Singh Pardeep, Chaturvedi R K, Mishra Ankit, Kumari Lata, Singh Rishikesh, Pal D B, Giri Deen Dayal, Singh Nand Lal, Tiwary Dhanesh, Mishra Pradeep Kumar
Department of Chemistry, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India.
Environ Monit Assess. 2015 Apr;187(4):170. doi: 10.1007/s10661-015-4382-0. Epub 2015 Mar 9.
Multivariate statistical techniques were employed for monitoring of ground-surface water interactions in rivers. The river Varuna is situated in the Indo-Gangetic plain and is a small tributary of river Ganga. The study area was monitored at seven sampling sites for 3 years (2010-12), and eight physio-chemical parameters were taken into account for this study. The data obtained were analysed by multivariate statistical techniques so as to reveal the underlying implicit information regarding proposed interactions for the relevant area. The principal component analysis (PCA) and cluster analysis (CA), and the results of correlations were also studied for all parameters monitored at every site. Methods used in this study are essentially multivariate statistical in nature and facilitate the interpretation of data so as to extract meaningful information from the datasets. The PCA technique was able to compress the data from eight to three parameters and captured about 78.5% of the total variance by performing varimax rotation over the principal components. The varifactors, as yielded from PCA, were treated by CA which grouped them convincingly into three groups having similar characteristics and source of contamination. Moreover, the loading of variables on significant PCs showed correlations between various ground water and surface water (GW-SW) parameters. The correlation coefficients calculated for various physiochemical parameters for ground and surface water established the correlations between them. Thus, this study presents the utility of multivariate statistical techniques for evaluation of the proposed interactions and effective future monitoring of potential sites.
多元统计技术被用于监测河流中的地表水相互作用。瓦鲁纳河位于印度 - 恒河平原，是恒河的一条小支流。研究区域在7个采样点进行了为期3年（2010 - 2012年）的监测，本研究考虑了8个理化参数。对获得的数据采用多元统计技术进行分析，以揭示有关该相关区域拟议相互作用的潜在隐含信息。还对每个站点监测的所有参数进行了主成分分析（PCA）、聚类分析（CA）以及相关性研究。本研究中使用的方法本质上是多元统计方法，有助于对数据进行解释，以便从数据集中提取有意义的信息。PCA技术能够将数据从8个参数压缩到3个参数，并通过对主成分进行方差最大化旋转捕获了约78.5%的总方差。由PCA得出的变量因子通过CA进行处理，CA将它们令人信服地分为具有相似特征和污染源的三组。此外，显著主成分上变量的载荷显示了各种地下水和地表水（GW - SW）参数之间的相关性。为地下水和地表水的各种理化参数计算的相关系数确定了它们之间的相关性。因此，本研究展示了多元统计技术在评估拟议相互作用以及对潜在地点进行有效未来监测方面的实用性。
