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Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?
Author(s) -
Burns Emily E.,
ThomasOates Jane,
Kolpin Dana W.,
Furlong Edward T.,
Boxall Alistair B.A
Publication year - 2017
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.3842
Subject(s) - prioritization , environmental chemistry , environmental science , biochemical engineering , chemistry , management science , engineering
Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk‐based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs. Environ Toxicol Chem 2017;36:2823–2832. © 2017 SETAC