Degradation of Fluorotelomer-Based Polymers Contributes to the Global Occurrence of Fluorotelomer Alcohol and Perfluoroalkyl Carboxylates: A Combined Dynamic Substance Flow and Environmental Fate Modeling Analysis

2017 ◽  
Vol 51 (8) ◽  
pp. 4461-4470 ◽  
Author(s):  
Li Li ◽  
Jianguo Liu ◽  
Jianxin Hu ◽  
Frank Wania
Keyword(s):  
Environmental Fate ◽  
Substance Flow ◽  
Modeling Analysis ◽  
Environmental Fate Modeling

scholarly journals Environmental Fate Modeling of Nanoplastics in a Salinity Gradient Using a Lab-on-a-Chip: Where Does the Nanoscale Fraction of Plastic Debris Accumulate?

2021 ◽  
Vol 55 (5) ◽  
pp. 3001-3008
Author(s):  
Zélie Venel ◽  
Hervé Tabuteau ◽  
Alice Pradel ◽  
Pierre-Yves Pascal ◽  
Bruno Grassl ◽  
...  
Keyword(s):  
Environmental Fate ◽  
Salinity Gradient ◽  
Lab On A Chip ◽  
Plastic Debris ◽  
Environmental Fate Modeling

Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles

2015 ◽  
Vol 535 ◽  
pp. 150-159 ◽  
Author(s):  
Nicole Sani-Kast ◽  
Martin Scheringer ◽  
Danielle Slomberg ◽  
Jérôme Labille ◽  
Antonia Praetorius ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Environmental Fate ◽  
Engineered Nanoparticles ◽  
Environmental Fate Modeling

scholarly journals Using Information on Uncertainty to Improve Environmental Fate Modeling: A Case Study on DDT

2009 ◽  
Vol 43 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Urs Schenker ◽  
Martin Scheringer ◽  
Michael D. Sohn ◽  
Randy L. Maddalena ◽  
Thomas E. McKone ◽  
...  
Keyword(s):  
Environmental Fate ◽  
Environmental Fate Modeling

Importance of Applicability Domain of QSAR Models

2015 ◽  
pp. 180-211 ◽  
Author(s):  
Kunal Roy ◽  
Supratik Kar
Keyword(s):  
Environmental Fate ◽  
Interpolation Space ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Applicability Domain ◽  
Property Prediction ◽  
Qsar Models ◽  
Environmental Fate Modeling

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.

MATHEMATICAL ENVIRONMENTAL FATE MODELING

1988 ◽  
pp. 5-1-5.6-10
Author(s):  
Marc Bonazountas ◽  
Aviva Brecher ◽  
Robert G. Vranka
Keyword(s):  
Environmental Fate ◽  
Environmental Fate Modeling

scholarly journals Importance of Applicability Domain of QSAR Models

2017 ◽  
pp. 1012-1043
Author(s):  
Kunal Roy ◽  
Supratik Kar
Keyword(s):  
Environmental Fate ◽  
Interpolation Space ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Applicability Domain ◽  
Property Prediction ◽  
Qsar Models ◽  
Environmental Fate Modeling

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.

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