Adad, A. and Hmammouchi, R. and Taghki, A.I. and Abdellaoui, A. and Bouachrine, M. and Lakhlifi, T. (2013) Atmospheric half-lives of persistent organic pollutants (POPs) study combining DFT and QSPR results. Journal of Chemical and Pharmaceutical Research, 5 (7). pp. 28-41.

Full text not available from this repository.
Official URL:


Quantitative Structure-Property Relationship (QSPR) study was applied to the prediction of the characteristic of Persistent Organic Pollutants (POPs) screening for atmosphere persistence. The mean and maximum half-life estimations for degradation in air of 45 Nations Environment Program (UNEP) POPs and possible POPs were modeled using the parameters from quantum-chemical calculations at density functional theory (DFT) level. It was expected that the main contribution to the degradation rate was given by the EHOMO. Parameter Principal Component Analysis (PCA) method, the Multiple Linear Regression method (MLR), Partial Least Square analysis (PLS) and the Artificial Neural Network (ANN), showed a determination coefficient (R2) more than 0, 9. The prediction results were in excellent agreement with the experimental value.

Item Type: Article
Uncontrolled Keywords: ANN; DFT; Half-life; MLR; PCA; PLS, Degradation; Linear regression; Neural networks; Principal component analysis; Quantum chemistry, Organic pollutants, environmental, industrial and domestic chemicals; persistent organic pollutant; unclassified drug, air analysis; air pollution; article; artificial neural network; atmospheric dispersion; chemical structure; degradation; density functional theory; experimental model; half life time; multiple linear regression analysis; partial least squares regression; prediction; principal component analysis; quantitative structure property relation; quantum chemistry
Subjects: Pharmacology, Toxicology and Pharmaceutics
Divisions: SCIENTIFIC PRODUCTION > Pharmacology, Toxicology and Pharmaceutics
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:49

Actions (login required)

View Item View Item