Machine Learning Modeling for the Reactivity of Organic Contaminants in Engineered and Natural Environments

With support from the Environmental Chemical Sciences Program of the NSF Division of Chemistry, the researchers will develop machine learning models to predict the reactivity of thousands of organic contaminants (OCs) in engineered (water) and natural (soil and sediment) environments. To assess and mitigate risks associated with this vast number of OCs, accurate predictive models are needed to readily provide reasonable estimates of their reactivity, both during important water treatment processes and in the environment. However, existing models rely heavily on conventional statistical methods. They have multiple limitations such as small numbers and narrow scopes of OCs involved and lengthy calculations of molecular properties. 

The project will employ advanced machine learning algorithms to predict contaminant reactivities. The obtained machine learning models will help identify OCs of concern and optimize the treatment processes. In addition, environmental data science will be developed as a new educational track at the pilot scale. Graduate, undergraduate and high school students with diverse backgrounds will be engaged in interdisciplinary research, including modeling and experimental work. The project also plans hands-on activities on OCs for girls in grade 6-12 and underrepresented college students.

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