Hospital-Use Pharmaceuticals in Swiss Waters Modeled at High Spatial Resolution

• Published in: Environmental science & technology Vol. 50; no. 9; pp. 4742 - 4751
• Main Authors: Kuroda, Keisuke, Itten, René, Kovalova, Lubomira, Ort, Christoph, Weissbrodt, David G, McArdell, Christa S
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 03.05.2016
• Subjects: Effluents; Environmental Monitoring; Environmental science; Hospitals; Magnetic resonance imaging; Pharmaceutical Preparations; Pharmaceuticals; Rivers; Sewage; Sewage treatment plants; Tomography; Water; Water Pollutants, Chemical; Switzerland
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.6b00653

A model to predict the mass flows and concentrations of pharmaceuticals predominantly used in hospitals across a large number of sewage treatment plant (STP) effluents and river waters was developed at high spatial resolution. It comprised 427 geo-referenced hospitals and 742 STPs serving 98% of the general population in Switzerland. In the modeled base scenario, domestic, pharmaceutical use was geographically distributed according to the population size served by the respective STPs. Distinct hospital scenarios were set up to evaluate how the predicted results were modified when pharmaceutical use in hospitals was allocated differently; for example, in proportion to number of beds or number of treatments in hospitals. The hospital scenarios predicted the mass flows and concentrations up to 3.9 times greater than in the domestic scenario for iodinated X-ray contrast media (ICM) used in computed tomography (CT), and up to 6.7 times greater for gadolinium, a contrast medium used in magnetic resonance imaging (MRI). Field measurements showed that ICM and gadolinium were predicted best by the scenarios using number of beds or treatments in hospitals with the specific facilities (i.e., CT and/or MRI). Pharmaceuticals used both in hospitals and by the general population (e.g., cyclophosphamide, sulfamethoxazole, carbamazepine, diclofenac) were predicted best by the scenario using the number of beds in all hospitals, but the deviation from the domestic scenario values was only small. Our study demonstrated that the bed number-based hospital scenarios were effective in predicting the geographical distribution of a diverse range of pharmaceuticals in STP effluents and rivers, while the domestic scenario was similarly effective on the scale of large river-catchments.