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Multivariate Adaptive Regression Splines for Prediction of Rate Constants for Radical Degradation of Aromatic Pollutants in Water

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Abstract

A quantitative structure–activity relationship was developed to predict the rate constants for radical degradation of aromatic pollutants in water. A set of 1,508 zero-to three-dimensional descriptors was used for each molecule in the data set. Multiple linear regression was used as a descriptor selection method and the multivariate adaptive regression spline (MARS) method was successfully applied for the mapping model. The root-mean-square error and coefficient of determination were obtained as 0.0996 and 0.8998, respectively. In comparison with other models, the results show that MLR–MARS can be used as a powerful model for prediction of rate constants for radical degradation of aromatic pollutants in water.

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Authors and Affiliations

  1. School of Chemistry, Damghan University, Damghan, Iran

    Kobra Zarei, Morteza Atabati & Elham Teymori

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  1. Kobra Zarei
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  2. Morteza Atabati
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  3. Elham Teymori
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Correspondence to Kobra Zarei.

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Zarei, K., Atabati, M. & Teymori, E. Multivariate Adaptive Regression Splines for Prediction of Rate Constants for Radical Degradation of Aromatic Pollutants in Water. J Solution Chem 43, 445–452 (2014). https://doi.org/10.1007/s10953-014-0143-x

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  • DOI: https://doi.org/10.1007/s10953-014-0143-x

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