Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach.

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models.

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.

A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data.

The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.

A data engineering approach for sustainable chemical end-of-life management.

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. A case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e. g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

A data engineering framework for on-site end-of-life industrial operations.

Sustainable initiatives for converting end-of-life (EoL) material flows into feedstocks would make a crucial contribution towards protecting our environment and mitigating the negative impacts of anthropogenic activities. Chemical flow analysis enables decision-makers to identify potential environmental releases and exposure pathways at the EoL stage and, therefore, improves the estimation of chemical exposure. Certain industrial facilities apply on-site pollution abatement operations, thereby constituting nodes of the chemical EoL management chain that can be evaluated and improved to enable greater circularity of materials. This work enhances and extends a recently published EoL data engineering framework by using publicly-available databases, data- driven models, and analytic hierarchy approaches to track chemicals, estimate releases, and potential exposure pathways at on-site industrial pollution management operations. The extended framework develops pollution abatement unit (PAU) technologies and estimates their efficiencies, chemical releases, exposure media, operating expenses, and capital expenditures. Relevant case studies based on the food and pharmaceutical industry sectors illustrate the application of the framework for chemical flow allocation and analysis of a chemical of concern and the benefits of integrating and extending the framework with data-driven and multi-criteria decision-making models. The results show how the enhanced framework designs and evaluates PAU technology systems for managing EoL chemical flows and provides release inventories and pathways for conducting chemical risk evaluation and exposure assessment of potential on-site EoL scenarios.

Data engineering for tracking chemicals and releases at industrial end-of-life activities.

Performing risk evaluation is necessary to determine whether a chemical substance presents an unreasonable risk of injury to human health or the environment across its life cycle stages. Data gathering, reconciliation, and management for supporting risk evaluation are time-consuming and challenging, especially for end-of-life (EoL) activities due to the need for proper reporting and traceability. A data engineering framework using publicly-available databases to track chemicals in waste streams generated by industrial activities and transferred to other facilities across different U.S. locations for waste management is implemented. The analysis tracks chemicals in waste streams generated at industrial processes and handling at off-site facilities and then estimates releases from EoL activities. The final product of this effort is a framework that identifies a set of chemical, activity, and industry sector categories as well as hazardous waste flows, emission factors, and uncertainty indicators to describe EoL activities. This framework helps to identify EoL exposure scenarios that would otherwise not be evaluated. As a case study, methylene chloride, one of the first ten chemicals to undergo risk evaluation under the amended U.S. Toxic Substances Control Act, was evaluated with results highlighting potential additional exposure scenarios.

SUSTAINABILITY INDICATORS FOR END-OF-LIFE CHEMICAL RELEASES AND POTENTIAL EXPOSURE.

Understanding the chemical risk to environment and human health is an important issue when a waste management strategy and a control risk system is analyzed and selected. This is even more important at the end-of-life (recycling, recovery and disposal) scenario for a chemical due to the uncertainty in respect of the most susceptible receptors (e.g., workers), pathways (e.g., groundwater), routes (e.g., inhalation) and hazard (e.g., cancer) associated to a chemical exposure. Hence, selecting a group of sustainability performance indicators for estimating the chemical risk when evaluating end-of-life scenarios is a crucial task. Therefore, this manuscript focuses on a critical analysis of the sustainability indicators taxonomy which are used to assess chemical risk to the environment and human health during end-of-life scenarios. The insights from performing an extensive literature search in the largest database of peer-reviewed literature provide that chemical intake, hazard quotient, hazard index, and carcinogenic risk have been the most commonly used for human health chemical risk. In addition, previous research has been less focused on environment chemical risk, with ecological risk index being the most widely used indicator for. The most employed human health chemical risk sustainability indicators are part of a methodology suggested by U.S. Environmental Protection Agency for chemical risk assessment.