Improved Understanding of Dissolved Organic Matter Processing in Freshwater Using Complementary Experimental and Machine Learning Approaches

Dissolved
organic matter plays an important role in aquatic ecosystems
and poses a major problem for drinking water production. However,
our understanding of DOM reactivity in natural systems is hampered
by its complex molecular composition. Here, we used Fourier-transform
ion cyclotron resonance mass spectrometry (FT-ICR-MS) and data from
two independent studies to disentangle DOM reactivity based on photochemical
and microbial-induced transformations. Robust correlations of FT-ICR-MS
peak intensities with chlorophyll a and solar irradiation
were used to define 9 reactivity classes for 1277 common molecular
formulas. Germany’s largest drinking water reservoir was sampled
for 1 year, and DOM processing in stratified surface waters could
be attributed to photochemical transformations during summer months.
Microbial DOM alterations could be distinguished based on correlation
coefficients with chlorophyll a and often shared
molecular features (elemental ratios and mass) with photoreactive
compounds. In particular, many photoproducts and some microbial products
were identified as potential precursors of disinfection byproducts.
Molecular DOM features were used to further predict molecular reactivity
for the remaining compounds in the data set based on a random forest
model. Our method offers an expandable classification approach to
integrate the reactivity of DOM from specific environments and link
it to molecular properties and chemistry.