Abstract：
In this study, source water, finished water, and tap water were sampled monthly from two large drinking water treatment plants in Wuhan city, China for 12 months where physicochemical and microbiological parameters were measured, and the complex monitoring data was analyzed using single-factor assessment method, entropy weight water quality index (EWQI), and multivariate statistical techniques (i.e., cluster analysis (CA), discriminant analysis, and correlation analysis). The results of the single-factor assessment method showed that the total nitrogen pollution was the main problem in the source water quality, and the finished and tap water met the required quality standards. The EWQI values indicated that the overall quality of the source, finished, and tap water samples was \"Excellent.\" In addition, strengthening monitoring of parameters with high entropy weights, including Pb, Hg, sulfide, Cr in surface water and Hg, aerobic bateria count, and As in drinking water, were suggested, as they were prone to drastic changes. Spatial CA grouped the finished and tap water samples from the same plant into a cluster. Temporal CA grouped 12 sampling times of source water into Cluster 1 (June), Cluster 2 (April-May, and July-November), and Cluster 3 (December-March). Concerning finished and tap water, except the October was regrouped, the result of temporal CA was consistent to that of the source water. Based on similar characteristics of water samples, monitoring sites and frequency can be optimized. Moreover, stepwise discriminant analysis indicated that the spatiotemporal variations in water quality among CA-groups were enough to be explained by four or five parameters, which provided a basis for the selection of monitoring parameters. The results of correlation analysis showed that few pairwise correlations were both significant (P < 0.05) and stable across sampling sites, suggesting that the number of monitoring parameters was difficult to reduce through substitution. In summary, this study illustrates the usefulness of EWQI and the multivariate statistical techniques in the water quality assessment and monitoring strategy optimization.
摘要：
在这项研究中,水源,完成水,每月从武汉市的两个大型饮用水处理厂采样自来水，在中国进行了12个月的物理化学和微生物参数测量，采用单因素评价法对复杂监测数据进行分析,熵权水质指数(EWQI)，和多元统计技术(即，聚类分析(CA)，判别分析,和相关分析)。单因素评价法的结果表明,总氮污染是水源水质的主要问题,成品水和自来水符合规定的质量标准。EWQI值表明源的整体质量，已完成,和自来水样本是\"非常好。\"此外,加强对高熵权参数的监测，包括Pb,Hg,硫化物,地表水中的Cr和Hg，有氧蝙蝠计数，和饮用水一样，被建议，因为它们容易发生剧烈的变化。空间CA将来自同一植物的成品和自来水样品分组为一组。时序CA将12次水源水采样分为第1组(6月)，集群2（4月至5月，和7月至11月)，和第3组（12月至3月）。关于成品水和自来水，除了十月重新集结，时间CA的结果与源水的结果一致。根据水样的相似特征，监控站点和频率可以优化。此外，逐步判别分析表明，CA组之间水质的时空变化足以由四个或五个参数来解释。为监测参数的选择提供了依据。相关性分析结果表明，不同采样点的配对相关性较少（P<0.05）且稳定，这表明监测参数的数量很难通过替代来减少。总之,本研讨说明了EWQI和多元统计技巧在水质评价和监测战略优化中的有用性。
