The
goal of this research was to identify functional groups that
determine rates of micropollutant (MP) biotransformations performed
by wastewater microbial communities. To meet this goal, we performed
a series of incubation experiments seeded with four independent wastewater
microbial communities and spiked them with a mixture of 40 structurally
diverse MPs. We collected samples over time and used high-resolution
mass spectrometry to estimate biotransformation rate constants for
each MP in each experiment and to propose structures of 46 biotransformation
products. We then developed random forest models to classify the biotransformation
rate constants based on the presence of specific functional groups
or observed biotransformations. We extracted classification importance
metrics from each random forest model and compared them across wastewater
microbial communities. Our analysis revealed 30 functional groups
that we define as either biotransformation promoters, biotransformation
inhibitors, structural features that can be biotransformed based on
uncharacterized features of the wastewater microbial community, or
structural features that are not rate-determining. Our experimental
data and analysis provide novel insights into MP biotransformations
that can be used to more accurately predict MP biotransformations
or to inform the design of new chemical products that may be more
readily biodegradable during wastewater treatment.