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Stephanie E. Hummel

Publications and source records attributed to Stephanie E. Hummel.

2 recordsLinked to original sources

Prioritizing chemicals of emerging concern in the Great Lakes Basin using covariance of chemical concentrations and diverse biological responses from a variety of species

The Great Lakes Restoration Initiative aims to protect and restore the nation’s largest freshwater resource, in part, by furthering our understanding of the effects of contaminants of emerging concern (CECs) and chemical mixtures on aquatic and terrestrial organisms. To address this goal, an interagency team conducted field studies at sites along the Maumee River in Ohio, USA, in 2016–2017, monitoring CEC levels along with diverse in vitro and in vivo biological effects in ecologically relevant species (fathead minnows, tree swallows, and golden clams). The objective of the present work was to prioritize the CECs in these studies for further monitoring and assessment by determining if there are patterns in chemical–bioeffect relations across data sets, species, and response types that indicate relatively high or low hazard to aquatic life from CEC exposure. Of the 748 monitored chemicals, 425 were detected and were analyzed for covariance with bioeffects. All 748 chemicals were placed into 10 bins based on their frequencies of monitoring, detection, and covariance with bioeffects across studies and species. We describe how chemicals can be prioritized across bins to aid monitoring and assessment efforts. Our approach using effects-based monitoring data is especially useful for prioritizing chemicals with little or no traditional toxicity testing data. Similar evidence-based prioritizations will allow agencies to more efficiently allocate limited resources to improve the ability to protect aquatic and terrestrial organisms from adverse impacts due to contaminant exposure.

Ohio

Predicting the occurrence of chemicals of emerging concern in surface water and sediment across the U.S. portion of the Great Lakes Basin

Chemicals of emerging concern (CECs) are introduced into the aquatic environment via various sources, posing a potential risk to aquatic organisms. Previous studies have identified relationships between the presence of CECs in water and broad-scale watershed characteristics. However, relationships between the presence of CECs and source-related watershed characteristics have not been explored across the Great Lakes basin. Boosted regression tree (BRT) analyses were used to develop predictive models of CEC occurrence in water and sediment throughout 24 U.S. tributaries to the Great Lakes. Models were based on the distribution of both broad-scale and source-related watershed characteristics. Twenty-one upstream watershed characteristics, including land cover, number of permitted point sources, and distance to point sources were used to develop models predicting the probability of CEC occurrence in surface water and bottom sediment. Total accuracy of BRT models ranged from 66% to 94% for both matrices. All 21 watershed characteristics were important predictor variables in at least one surface-water model; twenty were important in at least one bottom-sediment model. Among the model variables, developed land use and distance to point sources were important predictors of the presence of CEC classes in both water and sediment. Although limitations exist, BRT models are one tool available for assessing vulnerability of fisheries and aquatic resources to CEC occurrences.

Great Lakes basin