Pollution Trees: Identifying Similarities among Complex Pollutant Mixtures in Water and Correlating Them to Mutagenicity

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• 作者：Weiwei Zheng ; Xia Wang ; Dajun Tian ; Hao Zhang ; Weidong Tian ; Melvin E. Andersen ; Yuxin Zheng ; Xin Sun ; Songhui Jiang ; Zhaojin Cao ; Gengsheng He ; Weidong Qu
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2012
• 出版时间：July 3, 2012
• 年：2012
• 卷：46
• 期：13
• 页码：7274-7282
• 全文大小：339K
• 年卷期：v.46,no.13(July 3, 2012)
• ISSN：1520-5851

文摘

There are relatively few tools available for computing and visualizing similarities among complex mixtures and in correlating the chemical composition clusters with toxicological clusters of mixtures. Using the 鈥渋ntersection and union ratio (IUR)鈥?and other traditional distance matrices on contaminant profiles of 33 specific water samples, we used 鈥減ollution trees鈥?to compare these mixtures. The 鈥減ollution trees鈥?constructed by neighbor-joining (NJ), maximum parsimony (MP), and maximum likelihood (ML) methods allowed comparison of similarities among these samples. The mutagenicity of each sample was then mapped to the 鈥減ollution tree鈥? The IUR-distance-based measure proved effective in comparing chemical composition and compound level differences between mixtures. We found a robust 鈥減ollution tree鈥?containing seven major lineages with certain broad characteristics: treated municipal water samples were different from raw water samples and untreated rural drinking water samples were similar with local water sources. The IUR-distance-based tree was more highly correlated to mutagenicity than were other distance matrices, i.e., MP/ML methods, sampling group, region, or water type. IUR-distance-based 鈥減ollution trees鈥?may become important tools for identifying similarities among real mixtures and examining chemical composition clusters in a toxicological context.