Hernandez-Betancur Jose D, Ruiz-Mercado Gerardo J, Martin Mariano
Department of Chemical Engineering, University of Salamanca, Salamanca 37008, Spain.
Office of Research & Development, US Environmental Protection Agency, Cincinnati, Ohio 45268, United States.
ACS Sustain Chem Eng. 2023 Feb 24;11(9):3594-3602. doi: 10.1021/acssuschemeng.2c05662. eCollection 2023 Mar 6.
Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.
从生命周期的角度分析化学品及其对环境的影响，可以进行全面的分析，其中会考虑到报废（EoL）活动。化学风险评估、预测环境排放以及寻找报废途径和暴露场景都依赖于化学流数据的可用性。然而，获取此类数据并系统地确定报废活动和潜在的化学暴露场景具有挑战性。因此，这项工作基于基于化学结构的机器学习（ML）模型创建了定量结构-转移关系（QSTR）模型，以辅助环境管理决策，来预测潜在的工业报废活动、化学流分配、环境释放和暴露途径。根据ML实验跟踪，进一步的多标签分类方法可能会提高QSTR模型的可预测性。所开发的QSTR模型将帮助利益相关者预测和理解潜在的报废管理活动和回收循环，从而为全球市场中的新化学品或现有化学品进行环境决策和报废暴露评估。
