About the project
PROMISCES addresses the challenge of understanding PFAS (per- and polyfluoroalkyl substances), a group of over 12,000 chemicals known for their persistence in the environment. Experimental data on key PFAS properties, such as how they dissolve in water or move between air, soil, and water, is extremely limited. To solve this, PROMISCES developed advanced computer models to predict these properties. This approach fills critical data gaps, supporting more accurate environmental assessments and helping policymakers manage the risks posed by PFAS.
Description of success
QSAR Lab Ltd., a project member and main partner in digital chemistry within the PROMISCES project, developed a cutting-edge methodology to predict the behaviour of PFAS in the environment. The project extended the capabilities of existing QSPR (Quantitative Structure-Property Relationship) models to estimate crucial properties like vapor pressure, solubility, and partitioning between air, water, and soil. By using a combination of physics-based quantum chemistry and data-driven models, PROMISCES have improved the predictions of phys-chem properties of PFAS.
These predictions enable researchers to model how PFAS travel and accumulate in the environment, which is essential for assessing their potential risks to ecosystems and human health. To make these tools accessible, the project created a user-friendly web app that allows researchers to predict PFAS properties quickly and consistently.
Figure 1: Diagram of the methodology process.
Highlights
Developed a predictive digital tool to estimate key PFAS properties, including solubility, vapor pressure, and partition coefficients.
Addressed data gaps that traditional experimental methods cannot easily fill, improving understanding of PFAS distribution in the environment.
Created an intuitive web app, enabling fast and consistent predictions for a wide range of PFAS.
Provided insights that support better environmental modelling and chemical risk assessments.
Enabled faster, cost-effective environmental risk assessments compared to laboratory-based approaches.
Supported regulatory efforts with insights that help shape standards and tighten controls for PFAS usage and disposal.
Outputs
Extended the predictive capability of QSPR models for PFAS vapor pressure, solubility in water, and octanol-water partition coefficient
Filled in data gaps regarding environmental data on PFAS by using predicted variables and the soil adsorption coefficient, which are responsible for the transport and distribution of PFAS in the environment.
Published relevant peer-reviewed articles for the above outputs.
Developed the Streamlit app for easy prediction of phys-chem properties of PFAS.
Impact
PROMISCES has significantly advanced the understanding of PFAS behaviour in the environment. The predictive tools offer a faster, cost-effective alternative to traditional laboratory experiments, making it possible to study PFAS properties at scale. These insights are crucial for modelling how PFAS move through air, water, and soil, and for identifying their potential to accumulate in plants, animals, and humans. The project provides critical data to support tighter regulations, informed decision-making, and improved management of PFAS risks.
The tools and methods developed through PROMISCES can also be adapted to study other complex chemical groups, offering long-term value for environmental science and policy.
Lessons
Modelling specific physicochemical properties can predict results that would be impossible to generate experimentally under laboratory conditions.
Generating simulated data for QSPR modelling makes it possible to study the physicochemical properties of PFAS in a faster, more efficient, and less expensive way.
Other information
PROMISCES improved how PFAS behaviour is predicted by combining quantum chemistry with data-driven modelling. A key achievement was closing data gaps for thousands of PFAS compounds, allowing for more reliable environmental risk assessments. The work extended QSPR models to cover important properties like Henry’s Law constant, air-water partitioning, and soil adsorption - factors critical to understanding how PFAS move and accumulate. These insights support regulatory decisions and provide a practical tool for researchers and policymakers working on PFAS management.
