ArticleJournal of hazardous materials2021
Data engineering for tracking chemicals and releases at industrial end-of-life activities.
Article in Journal of hazardous materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Exploring automated tracking of chemicals through their conditions of use to support life cycle chemical assessment.Journal of industrial ecology · 2025Article
- Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.Environmental science & technology · 2024Article
- Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach.Resources, conservation, and recycling · 2023Article
- Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models.ACS sustainable chemistry & engineering · 2023Article
- A generic scenario analysis of end-of-life plastic management: Chemical additives.Journal of hazardous materials · 2023Article
- A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data.Applied sciences (Basel, Switzerland) · 2022Article
- A data engineering approach for sustainable chemical end-of-life management.Resources, conservation, and recycling · 2022Article
- A data engineering framework for on-site end-of-life industrial operations.Journal of cleaner production · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.