PERK: An R/Shiny application to predict and visualise concentrations of pharmaceuticals in the aqueous environment

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Abstract

Predicting the concentration of active pharmaceuticals ingredients (API) in the environment using modelling approaches is an important aspect in the assessment of their environmental risk, especially for the API with no or limited analytical detection methods. However, handling, validating, and incorporating diverse datasets, including API prescription/consumption data, metabolism, flow data, removal efficiency during wastewater treatment, and dilution factor for the modelling is often laborious and time-consuming. The aim of this manuscript is to evaluate R/Shiny based tool, PERK, to facilitate automated modelling and reporting predicted environmental concentration (PEC) of a comprehensive set of API in different environmental matrices. PERK helped to calculate PEC in wastewater influent, effluent, and river, and compare with measured environmental concentrations (MEC) for five catchments located in England. Prediction accuracy (PA), the ratio between PEC and MEC, can be also generated with the tool. PERK provides consistent interactive user-interface, enabling user to visualise the results with limited programming knowledge.

Original languageEnglish
Article number162352
JournalScience of the Total Environment
Volume875
Early online date21 Feb 2023
DOIs
Publication statusPublished - 1 Jun 2023

Bibliographical note

Funding Information:
This work was supported by the Wastewater Fingerprinting for Public Health Assessment (ENTRUST) project funded by Wessex Water Services Ltd and EPSRC IAA (Grant No. EP/R51164X/1 , ENTRUST IAA) and IPC Project . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Open Access funding enabled and organized by University of Bath.

Data availability
Data will be made available on request.

Keywords

  • Chemical mining
  • Decision support system
  • Environmental monitoring
  • Influent modelling
  • Model-based evaluation
  • Risk assessment tool
  • Water fingerprinting

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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