Integrating data engineering and process systems engineering for end-of-life chemical flow analysis

End-of-life (EoL) chemical flow analysis (CFA) is essential for understanding the potential environmental fate and exposure pathways and supporting cost-effective circular economy routes of chemicals. This study presents a multi-scale computational framework that integrates data engineering, data-driven modeling, and process systems engineering (PSE) methods to support CFA during the EoL stage of chemicals. Using methyl methacrylate (MMA) as a case study, the framework examines the EoL supply and management chain for plastic-related chemicals, leveraging publicly available regulatory data to identify potential chemical redistribution, environmental releases, and EoL exposure scenarios. The analysis finds potential inter-EoL transfers, where chemicals transition between EoL activities before disposal or reuse, potentially leading to unintended environmental releases. Other chemicals detected alongside MMA in EoL streams (co-occurring chemicals) suggest contamination in recycled materials, unintentional environmental releases during recycling, and releases from wastewater treatment systems. However, data limitations and reporting variability introduce uncertainties that may affect chemical tracking accuracy. This study underscores the need to integrate facility, process, and equipment-level data to address these challenges to refine environmental release estimates and exposure assessments. Future research should explore hybrid modeling approaches, combining top-down regulatory data with bottom-up process insights, and leverage graph-based methods for EoL supply chain simulation. Advancing data-driven CFA methodologies can provide a science-based foundation for regulatory oversight, circular economy strategies, and sustainable chemical management.