A Quantitative Structure-Property Relationship Study on Reaction Rate Constants for Reductive Debromination of Polybrominated Diphenyl Ethers by Zero-Valent Iron

Abstract:

A quantitative structure property relationship (QSPR) study was performed in this work to develop models for predicting reaction rate constants for reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). Both multiple linear regression (MLR) and artificial neural network (ANN) methods were employed for QSPR studies based on the experimental kinetic data of the fourteen PBDE congeners. Both the developed MLR and ANN models could give satisfactory prediction abilities, and the performance of the ANN model seems slightly better than that of the MLR model. In addition, energy of lowest unoccupied molecular orbital (ELUMO) and total energy (TE) were found to be the two relatively important variables in the ANN model via the assessment using both the Garson’s algorithm and connection weight approach.

• Periodical: Advanced Materials Research (Volumes 550-553)
• Pages: 2668-2675
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.550-553.2668
• Online since: July 2012
• Authors: Ji Wei Hu, Yuan Zhuang, Jin Luo, Xiong Hui Wei
• Keywords: MLR, Neural Network (NN), Polybrominated Diphenyl Ether (PBDE), QSPR, Reductive Debromination

References

[1] P.O. Darnerud, G.S. Eriksen, T. Jóhannesson, P.B. Larsen, M. Viluksela, Polybrominated diphenyl ethers: occurrence, dietary exposure, and toxicology. Environ. Health Perspect. Vol. 109 (2001), pp.49-68.

DOI: 10.1289/ehp.01109s149

[2] A. Sjödin, J. Patterson, G.D. Patterson, Å. Bergman, A review on human exposure to brominated flame retardants - particularly polybrominated diphenyl ethers. Environ. Int. Vol. 29 (2003), p.829–839.

DOI: 10.1016/s0160-4120(03)00108-9

[3] P.S. Haglund, D.R. Zook, H.Buser, J.W. Hu, Identification and quantification of polybrominated diphenyl ethers and methoxy-polybrominated diphenyl ethers in baltic biota. Environ. Sci. Technol. Vol. 31 (1997), pp.3281-3287.

DOI: 10.1021/es9702834

[4] R.A. Hites, Polybrominated diphenyl ethers in the environment and in people: a meta-analysis of concentrations. Environ. Sci. Technol. Vol. 38 (2004), pp.945-956.

DOI: 10.1021/es035082g

[5] X.Q. Li, D.W. Elliott, W.X. Zhang, Zero-valent iron nanoparticles for abatement of environmental pollutants: materials and engineering aspects. Crit. Rev. Solid State Vol. 31 (2006), p.111–122.

DOI: 10.1080/10408430601057611

[6] Y. Zhuang, S. Ahn, R.G. Luthy, Debromination of polybrominated diphenyl ethers by nanoscale zerovalent iron: pathways, kinetics, and reactivity. Environ. Sci. Technol. Vol. 44 (2010), pp.8236-8242.

DOI: 10.1021/es101601s

[7] A. Li, C. Tai, Z.S. Zhao, Y.W. Wang, Q.H. Zhang, G.B. Jiang, J.T. Hu, Debromination of decabrominated diphenyl ether by resin-bound iron nanoparticles. Environ. Sci. Technol. Vol. 41 (2007), pp.6841-6846.

DOI: 10.1021/es070769c

[8] J.W. Hu, L. Eriksson, Å. Bergman, E. Jakobsson, E. Kolehmainen, J. Knuutinen, R. Suontamo, X.H. Wei, Molecular orbital studies on brominated diphenyl ethers. Part II - reactivity and quantitative structure–activity (property) relationships. Chemosphere Vol. 59 (2005), pp.1043-1057.

DOI: 10.1016/j.chemosphere.2004.11.029

[9] H.X. Gao, J.M. Shreeve, Azole-Based Energetic Salts. Chem. Rev. Vol. 11 (2011), pp.7377-7436.

DOI: 10.1021/cr200039c

[10] C. Brasquet, B. Bourges, P. Le Cloirec, Quantitative structure-property relationship (QSPR) for the adsorption of organic compounds onto activated carbon cloth: comparison between multiple linear regression and neural network. Environ. Sci. Technol. Vol. 33 (1999), pp.4226-4231.

DOI: 10.1021/es981358m

[11] J.W. Hu, X.Y. Zhang, Z.W. Wang, A review on progress in QSPR studies for surfactants. Int. J. Mol. Sci. Vol. 11 (2010), pp.1020-1047.

[12] A.P. Worth, A.Bassan, J. De bruijn, A. Gallegos saliner, T. Netzeva, G. Patlewicz, M. Pavan, I. Tsakovska, S. Eisenreich, The role of the european chemicals bureau in promoting the regulatory use of (Q)SAR methods. SAR QSAR Environ. Res. Vol. 18 (2007), pp.111-125.

DOI: 10.1080/10629360601054255

[13] Y. Keum, Q.X. Li, Reductive debromination of polybrominated diphenyl ethers by zerovalent iron. Environ. Sci. Technol. Vol. 39 (2005), pp.2280-2286.

DOI: 10.1021/es048846g

[14] G.B. Rocha1, R.O. Freirel, A.M. Simasl, J.J.P. Stewart, RM1: A reparameterization of AM1 for H, C, N, O, P, S, F, Cl, Br, and I. J. Comput. Chem. Vol. 27 (2006), pp.1101-1111.

DOI: 10.1002/jcc.20425

[15] J.J.P. Stewart, MOPAC2009, Stewart Computational Chemistry, Colorado Springs, CO, USA, HTTP://OpenMOPAC.net (2011).

[16] I.T. Jolliffe, Principal Component Analysis, Series: Springer Series in Statistics, 2nd (Springer, New York 2002).

[17] Information on http://en.wikipedia.org/wiki/Principal_component_analysis.

[18] T.L. Johnson, M.M. Scherer, P.G. Tratnyek, Kinetics of halogenated organic compound degradation by iron metal. Environ. Sci. Technol. Vol. 30 (1996), pp.2634-2640.

DOI: 10.1021/es9600901

[19] G.D. Garson, Interpreting neural-network connection weights. Artif. Intell. Expert Vol. 6 (1991), p.47–51.

[20] A.T.C. Goh, Back-propagation neural networks for modeling complex systems. Artif. Intell. Eng. Vol. 9 (1995), pp.143-151.

[21] J.D. Olden, D.A. Jackson, Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model Vol. 154 (2002), pp.135-150.

DOI: 10.1016/s0304-3800(02)00064-9

[22] J.D. Olden, M.K. Joy, R.G. Death, An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model Vol. 178 (2004), p.389–397.

DOI: 10.1016/j.ecolmodel.2004.03.013