Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

2020 ◽  
Vol 725 ◽  
pp. 138455 ◽  
Author(s):  
Xiaojuan Qi ◽  
Xuehua Li ◽  
Hongye Yao ◽  
Yang Huang ◽  
Xiyun Cai ◽  
...  
Keyword(s):  
Organic Pollutants ◽  
Partition Coefficients ◽  
Plant Cuticle

Partition Coefficients of Active Ingredients between Plant Cuticle and Adjuvants As Related to Rates of Foliar Uptake

1997 ◽  
Vol 45 (9) ◽  
pp. 3659-3665 ◽  
Author(s):  
Peter Baur ◽  
Hend Marzouk ◽  
Jörg Schönherr ◽  
B. Terence Grayson
Keyword(s):  
Partition Coefficients ◽  
Plant Cuticle ◽  
Foliar Uptake ◽  
Active Ingredients

Prediction of Soot-Water Partition Coefficients for Persistent Organic Pollutants

2012 ◽  
Vol 518-523 ◽  
pp. 2677-2681
Author(s):  
Hui Ying Xu ◽  
Jian Wei Zou ◽  
Wei Wang
Keyword(s):  
Organic Pollutants ◽  
Persistent Organic Pollutants ◽  
Electrostatic Potential ◽  
Polybrominated Diphenyl Ethers ◽  
Partition Coefficients ◽  
Molecular Surface ◽  
Linear Regression Method ◽  
Qspr Model ◽  
Linear Relationships ◽  
Leave One Out

In the present study, geometrical optimization and electrostatic potential calculations have been performed at the HF/6-31G* level of theory for 25 investigated persistent organic pollutants (POPs), including ten polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), nine polychlorinated biphenyls (PCBs), four polycyclic aromatic hydrocarbons (PAHs) and two polybrominated diphenyl ethers (PBDEs). A number of statistically-based parameters have been obtained. Linear relationships between soot–water partition coefficients (log KSC) of POPs and the structural descriptors have been established by multiple linear regression method. The result shows that the quantities derived from electrostatic potential, together with molecular surface area (AS) and the energy of the highest occupied molecular orbital (EHOMO) can be well used to express the quantitative relationships between structure and soot–water partition coefficients of POPs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient (RCV) of 0.9797. Furthermore, the predictive power of this model was further examined for the external test set. The QSPR model established may provide a new powerful method for predicting soot–water partition coefficients (KSC) of persistent organic pollutants.

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