CAREER: Mechanistic Investigation of Chemical Photodegradation to Aid in Novel Pesticide Design

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This CAREER award from the Environmental Chemical Sciences Program in the Division of Chemistry supports Professor Jakub Kostal and his students and postdoctoral associates for a computational study of the environmental fate of pesticides. Crop protection is ever more important as global population grows. It is estimated that over two million tons of active pesticide ingredients are used annually. The environmental fate of these chemicals and their ability to degrade after use are not well understood. Environmental concerns require changes to the use of these chemicals in the environment. This research aims to advance our understanding of light-initiated breakdown mechanisms of pesticides in the air and in surface waters using computational techniques. This research will inform the design of alternatives to existing commercial pesticides, which will not persist in the environment. The students and postdocs participating in this research will be trained in both environmental and computational chemistry. Professor Kostal also leads an annual design challenge for chemistry students hosted by the American Chemical Society Green Chemistry & Engineering Conferences. In this challenge, teams of chemistry students will apply computational methods to the design of novel commercial chemicals that are both high performing and non-persistent.Photochemical reactions are the primary route for abiotic degradation of pesticides. Among those, indirect reactions initiated by excited natural organic matter are the most common. This work aims to improve our understanding of the reaction mechanisms between common pesticides and natural organic matter. The goal is to develop guidelines for the effective use of existing pesticides and to design novel pesticides that will not persist in the environment after use. Computational methods, notably time-independent and dependent density functional theory, are used. The project develops a comprehensive database of photodegradation metrics, which are made available to the scientific public. These metrics can, in turn, be used to better understand structure-activity relationships for abiotic transformations of pesticides by applying machine learning and pattern-recognition methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.