PERK: An R/Shiny application to predict and visualise concentrations of pharmaceuticals in the aqueous environment
Kishore Jagadeesan, Ruth Barden, Barbara Kasprzyk‐Hordern
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.