Nazeer Summya, Ali Zeshan, Malik Riffat Naseem
Environmental Biology Laboratory, Department of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
National Institute of Bioremediation, National Agricultural Research Center (NARC), Park Road, Islamabad, 45500, Pakistan.
Arch Environ Contam Toxicol. 2016 Jul;71(1):97-112. doi: 10.1007/s00244-016-0272-x. Epub 2016 Mar 21.
The present study was designed to determine the spatiotemporal patterns in water quality of River Soan using multivariate statistics. A total of 26 sites were surveyed along River Soan and its associated tributaries during pre- and post-monsoon seasons in 2008. Hierarchical agglomerative cluster analysis (HACA) classified sampling sites into three groups according to their degree of pollution, which ranged from least to high degradation of water quality. Discriminant function analysis (DFA) revealed that alkalinity, orthophosphates, nitrates, ammonia, salinity, and Cd were variables that significantly discriminate among three groups identified by HACA. Temporal trends as identified through DFA revealed that COD, DO, pH, Cu, Cd, and Cr could be attributed for major seasonal variations in water quality. PCA/FA identified six factors as potential sources of pollution of River Soan. Absolute principal component scores using multiple regression method (APCS-MLR) further explained the percent contribution from each source. Heavy metals were largely added through industrial activities (28 %) and sewage waste (28 %), nutrients through agriculture runoff (35 %) and sewage waste (28 %), organic pollution through sewage waste (27 %) and urban runoff (17 %) and macroelements through urban runoff (39 %), and mineralization and sewage waste (30 %). The present study showed that anthropogenic activities are the major source of variations in River Soan. In order to address the water quality issues, implementation of effective waste management measures are needed.
本研究旨在运用多元统计方法确定索安河水质量的时空模式。2008年在季风季节前后，对索安河及其相关支流沿线共26个地点进行了调查。层次聚类分析（HACA）根据污染程度将采样点分为三组，水质退化程度从低到高。判别函数分析（DFA）表明，碱度、正磷酸盐、硝酸盐、氨、盐度和镉是能够显著区分HACA所确定的三组的变量。通过DFA确定的时间趋势表明，化学需氧量、溶解氧、pH值、铜、镉和铬可归因于水质的主要季节性变化。主成分分析/因子分析（PCA/FA）确定了六个因素作为索安河潜在的污染源。使用多元回归方法的绝对主成分得分（APCS-MLR）进一步解释了每个来源的贡献率。重金属主要通过工业活动（28%）和污水排放（28%）进入，营养物质通过农业径流（35%）和污水排放（28%）进入，有机污染通过污水排放（27%）和城市径流（17%）进入，大量元素通过城市径流（39%）以及矿化和污水排放（30%）进入。本研究表明，人为活动是索安河水质变化的主要来源。为了解决水质问题，需要实施有效的废物管理措施。
