This work investigated the application of several fluorescence excitation–emission matrix analysis methods as natural organic matter（NOM） indicators for use in predicting the formation of trihalomethanes（THMs） an...This work investigated the application of several fluorescence excitation–emission matrix analysis methods as natural organic matter（NOM） indicators for use in predicting the formation of trihalomethanes（THMs） and haloacetic acids（HAAs）. Waters from four different sources（two rivers and two lakes） were subjected to jar testing followed by 24 hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures： dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking,and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component（PC） scores resulted in the lowest mean squared prediction error of cross-folded test sets（THMs： 43.7（μg/L）^2, HAAs： 233.3（μg/L）^2）. Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation–emission matrix–principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied.展开更多
基金funded in part by the Canadian Water Network and the Natural Sciences and Engineering Research Council of Canada Chair in Drinking Water Research at the University of Toronto
文摘This work investigated the application of several fluorescence excitation–emission matrix analysis methods as natural organic matter（NOM） indicators for use in predicting the formation of trihalomethanes（THMs） and haloacetic acids（HAAs）. Waters from four different sources（two rivers and two lakes） were subjected to jar testing followed by 24 hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures： dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking,and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component（PC） scores resulted in the lowest mean squared prediction error of cross-folded test sets（THMs： 43.7（μg/L）^2, HAAs： 233.3（μg/L）^2）. Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation–emission matrix–principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied.
