Per- and polyfluoroalkyl substances (PFAS) and industrial persistent, mobile, and toxic (iPMT) compounds pose growing risks to water quality. However, quantitative tools to prioritize compounds for remediation and evaluate exposure under varying environmental scenarios remain limited. This paper presents the first application of a Bayesian probabilistic early warning exposure assessment (EWEX) model to evaluate PFAS and iPMTs in indirect potable water reuse (IPR) systems. By integrating literature-derived and site-specific data, the model explicitly accounts for uncertainty and provides a flexible framework for scenario analysis. The model’s performance was demonstrated for PFOS, trifluoromethanesulfonic acid (TFMSA), and carbamazepine (CBZ), revealing sensitivity to data quality and emphasizing the importance of robust monitoring and transferable data sets. In addition to removal via treatment processes, dilution in the environment substantially reduces contaminant concentrations, highlighting the influence of the hydrological setting and the associated climate-related environmental factors on exposure outcomes. The EWEX framework serves as a decision-support tool for utilities and regulators, facilitating risk-informed management of emerging contaminants in water reuse systems. Future work should expand the data set for poorly studied compounds, refine treatment process representations, especially for short-chain PFAS, and validate model outputs through real-world monitoring.

A risk-based human health exposure assessment (HHEA) model was developed to evaluate the exposure for humans in 4 circular economy (CE) routes investigated in 6 of the 7 case studies in the project PROMISCES. The HHEA is a probabilistic tool evaluating the risk posed to human health. The HHEA was applied to the following routes: 1) semi-closed drinking water cycle; 2) groundwater remediation; 3) water reuse for agricultural irrigation; and 4) nutrient recovery. Each of these exposure routes results in a product – drinking water or lettuce – which can be consumed by humans. For some routes, the exposure is purely theoretical, while for others, the entire process chain is investigated in the PROMISCES case study.

The HHEA is built on Bayesian principles and includes Bayesian updating, which enables assessment of risk under conditions of low data availability and high uncertainty. This is particularly useful for evaluation of substances such as PFAS and other industrial persistent, mobile and potentially toxic (iPMT) substances, the removal of which in treatment processes is not yet well studied in literature. The deliverable explains the different treatments, environmental matrices, and substances which were the focus of the initial assessment. It describes the construction of the HHEA model, with explanations of how different data types – literature data, site specific data, and modelled data – are used to update the prior probability of the removal factor for substances in a process. It also describes how non-technical processes, such as mixing or evaporation, have been included into the treatment trains evaluated. Finally, individual reference quotients for the substances are established, which are used to assess the relative risk of the final concentrations in the products which could be consumed by humans.

Poster presented at the IWA Leading Edge Technologies Conference in Essen, Germany in June 2024