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Multicriteria Decision Analysis Characterization of Chemical Hazard Assessment Data Sources
Author(s) -
He Haoyang,
Malloy Timothy F,
Schoenung Julie M
Publication year - 2019
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.4182
Subject(s) - safer , risk analysis (engineering) , hazard , data quality , multiple criteria decision analysis , hazard analysis , computer science , quality (philosophy) , reliability (semiconductor) , reliability engineering , data mining , operations research , engineering , operations management , business , metric (unit) , philosophy , chemistry , power (physics) , computer security , physics , organic chemistry , epistemology , quantum mechanics
Chemical hazard assessment (CHA), which aims to investigate the inherent hazard potential of chemicals, has been developed with the purpose of promoting safer consumer products. Despite the increasing use of CHA in recent years, finding adequate and reliable toxicity data required for CHA is still challenging due to issues regarding data completeness and data quality. Also, collecting data from primary toxicity reports or literature can be time consuming, which promotes the use of secondary data sources instead. In this study, we evaluate and characterize numerous secondary data sources on the basis of 5 performance attributes: reliability, adequacy, transparency, volume, and ease of use. We use GreenScreen for Safer Chemicals v1.4 as the CHA framework, which defines the endpoints of interest used in this analysis. We focused upon 34 data sources that reflect 3 types of secondary data: chemical‐oriented data sources, hazard‐trait–oriented data sources, and predictive data sources. To integrate and analyze the evaluation results, we applied 2 multicriteria decision analysis (MCDA) methodologies: multiattribute utility theory (MAUT) and stochastic multiobjective acceptability analysis (SMAA). Overall, the findings in this research program allow us to explore the relative importance of performance criteria and the data source quality for effectively conducting CHA. Integr Environ Assess Manag 2019;00:1–14. © 2019 SETAC