Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios

• Published in: Integrated environmental assessment and management Vol. 20; no. 5; pp. 1715 - 1735
• Main Authors: Welch, Samuel A., Grung, Merete, Madsen, Anders L., Jannicke Moe, S.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.09.2024
• Subjects: Bayesian analysis; Drugs; Environmental assessment; Environmental Impact Assessment; Environmental management; Environmental risk; Environmental risk assessment; Integrated environmental assessment; Mathematical models; Mitigation; Object‐oriented Bayesian networks; Pharmaceutical pollution; Pharmaceuticals; Population dynamics; Population growth; Probabilistic models; Probabilistic risk assessment; Probability theory; Prototypes; Risk assessment; Risk reduction; Rural environments; Statistical analysis; Toxicology; Wastewater treatment; Water treatment; Water treatment plants; Object‐oriented Bayesian networks; Probabilistic risk assessment; Environmental risk assessment; Wastewater treatment; Pharmaceutical pollution
• ISSN: 1551-3777; 1551-3793; 1551-3793
• DOI: 10.1002/ieam.4939

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object‐oriented Bayesian network (BN) for the prediction of environmental risk for six high‐priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;20:1715–1735. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points We constructed a prototype object‐oriented Bayesian network to predict the environmental risks of six key pharmaceuticals in Norwegian surface waters under 36 plausible future scenarios. Object‐oriented design paradigms allowed efficient modular construction of a pharmaceutical consumption‐to‐pollution probabilistic environmental risk assessment (ERA) network. Our initial predictions indicated that, of the pharmaceuticals considered, ethinylestradiol presented by far the highest risk quotients, followed by ibuprofen; risk was greatest in rural areas, without effective wastewater treatment, and under high population growth scenarios. Our prototype sets the stage for Bayesian network‐based ERA of pharmaceuticals; however, there are several technical limitations that still hold the approach back.