HRSB. The Principal components (Factors) obtained from FA indicated that the parameters for water quality variations were mainly related to heavy metals (Pb, Mn, Zn and Fe) and organic related parameters (COD, PI and DO). The results revealed that the major causes of water quality deterioration were related to inflow of industrial, domestic and agricultural effluents into the Huaihe River. Three significant sampling locations-(sites 2, 3 and 4), (sites 1 and 5) and (sites 6 and 7)-were detected on the basis of similarity of their water quality. Thus, these methods were believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.
机译：在这项研究中，应用了包括主成分分析（PCA）/因子分析（FA）和聚类分析（CA）在内的多元统计方法来分析从蚌埠淮河段（HRSB）获得并在2年期间产生的地表水质量数据集。年（2011-2012年）对7个采样点的19个参数进行监控。对7个采样点的PCA结果显示，PCA的前四个成分在HRSB数据集中显示了94.89％的总方差。从FA获得的主成分（因子）表明，水质变化的参数主要与重金属（Pb，Mn，Zn和Fe）和有机相关参数（COD，PI和DO）有关。结果表明，水质恶化的主要原因与工业，家庭和农业废水流入淮河有关。根据水质的相似性，检测到三个重要的采样地点（地点2、3和4），（地点1和5）以及（地点6和7）。因此，这些方法被认为对帮助水资源管理者了解水质问题的复杂性质并确定改善水质的优先次序很有价值。

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