Kumaresan M, Riyazuddin P
Department of Analytical Chemistry, University of Madras, Guindy Campus, Chennai, 600 025, India.
Environ Monit Assess. 2008 Mar;138(1-3):65-79. doi: 10.1007/s10661-007-9761-8. Epub 2007 May 17.
An approach is described for viewing the interrelationship between different variables and also tracing the sources of pollution of groundwater of north Chennai (India). The data set of 43 variables which include major ions, minor ions and trace metal speciation (Cu, Pb, Cd and Zn) collected during the pre-monsoon and post-monsoon seasons of the year 2000-2001, was subjected to R-mode factor analysis to comprehend the distribution pattern of the said variables. It was found that first factor measures salinity and hardness which explained 19.12% of the total variance (comprised of variables EC, TDS, Na+, K+, Ca2+, Mg2+, total hardness, Cl- and SO4(2-)) during pre-monsoon, while it was 25.08% during post-monsoon. The second and third factors were attributed to speciation of zinc and copper ions during both pre-monsoon and post-monsoon. Although there were two more factors, loaded with speciation parameters of lead and cadmium, the variance of them were less than 10%. From this study it is seen that sea water intrusion, municipal solid waste disposal are the identified sources of component of pollution. The importance of metal ions is taking a secondary role and the anthropogenic origin-industrial activity, is the reason in the evaluation of pollution status as they come in the second, third, fourth and fifth factors. As the trace metal speciation was grouped in separate factors, linear regression model (LRM) with correlation analysis was applied to check its validity for prediction of speciation and to apply LRM for rapid monitoring of water pollution.
本文描述了一种用于观察不同变量之间的相互关系以及追踪印度钦奈北部地下水污染源的方法。对2000 - 2001年季风前和季风后季节收集的包括主要离子、微量离子和痕量金属形态（铜、铅、镉和锌）在内的43个变量的数据集进行了R型因子分析，以了解这些变量的分布模式。结果发现，第一个因子衡量盐度和硬度，在季风前占总方差（由电导率、总溶解固体、钠离子、钾离子、钙离子、镁离子、总硬度、氯离子和硫酸根离子组成）的19.12%，而在季风后为25.08%。第二和第三个因子归因于季风前和季风后锌和铜离子的形态。虽然还有另外两个因子，负载铅和镉的形态参数，但它们的方差小于10%。从这项研究可以看出，海水入侵、城市固体废弃物处理是已确定的污染源组成部分。金属离子的重要性处于次要地位，而人为来源——工业活动，是评估污染状况的原因，因为它们出现在第二、第三、第四和第五个因子中。由于痕量金属形态被分组在不同因子中，因此应用了带有相关分析的线性回归模型（LRM）来检验其对形态预测的有效性，并应用LRM对水污染进行快速监测。
