Quantitative structure–property relationships (QSPRs) on direct photolysis of PCDDs

Chen, Jingwen; Quan, Xie; Schramm, Karl‐Werner; Kettrup, A.; Yang, Fenglin

doi:10.1016/s0045-6535(00)00554-3

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…EE1-O, EN1-O, and qO describe the character of the oxygen atoms and play an important role in the relation. This supports the suggestion that fission of the ether bond in the dioxin ring is the most likely route for direct photolysis (Chen et al, 2001c). The combination of MW and bond order of the carbon-halogen bonds (BO) led to correct estimate of φ of 17 substituted bromo and iodobenzenes (r 2 = 0.789; Table S9; Chen et al, 1998b), and the quantum yield of 12 substituted aromatic halides was best correlated with BS and Es (r 2 = 0.940; Table S9; Peijnenburg et al, 1992).…”

Section: Abiotic Degradation Insupporting

confidence: 88%

“…Several descriptors related to bond order were useful for some environmental parameters (Tables S4, S5, and S10): the bond order of the carbonhalogen bonds (BO; Chen et al, 1998b;Chen et al, 2001c;Zhao et al, 2001); the OH bond order (BO OH ), which is a measure of the strength of OH bond (Citra, 1999;Hollingsworth et al, 2002); the average bond order (ABO(N)), which is a term to correct the deficiency of the electrostatic or hydrogenbonding parameter for the N-atom containing compounds (Katritzky et al, 1998); and the minimum bond order of an atom C (MinC; Pompe and Veber, 2001). The bond strength (BS) of the carbon-halogen bond to be broken enables the differentiation between the reactivity of the various halogen atoms (F, Br, Cl, and I;Peijnenburg et al, 1992).…”

Section: Quantum-chemical Descriptorsmentioning

confidence: 99%

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Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

Mamy¹,

Patureau²,

Barriuso³

et al. 2014

Critical Reviews in Environmental Science and Technology

124

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A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

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Section: Abiotic Degradation Insupporting

confidence: 88%

Section: Quantum-chemical Descriptorsmentioning

confidence: 99%

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

Mamy¹,

Patureau²,

Barriuso³

et al. 2014

Critical Reviews in Environmental Science and Technology

124

View full text Add to dashboard Cite

show abstract

“…The first method relys on experience, the observable or perceivable characters of the studied molecules, and the possible underlying mechanism. For example, photohydrolysis is a main pathway for photolysis of halogenated aromatic compounds, thus various quantum chemical descriptors characterizing the C-X bonds were computed and employed for QSARs development on photolysis quantum yields of halogenated compounds [17,18] . The second method relies on the established models.…”

Section: Principles and Methods Of (Q)sarsmentioning

confidence: 99%

“…Some early QSAR models mainly focused on properties related with environmental partitioning (such as S W [28,29] , K OW [30,31,42] , bioconcentration factors (BCF) [43] , octanol-air partition coefficients (K OA ) [44,45] and soil or sediment adsorption coefficients (K OC ) [46,47] ) and acute toxicity to aquatic organisms (median lethal concentration (LC 50 ) [48] or median effect concentration (EC 50 ) [49] ). Later, QSARs studies were extended to environmental endocrine disruptor activity [50 -52] , and reaction kinetics of organic pollutants, such as biodegradability [53] , photolysis rate constants [54] or quantum yields [17,18] , reduction rate constants by zero-valent iron [15] , oxidation rate constants by hydroxyl radicals [16] , etc.…”

Section: Development Of (Q)sarsmentioning

confidence: 99%

Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds

Chen

et al. 2008

Sci. China Ser. B-Chem.

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Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR), collectively referred to as (Q)SARs, play an important role in ecological risk assessment (ERA) of organic chemicals. (Q)SARs can fill the data gap for physical-chemical, environmental behavioral and ecotoxicological parameters of organic compounds; they can decrease experimental expenses and reduce the extent of experimental testing (especially animal testing); they can also be used to assess the uncertainty of the experimental data. With the development for several decades, (Q)SARs in environmental sciences show three features: application orientation, multidisciplinary integration, and intelligence. Progress of (Q)SAR technology for ERA of toxic organic compounds, including endpoint selection and mathematic methods for establishing simple, transparent, easily interpretable and portable (Q)SAR models, is reviewed. The recent development on defining application domains and diagnosing outliers is summarized. Model characterization with respect to goodness-of-fit, stability and predictive power is specially presented. The purpose of the review is to promote the development of (Q)SARs orientated to ERA of organic chemicals.(Q)SARs, ecological risk assessment, application domain, stability, predictive power

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“…In recent years, the quantitative structure and activity or property relationship (QSAR/QSPR) study of PCDD/Fs has raised great concern [2,3,4,5,6]. Artificial neural networks (ANN) have been widely used in QSAR/QSPR study owing to its prominent non-linear mapping ability.…”

mentioning

confidence: 99%

Prediction of n -octanol/water partition coefficients for polychlorinated dibenzo- p -dioxins using a general regression neural network

Zheng

Huang

Lü

2003

Analytical and Bioanalytical Chemistry

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A general regression neural network was used for the first time to study quantitative structure and property relationships of organic pollutants to correlate and predict n-octanol/water partition coefficients of polychlorinated dibenzo- p -dioxins from their topological molecular descriptors. In total, 42 polychlorinated dibenzo- p -dioxins and dibenzo- p -dioxins were available for this study-42 polychlorinated dibenzo- p -dioxins and dibenzo- p -dioxins in the training data set and 41 polychlorinated dibenzo- p -dioxins in the test data set. Partial least squares regression, back propagation network and general regression neural network models were trained using the training data set, and the accuracy of the models obtained were examined by the use of leave-one-out cross-validation. For prediction of the n-octanol/water partition coefficient, the best method is the general regression neural network. With the test data set, the correlation coefficient, root mean square error and mean absolute relative error for the general regression neural network model are 0.9276, 0.22 and 2.79%, respectively. For describing the structure of polychlorinated dibenzo- p -dioxins, the topological molecular descriptors outperform the mobile order and disorder thermodynamic method.

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Quantitative structure–property relationships (QSPRs) on direct photolysis of PCDDs

Cited by 16 publications

References 21 publications

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds

Prediction of n -octanol/water partition coefficients for polychlorinated dibenzo- p -dioxins using a general regression neural network

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