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Data-Driven Framework for Tracking Chemical Flows at End-of-Life Stage
Citation:
Hernandez-Betancur, J., M. Martin, AND Gerardo J. Ruiz-Mercado. Data-Driven Framework for Tracking Chemical Flows at End-of-Life Stage. International Congress on Sustainability Science & Engineering (ICOSSE '20), N/A, August 02 - 05, 2020.
Impact/Purpose:
Determining whether a chemical may represent an unreasonable risk to the health of a human being and the environment is an important criterion for an adequate choice of materials for use, consumption, and regulatory decision-making. This presentation describes a data-reconciliation and learning-from-data framework for tracking a chemical into its probable recycling, recovery, and/or reuse stage. The chemical end-of-Use (EoU) management at off-site facilities may result in potential significant releases of a chemical of interest, and therefore relevant to a more complete Toxic Substance Control Act (TSCA) risk evaluation. This data reconciliation and learning-from-data framework, based on public-available regulatory databases, aims the facilitation of chemical risk evaluation under TSCA needs by addressing the uncertainty and data requirements in the disposition of a chemical of interest when determining its EoU management scenarios, which is not currently analyzed.
Description:
Determining whether a chemical may represent an unreasonable risk to the health of a human being and the environment is an important criterion for an adequate choice of materials for use, consumption, and regulatory decision-making. For example, the U.S. Environmental Protection Agency (USEPA) needs to assess the risk of chemicals under the Toxic Substances Control Act (TSCA), like those implemented by the European Chemicals Agency (ECHA) under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH). However, assessing risk at end-of-use (EoU) stages such as recycling, energy recovery, treatment, and disposal, is a time-consuming and challenging task due to data collection, manipulation, availability, uncertainty, and traceability. Therefore, this work aids by supplying a big data engineering framework, based on public-available regulatory databases, for tracking a chemical in waste streams generated at a given industrial facility and transferred to off-site locations for further management, where other releases from pollution abatement technologies may occur. The proposed approach is expected to be useful to streamline chemical risk and exposure assessment at EoU stages, considering statistical techniques, process simulation, and build learning-from-data models to predict releases of existing or new chemicals. In addition, this framework may support life cycle initiatives by supplying inventories of chemical releases from EoU activities and integrating life cycle assessment and risk analysis at EoU scenarios.