Quantitative structure fate relationships for multimedia environmental analysis

Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models...

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Detalhes bibliográficos
Autor: Martínez Brito, Izacar Jesús
Formato: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2010
País:España
Recursos:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/8590
Acesso em linha:http://www.tdx.cat/TDX-0825110-122638
http://hdl.handle.net/10803/8590
Access Level:acceso abierto
Palavra-chave:principal component analysis(PCA)
physicochemical properties
multimedia environmental model (MEM)
molecular descriptors
fate estimations
domain of applicability(DOA)
supervised learning algorithms
self organizing map(SOM)
radial basis function(RBF)
quantitative stucture-fate relationship(QSFR)
quantitative property-fate relationship(QPFR)
support vector regression(SVR)
uncertainty analysis
unsupervised learning algorithms
artificial neural network(ANN)
backpropagation network(BPN)
504
62
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dc.title.none.fl_str_mv Quantitative structure fate relationships for multimedia environmental analysis
title Quantitative structure fate relationships for multimedia environmental analysis
spellingShingle Quantitative structure fate relationships for multimedia environmental analysis
Martínez Brito, Izacar Jesús
principal component analysis(PCA)
physicochemical properties
multimedia environmental model (MEM)
molecular descriptors
fate estimations
domain of applicability(DOA)
supervised learning algorithms
self organizing map(SOM)
radial basis function(RBF)
quantitative stucture-fate relationship(QSFR)
quantitative property-fate relationship(QPFR)
support vector regression(SVR)
uncertainty analysis
unsupervised learning algorithms
artificial neural network(ANN)
backpropagation network(BPN)
504
62
title_short Quantitative structure fate relationships for multimedia environmental analysis
title_full Quantitative structure fate relationships for multimedia environmental analysis
title_fullStr Quantitative structure fate relationships for multimedia environmental analysis
title_full_unstemmed Quantitative structure fate relationships for multimedia environmental analysis
title_sort Quantitative structure fate relationships for multimedia environmental analysis
dc.creator.none.fl_str_mv Martínez Brito, Izacar Jesús
author Martínez Brito, Izacar Jesús
author_facet Martínez Brito, Izacar Jesús
author_role author
dc.contributor.none.fl_str_mv Grifoll Taverna, Jordi
Giralt, Francesc
Universitat Rovira i Virgili. Departament d'Enginyeria Química
dc.subject.none.fl_str_mv principal component analysis(PCA)
physicochemical properties
multimedia environmental model (MEM)
molecular descriptors
fate estimations
domain of applicability(DOA)
supervised learning algorithms
self organizing map(SOM)
radial basis function(RBF)
quantitative stucture-fate relationship(QSFR)
quantitative property-fate relationship(QPFR)
support vector regression(SVR)
uncertainty analysis
unsupervised learning algorithms
artificial neural network(ANN)
backpropagation network(BPN)
504
62
topic principal component analysis(PCA)
physicochemical properties
multimedia environmental model (MEM)
molecular descriptors
fate estimations
domain of applicability(DOA)
supervised learning algorithms
self organizing map(SOM)
radial basis function(RBF)
quantitative stucture-fate relationship(QSFR)
quantitative property-fate relationship(QPFR)
support vector regression(SVR)
uncertainty analysis
unsupervised learning algorithms
artificial neural network(ANN)
backpropagation network(BPN)
504
62
description Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models (MEMs) using several physicochemical properties estimated from QSARs. Dimensionless compartmental mass ratios of 468 validation chemicals were compared, in logarithmic units, between: a) SimpleBox 3, a Level III MEM, propagating random property values within statistical distributions of widely recommended QSARs; and, b) Support Vector Regressions (SVRs), acting as Quantitative Structure-Fate Relationships (QSFRs), linking mass ratios to molecular weight and constituent counts (atoms, bonds, functional groups and rings) for training chemicals. Best predictions were obtained for test and validation chemicals optimally found to be within the domain of applicability of the QSFRs, evidenced by low MAE and high q2 values (in air, MAE≤0.54 and q2≥0.92; in water, MAE≤0.27 and q2≥0.92).
publishDate 2010
dc.date.none.fl_str_mv 2010
2010
2010
2011
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://www.tdx.cat/TDX-0825110-122638
http://hdl.handle.net/10803/8590
url http://www.tdx.cat/TDX-0825110-122638
http://hdl.handle.net/10803/8590
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Rovira i Virgili
publisher.none.fl_str_mv Universitat Rovira i Virgili
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407368550285312
spelling Quantitative structure fate relationships for multimedia environmental analysisMartínez Brito, Izacar Jesúsprincipal component analysis(PCA)physicochemical propertiesmultimedia environmental model (MEM)molecular descriptorsfate estimationsdomain of applicability(DOA)supervised learning algorithmsself organizing map(SOM)radial basis function(RBF)quantitative stucture-fate relationship(QSFR)quantitative property-fate relationship(QPFR)support vector regression(SVR)uncertainty analysisunsupervised learning algorithmsartificial neural network(ANN)backpropagation network(BPN)50462Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models (MEMs) using several physicochemical properties estimated from QSARs. Dimensionless compartmental mass ratios of 468 validation chemicals were compared, in logarithmic units, between: a) SimpleBox 3, a Level III MEM, propagating random property values within statistical distributions of widely recommended QSARs; and, b) Support Vector Regressions (SVRs), acting as Quantitative Structure-Fate Relationships (QSFRs), linking mass ratios to molecular weight and constituent counts (atoms, bonds, functional groups and rings) for training chemicals. Best predictions were obtained for test and validation chemicals optimally found to be within the domain of applicability of the QSFRs, evidenced by low MAE and high q2 values (in air, MAE≤0.54 and q2≥0.92; in water, MAE≤0.27 and q2≥0.92).Las propiedades fisicoquímicas de un gran espectro de contaminantes químicos son desconocidas. Esta tesis analiza la posibilidad de evaluar la distribución ambiental de compuestos utilizando algoritmos de aprendizaje supervisados alimentados con descriptores moleculares, en vez de modelos ambientales multimedia alimentados con propiedades estimadas por QSARs. Se han comparado fracciones másicas adimensionales, en unidades logarítmicas, de 468 compuestos entre: a) SimpleBox 3, un modelo de nivel III, propagando valores aleatorios de propiedades dentro de distribuciones estadísticas de QSARs recomendados; y, b) regresiones de vectores soporte (SVRs) actuando como relaciones cuantitativas de estructura y destino (QSFRs), relacionando fracciones másicas con pesos moleculares y cuentas de constituyentes (átomos, enlaces, grupos funcionales y anillos) para compuestos de entrenamiento. Las mejores predicciones resultaron para compuestos de test y validación correctamente localizados dentro del dominio de aplicabilidad de los QSFRs, evidenciado por valores bajos de MAE y valores altos de q2 (en aire, MAE≤0.54 y q2≥0.92; en agua, MAE≤0.27 y q2≥0.92).Universitat Rovira i VirgiliGrifoll Taverna, JordiGiralt, FrancescUniversitat Rovira i Virgili. Departament d'Enginyeria Química2011201020102010info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://www.tdx.cat/TDX-0825110-122638http://hdl.handle.net/10803/8590TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/85902026-06-14T12:46:07Z
score 15,300719

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