Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente

• Autor(a) principal: Olguín, Carlos José Maria
• Data de Publicação: 2017
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br/handle/tede/3006
• topic: Risco ambiental; Coeficiente de partição; Modelos QSPR; Environmental risk; Partition coefficient; QSPR models; ENGENHARIA SANITARIA::RECURSOS HIDRICOS

The soil sorption coefficient normalized for organic carbon content (Koc) is a physicochemical parameter used in environmental risk assessments to determine the final destination of chemicals released in the environment. So, in oreder to predict this parameter, several models were proposed based on the relationship between LogKoc and LogP. The difficulty and cost to obtain experimental values of LogP have drawn to the algorithms development to calculate those values. Thus, in the first paper of this thesis, several free algorithms were considered to calculate LogP, and it was concluded that the best QSPR models to predict soil sorption coefficient of organic nonionic compounds were obtained using ALOGPs, KOWWIN and XLOGP3 algorithms. This study demonstrated the importance and usefulness of the statistical equivalence test used, since it allowed us to state that the models obtained from the considered algorithms are statistically equivalent. In this study, the both importance and usefulness of the statistical equivalence test were proved. These data allowed us to state that the models that have been obtained from the algorithms are statistically equivalent. Thus, in the impossibility of obtaining LogP values based on one of the algorithms, values obtained by another one of them can be used. It was also observed that the models presented in this study presented statistical quality and predictive capacity compatible with more complex models recently published in the area. In addition, it is a well accepted practice in the area the requirement to validate the prediction of a QSPR model from a data set that was not used in the model generation. In this context, some studies have explored the impact that several sizes of training sets would have on the predictive capacity of the generated QSPR models, consequently not reaching conclusive results. Thus, the second paper has been shown that, from not so large training sets, statistically equivalent QSPR models can be developed and that these models have similar predictive capacity to those ones created from a larger training set. Therefore, models were generated considering LogP values of the total training set, calculated with the ALOGPs algorithm and also with subsets of itself (i.e., halves, quarters and eighths). This study, just like the previous one, has confirmed the importance of using the statistical equivalence test since it was ascertained that, following the adopted procedures, the models obtained with subsets of the training set are statistically equivalent