QSAR models for the removal of organic micropollutants in four different river water matrices

Handle URI:
http://hdl.handle.net/10754/562138
Title:
QSAR models for the removal of organic micropollutants in four different river water matrices
Authors:
Sudhakaran, Sairam; Calvin, James ( 0000-0002-8442-6994 ) ; Amy, Gary L.
Abstract:
Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E LUMO-E HOMO), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R 2, greater than 0.8. Internal and external validations were performed on the models. © 2011 Elsevier Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program
Publisher:
Elsevier
Journal:
Chemosphere
Issue Date:
Apr-2012
DOI:
10.1016/j.chemosphere.2011.12.006
PubMed ID:
22245076
Type:
Article
ISSN:
00456535
Appears in Collections:
Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSudhakaran, Sairamen
dc.contributor.authorCalvin, Jamesen
dc.contributor.authorAmy, Gary L.en
dc.date.accessioned2015-08-03T09:45:41Zen
dc.date.available2015-08-03T09:45:41Zen
dc.date.issued2012-04en
dc.identifier.issn00456535en
dc.identifier.pmid22245076en
dc.identifier.doi10.1016/j.chemosphere.2011.12.006en
dc.identifier.urihttp://hdl.handle.net/10754/562138en
dc.description.abstractOzonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E LUMO-E HOMO), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R 2, greater than 0.8. Internal and external validations were performed on the models. © 2011 Elsevier Ltd.en
dc.publisherElsevieren
dc.subjectAdvanced oxidation process (AOP)en
dc.subjectMolecular descriptorsen
dc.subjectPPCPsen
dc.subjectQSARen
dc.subjectQuantum-chemicalen
dc.subjectValidationen
dc.titleQSAR models for the removal of organic micropollutants in four different river water matricesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.identifier.journalChemosphereen
dc.contributor.institutionTexas A and M University, College Station, TX, United Statesen
kaust.authorSudhakaran, Sairamen
kaust.authorCalvin, Jamesen
kaust.authorAmy, Gary L.en
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