Tavakol Mitra, Arjmandi Reza, Shayeghi Mansoureh, Monavari Seyed Masoud, Karbassi Abdolreza
Dept. of Environmental Sciences, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Dept. of Medical Entomology and Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Iran J Public Health. 2017 Jan;46(1):83-92.
One of the key issues in determining the quality of water in rivers is to create a water quality control network with a suitable performance. The measured qualitative variables at stations should be representative of all the changes in water quality in water systems. Since the increase in water quality monitoring stations increases annual monitoring costs, recognition of the stations with higher importance as well as main parameters can be effective in future decisions to improve the existing monitoring network.
Sampling was carried out on 12 physical and chemical parameters measured at 15 stations during 2013-2014 in Haraz River, northern Iran.
The results of the measurements were analyzed using multivariate statistical analysis methods including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA). According to the CA, PCA, and FA, the stations were divided into three groups of high pollution, medium pollution, and low pollution.
The research findings confirm applicability of multivariate statistical techniques in the interpretation of large data sets, water quality assessment, and source apportionment of different pollution sources.
确定河流水质的关键问题之一是建立一个具有适当性能的水质控制网络。各监测站测得的定性变量应能代表水系统中水质的所有变化。由于水质监测站数量的增加会提高年度监测成本，识别具有更高重要性的监测站以及主要参数，对于未来改进现有监测网络的决策可能会很有效。
2013年至2014年期间，在伊朗北部哈拉兹河的15个监测站对12项理化参数进行了采样。
使用多元统计分析方法，包括聚类分析（CA）、主成分分析（PCA）、因子分析（FA）和判别分析（DA），对测量结果进行了分析。根据聚类分析、主成分分析和因子分析，监测站被分为高污染、中污染和低污染三组。
研究结果证实了多元统计技术在解释大数据集、水质评估以及不同污染源的源分配方面的适用性。
