Oral Presentation Society for Freshwater Science 2025 Annual Meeting

Using Bayesian causal inference to assess the remediation effectiveness of dredging on an urban river-lacustrine ecosystem. (117327)

Michael B. Griffith 1 , Marc A. Mills 1 , James M. Lazorchak 1 , Charles Carpenter 2 , Danielle Buhr-Strachan 2 , Roger B Yeardley 1
  1. Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, U.S.A.
  2. Neptune and Company, Lakewood, Colorado, U.S.A.

The lower 15-kilometers of the Ottawa River, a tributary to Lake Erie’s North Maumee Bay, flows through an industrialized part of the Toledo, Ohio, metropolitan area, and its sediments have been contaminated particularly with polychlorobiphenyls and polyaromatic hydrocarbons. As such, this river segment is included in the Maumee River Area of Concern under the Great Lakes Legacy Act. Before dredging in 2010 and most recently in 2020, sampling has been conducted to evaluate the effectiveness of the Ottawa River remediation efforts. The efficacy of dredging was measured through contaminant concentrations in different media and  biotic assemblages, such as macroinvertebrates. Using these data, we are developing a Bayesian causal analysis as an approach to infer the causal relationships between dredging; contaminant concentrations in sediments, water, artificial polyethylene devices, and macroinvertebrate tissues; sediment particle size; and macroinvertebrate metrics, focusing on the Ohio EPA’s Lacustuary Invertebrate Community Index. Analyses have produced acyclic directed graphs that estimate the impact of dredging on these variables. Further analyses may add experimental results from the literature to improve model estimates from the directed acyclic graphs.