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.

The PROMISCES project aims to develop innovative, systemic solutions to protect health, environment, and natural resources from persistent, mobile and potentially toxic (PM(T)) substances by addressing regulatory gaps and promoting circular economy principles. This deliverable, in particular seeks to:

· Identify inconsistencies, gaps, and challenges within the existing EU legal and policy framework related to PM(T) substances.

· Promote harmonized regulatory approaches across environmental compartments.

· Provide EU and national policymakers with actionable, evidence-based policy recommendations to improve the management of PMT(s) in the soil-sediment-water system (and beyond).

· Emphasize that updated policy approaches address disparities and technical, financial and social challenges across Member States (MS).

The "Toolbox Fate & Transport Modelling of PMTs in the Environment" is a key deliverable from the H2020 PROMISCES project. This toolbox is a demonstrator that includes a collection of models developed in the PROMISCES project which are designed to assess the fate and transport of persistent, mobile, and toxic substances (PMTs) across various scales (local, regional) and conditions (e.g., urban run-off, bank filtration, unsaturated zone, groundwater).
This toolbox presents the basic information with links to the software and model input files with which the models can be run. This deliverable is intended for qualified modellers. It is complementary with the Guidance document, deliverable D2.4 (Zessner et al., 2025) which describes how to apply modelling tools in a tiered way as part of predictive risk assessment.

The scope of this document, produced as part of the H2020 PROMISCES project, is to provide guidance for applications of models with a specific focus on model trains for the assessment of exposure to PMTs as part of the predictive risk assessment related to surface and groundwater. This document explains the basic concepts of specific models and how best to use them in model
trains in the framework of a tiered approach. The intention is to inform users and interested stakeholders about what needs to be considered when using different methods, what is the best use of specific models, what are the best combinations in model trains and what are their current limitations.

The Horizon 2020 project PROMISCES aims to increase the circularity of resources by overcoming barriers associated with the presence of PM(T)s in the soil-sediment-water system.

This deliverable provides guidance on how to co-create a solution strategy for dealing with PM(T)(s) in a circular economy. For this, we have used the experience and lessons learnt in the co-creation workshops organized within the PROMISCES project.

Highlights

• PFAS and other persistent substances found in industrial urban stormwater runoff

• PFOA-equivalent concentrations for PFAS-24 exceeded EU proposal for surface- and groundwater

• Urban stormwater runoff from industrial sites is one source of PFAS in surface waters

Currently, there is uncertainty about emissions of pharmaceuticals into larger closed ecosystems that are at risk such as the Baltic Sea. There is an increasing need for selecting the right strategies on advanced wastewater treatment. This study analysed 35 pharmaceuticals and iodinated X-ray contrast media in effluents from 82 Wastewater Treatment Plants (WWTPs) across Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland and Sweden. Measured concentrations from Finland and Denmark were compared to predicted effluent concentrations using different levels of refinement. The concentrations predicted by the Total Residue Approach, as proposed by the European Medicines Agency, correlated with R(2) of 0.18 and 0.031 to measured ones for Denmark and Finland, respectively and the predicted data were significantly higher than the measured ones. These correlations improved substantially to R(2) of 0.72 and 0.74 after adjusting for estimated human excretion rates and further to R(2) = 0.91 and 0.78 with the inclusion of removal rates in WWTPs. Temporal analysis of compound variations in a closely monitored WWTP showed minimal fluctuation over days and weeks for most compounds but revealed weekly shifts in iodinated X-ray contrast media due to emergency-only operations at X-ray clinics during weekends and an abrupt seasonal change for gabapentin. The findings underscore the limitations of current predictive models and findings (...) demonstrate how these methodologies can be refined by incorporating human pharmaceutical excretion/metabolization as well as removal in wastewater treatment plants to more accurately forecast pharmaceutical levels in aquatic environments.

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

Pathogen removal in managed aquifer recharge (MAR) systems is dependent upon numerous operational, physicochemical water quality, and biological parameters. Due to the site-specific conditions affecting these parameters, guidelines for specifying pathogen removal have historically taken rather precautionary and conservative approaches in order to protect groundwater quality and public health. A literature review of regulated pathogens in MAR applications was conducted and compared to up-and-coming indicators and surrogates for pathogen assessment, all of which can be gathered into a toolbox from which regulators and operators alike can select appropriate pathogens for monitoring and optimization of MAR practices. Combined with improved knowledge of pathogen fate and transport obtained through lab- and pilot-scale studies and supported by modeling, this foundation can be used to select appropriate, site-specific pathogens for regarding a more efficient pathogen retention, ultimately protecting public health and reducing costs. This paper outlines a new 10 step-wise workflow for moving towards determining robust removal credits for pathogens based on risk management principles. This approach is tailored to local conditions while reducing overly conservative regulatory restrictions or insufficient safety contingencies. The workflow is intended to help enable the full potential of MAR as more planned water reuse systems are implemented in the coming years.

https://www.ncbi.nlm.nih.gov/pubmed/36931188