Issue 9, 2015

QSPR study on the photolysis half-life of PCDD/Fs adsorbed on spruce (Picea abies (L.) Karst.) needle surfaces under sunlight irradiation by using a molecular distance-edge vector index

Abstract

The quantitative structure property relationship (QSPR) for the photolysis half-life (t1/2) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) on spruce (Picea abies (L.) Karst.) needle surfaces under sunlight irradiation was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCDD/Fs. The quantitative relationship between the MDEV index and log t1/2 was modeled by using multivariable linear regression (MLR) and artificial neural network (ANN) respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction ability of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation is 3.47 and 4.25 respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation is 2.68 and 3.52 respectively. It is demonstrated that there is a quantitative relationship between the MDEV index and log t1/2 of PCDD/Fs. Both MLR and ANN are practical for modeling this relationship. The developed MLR model and ANN model can be used to predict the log t1/2 of PCDD/Fs. Thus, the log t1/2 of each PCDD/F congener was predicted by using the developed models.

Graphical abstract: QSPR study on the photolysis half-life of PCDD/Fs adsorbed on spruce (Picea abies (L.) Karst.) needle surfaces under sunlight irradiation by using a molecular distance-edge vector index

Article information

Article type
Paper
Submitted
09 Nov 2014
Accepted
17 Dec 2014
First published
17 Dec 2014

RSC Adv., 2015,5, 6617-6624

QSPR study on the photolysis half-life of PCDD/Fs adsorbed on spruce (Picea abies (L.) Karst.) needle surfaces under sunlight irradiation by using a molecular distance-edge vector index

L. Jiao, X. Wang, S. Bing, Z. Xue and H. Li, RSC Adv., 2015, 5, 6617 DOI: 10.1039/C4RA14178D

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