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Combining In Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: Accounting for the Transformation Products of decaBDE and Its Alternatives.

Ziye ZhengHans Peter H ArpGregory M PetersPatrik L Andersson
Published in: Environmental science & technology (2020)
Transformation products ought to be an important consideration in chemical alternatives assessment. In this study, a recently established hazard ranking tool for alternatives assessment based on in silico data and multicriteria decision analysis (MCDA) methods was further developed to include chemical transformation products. Decabromodiphenyl ether (decaBDE) and five proposed alternatives were selected as case chemicals; biotic and abiotic transformation reactions were considered using five in silico tools. A workflow was developed to select transformation products with the highest occurrence potential. The most probable transformation products of the alternative chemicals were often similarly persistent but more mobile in aquatic environments, which implies an increasing exposure potential. When persistence (P), bioaccumulation (B), mobility in the aquatic environment (M), and toxicity (T) are considered (via PBT, PMT, or PBMT composite scoring), all six flame retardants have at least one transformation product that can be considered more hazardous, across diverse MCDA. Even when considering transformation products, the considered alternatives remain less hazardous than decaBDE, though the range of hazard of the five alternatives was reduced. The least hazardous of the considered alternatives were melamine and bis(2-ethylhexyl)-tetrabromophthalate. This developed tool could be integrated within holistic alternatives assessments considering use and life cycle impacts or additionally prioritizing transformation products within (bio)monitoring screening studies.
Keyphrases
  • risk assessment
  • molecular docking
  • oxidative stress
  • machine learning
  • heavy metals
  • big data
  • decision making
  • life cycle
  • deep learning
  • simultaneous determination