Abstract

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.

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