Pollution trees: identifying similarities among complex pollutant mixtures in water and correlating them to...

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 "intersection and union ratio (IUR)" and other traditional distance matrices on contaminant profiles of 33 specific water samples, we used "pollution trees" to compare these mixtures. The "pollution trees" 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 "pollution tree". The IUR-distance-based measure proved effective in comparing chemical composition and compound level differences between mixtures. We found a robust "pollution tree" 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 "pollution trees" may become important tools for identifying similarities among real mixtures and examining chemical composition clusters in a toxicological context.