A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: EPiE

  • R. Oldenkamp
  • , S. Hoeks
  • , M. Čengić
  • , V. Barbarossa
  • , E.E. Burns
  • , A.B.A. Boxall
  • , A.M.J. Ragas

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Environmental risk assessment of pharmaceuticals requires the determination of their environmental exposure concentrations. Existing exposure modeling approaches are often computationally demanding, require extensive data collection and processing efforts, have a limited spatial resolution, and have undergone limited evaluation against monitoring data. Here, we present ePiE (exposure to Pharmaceuticals in the Environment), a spatially explicit model calculating concentrations of active pharmaceutical ingredients (APIs) in surface waters across Europe at ∼1 km resolution. ePiE strikes a balance between generating data on exposure at high spatial resolution while having limited computational and data requirements. Comparison of model predictions with measured concentrations of a diverse set of 35 APIs in the river Ouse (UK) and Rhine basins (North West Europe), showed around 95% were within an order of magnitude. Improved predictions were obtained for the river Ouse basin (95% within a factor of 6; 55% within a factor of 2), where reliable consumption data were available and the monitoring study design was coherent with the model outputs. Application of ePiE in a prioritisation exercise for the Ouse basin identified metformin, gabapentin, and acetaminophen as priority when based on predicted exposure concentrations. After incorporation of toxic potency, this changed to desvenlafaxine, loratadine, and hydrocodone.
Original languageEnglish
Pages (from-to)12494-12503
Number of pages10
JournalEnvironmental Science and Technology
Volume52
Issue number21
Early online date10 Oct 2018
DOIs
Publication statusPublished - 6 Nov 2018
Externally publishedYes

Funding

We thank John Wilkinson for his valuable contribution to the data collection and interpretation. This work was supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (iPiE Grant 115635).

FundersFunder number
European Commission
EU/EFPIA115635
Seventh Framework Programme115735

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