Improved prediction of pharmaceutical concentrations in wastewater using numerical correction factors applied to prescribing information

Abstract

Pharmaceuticals and metabolites excreted by humans are a threat to aquatic ecosystems globally. Gaps exist in environmental data sets which can be filled by predicting concentrations using prescribing data available at suitable spatial (e.g., wastewater treatment plant [WWTP] catchment area) and temporal scale (e.g., monthly). The aim was to improve the accuracy of predicted pharmaceutical concentrations in untreated influent wastewater. A conventional prediction approach of applying human excretion information to prescribing data found three of 12 analytes (metformin, desmethylvenlafaxine and clarithromycin) had acceptable predictions (within ±50% of their measured concentration) at three WWTPs of varying size. Several analytes had systematic underestimated predictions across WWTPs related to low analyte excretion rates. Laboratory-scale gravity sewer experiments revealed an important contributor was glucuronide metabolite deconjugation back to their parent pharmaceutical which could not be accounted for using excretion information. Therefore, numerical correction factors were derived (0.027–0.86) from prescribing and wastewater data to establish the fraction of prescribed pharmaceuticals found in wastewater. These account for changes between prescription of a pharmaceutical and its presence in wastewater (e.g., human metabolism and in-sewer transformation) without the need to quantify and correct for them individually. This enabled acceptable predictions for another six analytes (carbamazepine, propranolol, venlafaxine, fluoxetine, norfluoxetine and desmethylclarithromycin). Therefore, incorporating this approach in prediction models for treated effluents and river water can improve their accuracy for improved risk assessment. This is key to identify where subsequent technological or ‘upstream’ intervention is needed to target pharmaceutical environmental impacts.