Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors

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Bibliographic Details
Title: Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors
Authors: Gramatica, P. paola.gramatica@uninsubria.it, Pilutti, P.1, Papa, E.1
Source: Atmospheric Environment. 2003, Vol. 37 Issue 22, p3115. 10p.
Subjects: Environmental degradation, Nitrate minerals, Regression analysis
Abstract: The rate constant for the nighttime degradation of 114 heterogeneous organic compounds, through reaction with nitrate radicals in the troposphere, is predicted here by quantitative structure–activity relationships modelling. The multiple linear regression approach is based on a variety of theoretical molecular descriptors, selected by the genetic algorithms-variable subset selection procedure. The proposed model, calculated on a limited subset of compounds selected by a D-optimal experimental design and checked for reliability and robustness, has good predictivity, verified by internal (QLMO2=89.6%) and “external” validation (QEXT2=95.7%). The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The average root-mean square error for the prediction of log kNO3 was 0.57, similar to (and even smaller than) the typical experimental error range. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:The rate constant for the nighttime degradation of 114 heterogeneous organic compounds, through reaction with nitrate radicals in the troposphere, is predicted here by quantitative structure–activity relationships modelling. The multiple linear regression approach is based on a variety of theoretical molecular descriptors, selected by the genetic algorithms-variable subset selection procedure. The proposed model, calculated on a limited subset of compounds selected by a D-optimal experimental design and checked for reliability and robustness, has good predictivity, verified by internal (<f>QLMO2=89.6%</f>) and “external” validation (<f>QEXT2=95.7%</f>). The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The average root-mean square error for the prediction of <f>log kNO3</f> was 0.57, similar to (and even smaller than) the typical experimental error range. [Copyright &y& Elsevier]
ISSN:13522310
DOI:10.1016/S1352-2310(03)00293-0