Institute: District of Columbia
Year Established: 2020 Start Date: 2020-03-01 End Date: 2021-02-28
Total Federal Funds: $10,000 Total Non-Federal Funds: $20,000
Principal Investigators: Scott Glaberman
Abstract: The release of organic chemicals from industrial, urban, and agricultural sources are continually increasing. Yet, the ecological and human health effects of many of these chemicals remain unknown. This situation is made more challenging because many chemicals occur in complex mixtures, with potentially interactive effects. Environmental monitoring of aquatic systems has traditionally focused on quantifying chemical occurrence. However, hazard and/or risk assessment cannot be conducted based on contaminant concentrations alone. There is now an emerging trend to couple chemical monitoring programs with effects-based monitoring, which can include computational models, in vitro cell-based assays, and in vivo biomarker measurements from captured animals. Effects-based monitoring is powerful because it can integrate effects of chemicals found in complex mixtures and can yield insights into key biological processes (e.g., endocrine disruption, genotoxicity) that are important for both ecological and human health. Moreover, recent advances in technology make effects-based approaches technologically and economically feasible, and have been used successfully in a number of aquatic systems worldwide including by EPA and USGS in the Great Lakes. However, such approaches have not yet been applied to water quality monitoring in the Metropolitan Washington Area or the Chesapeake Bay.We propose to pioneer cutting-edge methodologies in effects-based monitoring in the DC area by bridging two long-term monitoring programs of organic chemicals from George Mason University in the Potomac River and American University in the Anacostia River. We will transform these monitoring datasets into computational predictions that specify 1) which types of biological effects are expected in human and aquatic life receptors, 2) whether exposures exceed risk benchmarks, and 3) which geographic locations are of greatest concern. These results will help water quality regulators and treatment plants identify and prioritize problematic contaminants. This project will also help unite water science programs from two DC area universities that can set the stage for a larger regional effects-based monitoring program. In this proposal, we focus on computational rather than experimental approaches since the former requires fewer resources and is ideal for the scope of a seed grant, since it takes greatest advantage of existing data, and is optimal for student learning.