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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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/8590 |
| Acceso en línea: | http://www.tdx.cat/TDX-0825110-122638 http://hdl.handle.net/10803/8590 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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). |
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