The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water ( k OH ) for 118 emerging micropollutants, by means of quantitati...

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Bibliographic Details
Published inEnvironmental science and pollution research international Vol. 24; no. 35; pp. 27366 - 27375
Main Authors Ortiz, Erlinda V., Bennardi, Daniel O., Bacelo, Daniel E., Fioressi, Silvina E., Duchowicz, Pablo R.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
Subjects
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Conformation
Contaminants
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Hydroxyl radicals
Mathematical models
Micropollutants
Oxidation
Oxidation rate
Regression analysis
Research Article
Waste Water Technology
Water Management
Water Pollution Control
Water treatment
Molecular descriptors
Reaction rate constant
Quantitative structure-property relationships
Water micropollutant
Replacement method
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Summary:In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water ( k OH ) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: R train 2 = 0.88 , RMS train  = 0.21, while for the test set is R test 2 = 0.87 , RMS test  = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.
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ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-017-0315-5