Chemicals in industrial and commercial products play crucial roles in the life quality of humankind. But these substances need proper management during their life cycle stages (e.g., manufacturing, use, end-of-life (EoL)) to minimize harmful effects on human health and the environment. Chemical risk assessment looks to determine those events or exposure scenarios that may result in adverse environmental and human health outcomes by understanding and preventing their causes. Therefore, estimating comprehensive EoL material flow data and activities is crucial in performing risk assessments to inform industrial, government, and community stakeholders. This work consists of creating a comprehensive EoL database and machine learning (ML) models to estimate potential EoL activities like recycling, disposal, and energy recovery and describe EoL exposure scenarios of chemicals. Thus, the results and findings from this contribution can be a starting point to supplement or update data used to address the reporting requirements established by regulatory databases, thereby incorporating the needed data entries for supporting human health and environmental risk assessments at the EoL stage. In addition, the potential releases, hazards, and risks identified in this work create an opportunity to assist in developing safer chemical recycling infrastructures to handle chemicals and support sustainable materials management efforts to achieve a U.S. chemical circular economy.

Estimating environmental releases at the chemical end-of-life stage to support risk assessment and circular economy

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