The Impact of Dissolved Organic Matter Composition on the Formation of Disinfection Byproducts in Groundwater

Naturally occurring dissolved organic matter (DOM) poses a risk to human health when it reacts with disinfectants (e.g., chlorine) to form harmful disinfection by-products (DBPs) during drinking water treatment. Past research has shown that the DBP formation potential of DOM in surface waters (e.g., lakes and rivers) is inherently linked to its molecular composition. In contrast, little is known about the composition of DOM in groundwater or about its reactivity with disinfectants, despite the importance of groundwater as a major source of drinking water. Furthermore, there is no reliable method to predict DBP formation based on the composition of groundwater DOM. The proposed project will close an important knowledge gap by relating the composition of DOM in groundwater to its potential to form DBPs. The research will use water samples collected from 20 drinking water utilities in Wisconsin (16 groundwater; 4 surface water). We will characterize the composition of DOM before and after reaction with chlorine using bulk measurements (e.g., dissolved organic carbon and UV-visible absorption spectra) and high-resolution mass spectrometry (i.e., Fourier transform-ion cyclotron mass spectrometry). The mass spectrometry technique enables us to identify thousands of unique molecular formulas in each sample, investigate which molecules are most reactive with chlorine, and identify novel chlorinated- and brominated-DBPs. We will apply hierarchical cluster analysis and principal component analysis to relate DOM composition with the formation of known DBPs (i.e., trihalomethanes, haloacetic acids, and haloacetonitriles) at the molecular level. This fundamental understanding of groundwater DOM reactivity will be used to extend existing multivariate regression models for DBP prediction by incorporating simple bulk metrics that represent DOM composition in groundwater. The unique data set generated by the proposed research will be used to validate the most successful models so that they can be applied by water managers throughout the state of Wisconsin.