A data-centric framework to perform chemical flow analysis

Abstract

Identification of potential end-of-life (EoL) exposure scenarios and environmental releases is important to perform chemical risk evaluation. Nonetheless, this task is time-consuming and challenging due to the vast amount of data that must be collected and the uncertainty that characterizes the EoL stage. Thus, this thesis proposes a data-driven methodology for automatically collecting data from publicly-accessible and siloed database systems and transforming them into a machine-readable structure. These data are used for exploratory data analysis and data-driven modeling to gain a better understanding of the EoL supply and management chain, identify chemical EoL exposure scenarios, and estimate releases.