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

Detalhes bibliográficos
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
Resumo: 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
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spelling Sampaio, Silvio Césarhttp://lattes.cnpq.br/9197019775809808Reis, Ralpho Rinaldo doshttp://lattes.cnpq.br/0979626502949916Vilas Boas, Marcio Antoniohttp://lattes.cnpq.br/8467243260512730Reis, Ralpho Rinaldo doshttp://lattes.cnpq.br/0979626502949916Diete, Jonathanhttp://lattes.cnpq.br/0507188444713095Frigo, Jiam Pireshttp://lattes.cnpq.br/6443025153770870http://lattes.cnpq.br/1712992286070431Olguín, Carlos José Maria2017-09-04T17:30:26Z2017-02-17OLGUÍN, Carlos José Maria. Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente. 2017. 115 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel,2017 .http://tede.unioeste.br/handle/tede/3006The 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 equivalentO coeficiente de sorção do solo normalizado para o conteúdo de carbono orgânico (Koc) é um parâmetro físico-químico utilizado em avaliações de risco ambiental e na determinação do destino final das substâncias químicas lançadas na natureza. Vários modelos para prever este parâmetro foram propostos com base na relação entre LogKoc e LogP. A dificuldade e o custo para a obtenção de valores experimentais de LogP levaram ao desenvolvimento de algoritmos para calculá-los. Assim, no primeiro artigo desta tese foram considerados diversos algoritmos gratuitos para cálculo de LogP, e concluiu-se que os melhores modelos QSPR para predizer o coeficiente de sorção do solo de compostos orgânicos não iónicos foram obtidos usando os algoritmos ALOGPs, KOWWIN e XLOGP3. Neste estudo, foram demonstradas a importância e a utilidade do teste de equivalência estatística utilizado, dados que nos permitiram afirmar que os modelos obtidos dos algoritmos considerados são estatisticamente equivalentes. Assim, na impossibilidade de obterem-se valores de LogP a partir de um dos algoritmos, valores obtidos por outro podem ser usados. Verificou-se ainda que os modelos apresentados neste estudo possuem qualidade estatística e capacidade de predição compatíveis à de modelos mais complexos, publicados recentemente na área. Adicionalmente, a necessidade de se realizar a validação da predição de um modelo QSPR a partir de um conjunto de dados que não foi utilizado na geração do modelo é uma prática bem aceita na área. Nesse contexto, alguns trabalhos exploraram o impacto que diversos tamanhos de conjuntos de treinamento teriam na capacidade de predição dos modelos QSPR gerados, não chegando a resultados conclusivos. Assim, no segundo artigo desta tese, foi mostrado que, a partir de conjuntos de treinamento não tão grandes, modelos QSPR estatisticamente equivalentes podem ser desenvolvidos e que tais modelos têm capacidade de predição similar daqueles criados a partir de um conjunto de treinamento maior. Para isto, modelos foram gerados considerando valores de LogP do conjunto de treinamento total, calculados com o algoritmo ALOGPs e também com subconjuntos do mesmo (i.e., metades, quartos e oitavos). Este estudo, assim como o anterior, confirmou a importância do uso do teste de equivalência estatística utilizado nesta tese já que foi verificado que, seguindo os procedimentos adotados, os modelos obtidos com subconjuntos do conjunto de treinamento são estatisticamente equivalentes.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2017-09-04T17:30:26Z No. of bitstreams: 2 Carlos_Olguin2017.pdf: 2821259 bytes, checksum: 4f44c019ceff1c4613be9b0b525a188e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2017-09-04T17:30:26Z (GMT). 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dc.title.por.fl_str_mv Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
dc.title.alternative.eng.fl_str_mv Modeling of soil sorption coefficient from persistent organic pollutants in the environment
title Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
spellingShingle Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
Olguín, Carlos José Maria
Risco ambiental
Coeficiente de partição
Modelos QSPR
Environmental risk
Partition coefficient
QSPR models
ENGENHARIA SANITARIA::RECURSOS HIDRICOS
title_short Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
title_full Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
title_fullStr Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
title_full_unstemmed Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
title_sort Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente
author Olguín, Carlos José Maria
author_facet Olguín, Carlos José Maria
author_role author
dc.contributor.advisor1.fl_str_mv Sampaio, Silvio César
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/9197019775809808
dc.contributor.advisor-co1.fl_str_mv Reis, Ralpho Rinaldo dos
dc.contributor.advisor-co1Lattes.fl_str_mv http://lattes.cnpq.br/0979626502949916
dc.contributor.referee1.fl_str_mv Vilas Boas, Marcio Antonio
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/8467243260512730
dc.contributor.referee2.fl_str_mv Reis, Ralpho Rinaldo dos
dc.contributor.referee2Lattes.fl_str_mv http://lattes.cnpq.br/0979626502949916
dc.contributor.referee3.fl_str_mv Diete, Jonathan
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/0507188444713095
dc.contributor.referee4.fl_str_mv Frigo, Jiam Pires
dc.contributor.referee4Lattes.fl_str_mv http://lattes.cnpq.br/6443025153770870
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/1712992286070431
dc.contributor.author.fl_str_mv Olguín, Carlos José Maria
contributor_str_mv Sampaio, Silvio César
Reis, Ralpho Rinaldo dos
Vilas Boas, Marcio Antonio
Reis, Ralpho Rinaldo dos
Diete, Jonathan
Frigo, Jiam Pires
dc.subject.por.fl_str_mv Risco ambiental
Coeficiente de partição
Modelos QSPR
topic Risco ambiental
Coeficiente de partição
Modelos QSPR
Environmental risk
Partition coefficient
QSPR models
ENGENHARIA SANITARIA::RECURSOS HIDRICOS
dc.subject.eng.fl_str_mv Environmental risk
Partition coefficient
QSPR models
dc.subject.cnpq.fl_str_mv ENGENHARIA SANITARIA::RECURSOS HIDRICOS
description 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
publishDate 2017
dc.date.accessioned.fl_str_mv 2017-09-04T17:30:26Z
dc.date.issued.fl_str_mv 2017-02-17
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dc.identifier.citation.fl_str_mv OLGUÍN, Carlos José Maria. Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente. 2017. 115 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel,2017 .
dc.identifier.uri.fl_str_mv http://tede.unioeste.br/handle/tede/3006
identifier_str_mv OLGUÍN, Carlos José Maria. Modelagem do coeficiente de sorção do solo de poluentes orgânicos persistentes no meio ambiente. 2017. 115 f. Tese(Doutorado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel,2017 .
url http://tede.unioeste.br/handle/tede/3006
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