title: Quantitative structure-biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons.
abstract: Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, stepwise multiple linear regression analysis method was applied to establish quantitative structure biodegradability relationship (QSBR) between the chemical structure and a novel biodegradation activity index (qmax) of 20 polycyclic aromatic hydrocarbons (PAHs). The frequency B3LYP/6-311+G(2df,p) calculations showed no imaginary values, implying that all the structures are minima on the potential energy surface. After eliminating the parameters which had low related coefficient with qmax, the major descriptors influencing the biodegradation activity were screened to be Freq, D, MR, EHOMO and ToIE. The evaluation of the developed QSBR mode, using a leave-one-out cross-validation procedure, showed that the relationships are significant and the model had good robustness and predictive ability. The results would be helpful for understanding the mechanisms governing biodegradation at the molecular level.
authors: State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address: xp12904@126.com.
authors: Peng
authors: P
authors: Xu
authors: State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
authors: Wencheng
authors: W
authors: Ma
authors: State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address: han13946003379@163.com.
authors: Hongjun
authors: H
authors: Han
authors: State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
authors: Shengyong
authors: S
authors: Jia
authors: State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
authors: Baolin
authors: B
authors: Hou
mesh_terms: D001419:Bacteria / Q000378:metabolism*; D001673:Biodegradation, Environmental; D004784:Environmental Monitoring; D016014:Linear Models; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry* / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D011084:Polycyclic Aromatic Hydrocarbons
keywords: Leave-one-out cross-validation; Polycyclic aromatic hydrocarbons; QSBR; Stepwise multiple linear regression