An open framework for automated chemical hazard assessment based on GreenScreen for Safer Chemicals: A proof of concept. Wehage, K., Chenhansa, P., & Schoenung, J. M Integrated Environmental Assessment and Management, 13(1):167–176, January, 2017. ![An open framework for automated chemical hazard assessment based on GreenScreen for Safer Chemicals: A proof of concept [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex GreenScreen® for Safer Chemicals is a framework for comparative chemical hazard assessment. It is the first transparent, open and publicly accessible framework of its kind, allowing manufacturers and governmental agencies to make informed decisions about the chemicals and substances used in consumer products and buildings. In the GreenScreen® benchmarking process, chemical hazards are assessed and classified based on 18 hazard endpoints from up to 30 different sources. The result is a simple numerical benchmark score and accompanying assessment report that allows users to flag chemicals of concern and identify safer alternatives. Although the screening process is straightforward, aggregating and sorting hazard data is tedious, time-consuming, and prone to human error. In light of these challenges, the present work demonstrates the usage of automation to cull chemical hazard data from publicly available internet resources, assign metadata, and perform a GreenScreen® hazard assessment using the GreenScreen® “List Translator.” The automated technique, written as a module in the Python programming language, generates GreenScreen® List Translation data for over 3000 chemicals in approximately 30 s. Discussion of the potential benefits and limitations of automated techniques is provided. By embedding the library into a web-based graphical user interface, the extensibility of the library is demonstrated. The accompanying source code is made available to the hazard assessment community. Integr Environ Assess Manag 2017;13:167–176. © 2016 SETAC
@article{wehage_open_2017,
title = {An open framework for automated chemical hazard assessment based on {GreenScreen} for {Safer} {Chemicals}: {A} proof of concept},
volume = {13},
issn = {1551-3793},
shorttitle = {An open framework for automated chemical hazard assessment based on {GreenScreen} for {Safer} {Chemicals}},
url = {http://onlinelibrary.wiley.com/doi/10.1002/ieam.1763/abstract},
doi = {10.1002/ieam.1763},
abstract = {GreenScreen® for Safer Chemicals is a framework for comparative chemical hazard assessment. It is the first transparent, open and publicly accessible framework of its kind, allowing manufacturers and governmental agencies to make informed decisions about the chemicals and substances used in consumer products and buildings. In the GreenScreen® benchmarking process, chemical hazards are assessed and classified based on 18 hazard endpoints from up to 30 different sources. The result is a simple numerical benchmark score and accompanying assessment report that allows users to flag chemicals of concern and identify safer alternatives. Although the screening process is straightforward, aggregating and sorting hazard data is tedious, time-consuming, and prone to human error. In light of these challenges, the present work demonstrates the usage of automation to cull chemical hazard data from publicly available internet resources, assign metadata, and perform a GreenScreen® hazard assessment using the GreenScreen® “List Translator.” The automated technique, written as a module in the Python programming language, generates GreenScreen® List Translation data for over 3000 chemicals in approximately 30 s. Discussion of the potential benefits and limitations of automated techniques is provided. By embedding the library into a web-based graphical user interface, the extensibility of the library is demonstrated. The accompanying source code is made available to the hazard assessment community. Integr Environ Assess Manag 2017;13:167–176. © 2016 SETAC},
language = {en},
number = {1},
urldate = {2018-01-08},
journal = {Integrated Environmental Assessment and Management},
author = {Wehage, Kristopher and Chenhansa, Panan and Schoenung, Julie M},
month = jan,
year = {2017},
keywords = {Published, Reviewed},
pages = {167--176},
}
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