Water Technology Centre for Eastern Region, Chandrasekharpur, Bhubaneswar, 751 023, Orissa, India.
Environ Monit Assess. 2010 Jul;166(1-4):149-57. doi: 10.1007/s10661-009-0991-9. Epub 2009 May 28.
Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.
地下水是城市地区饮用水的主要来源。由于城市化和发展对水质恶化的威胁日益增加，监测水质是确保其适合饮用的前提条件。但是，在常规间隔内对大量属性和参数到参数基础的水质评估是不可行的。多元技术可以在不丢失大量信息的情况下，将数据简化为一个合理的可管理数据集。在这项研究中，使用主成分分析，将 58 个水样的 11 个相关属性分为三个统计因子。判别分析确定“pH 值影响”为最显著的因子，pH 值、Fe 和 NO₃⁻为最具区分性的变量，可以作为水质指标。这些指标用于将采样点分为同质聚类，反映特定指标在整个研究区域内位置重要性，以用于监测饮用水水质。
