Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream

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Abstract
Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions (i.e., including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems (i.e., algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs (i.e., RQ ≥ 0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k < 0.1 h−1) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.
Funding Information
  • National Science Foundation (CBET Environmental Engineering 1803197)
  • U.S. Geological Survey (2017 104(g))
  • U.S. Geological Survey (G17AP00135)
  • U.S. Geological Survey (2017IA502G)
  • Iowa Water Center (15802100 (BR01))
  • University of Iowa (15802100 (BR01))
  • Center for Biocatalysis and Bioprocessing, University of Iowa (2 T32 GM008365)
  • National Institutes of Health (2 T32 GM008365)
  • Alfred P. Sloan Foundation

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