Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure–activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of differen...

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Bibliographic Details
Main Authors: Ying Zhang, Liangxu Xie, Dawei Zhang, Xiaojun Xu, Lei Xu
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Molecules
Subjects:
persistent organic pollutants
air half-life
quantitative structure–activity relationships
molecular descriptors
machine learning
Online Access:https://www.mdpi.com/1420-3049/28/22/7457

Internet

https://www.mdpi.com/1420-3049/28/22/7457

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