The very large number of emerging contaminants entering the water supply makes it desirable to assess environmental fate and behavior without direct measurements. Quantitative molecular similarity assessment (QMSA) is one evolving predictive technique. This research has three main objectives related to QMSA modeling of emerging contaminants: 1) demonstrating that QMSA models can accurately predict environmental parameters of interest for highly diverse chemical classes; 2) assessing the extent to which QMSA can help prioritize among unmeasured chemicals and determine which additional measurements will result in maximally increased model accuracy; and 3) measuring fundamental fate and transport parameters for several key emerging contaminants. Preliminary results will be presented for three types of environmental fate parameters: in vitro estrogenicity, sorption distribution coefficient (Kd), and first-order degradation rate constants (k1) for simulated activated sludge treatment. Future work will focus on the tradeoffs between structural representativeness versus redundancy during prioritization among unmeasured chemicals.

Quantitative molecular similarity analysis for predicting environmental properties and prioritizing emerging contaminants research

243rd National Meeting (2012)

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