Evaluation of Predicted and Observed Data on Biotransformation of Twenty-Nine Trace Organic Chemicals

Handle URI:
http://hdl.handle.net/10754/209395
Title:
Evaluation of Predicted and Observed Data on Biotransformation of Twenty-Nine Trace Organic Chemicals
Authors:
Bertolini, Maria
Abstract:
Trace organic chemicals present in household products, pesticides, pharmaceuticals and personal care products may have adverse ecotoxicological effects once they are released to the environment. These chemicals are usually transported with the sewage to wastewater treatment facilities, where they might be attenuated depending on the degree of treatment applied prior to discharge to receiving streams. This study evaluates the removal performance of 29 trace organic compounds during two different activated sludge treatment systems. Predominant attenuation processes such as biotransformation and sorption for the target compounds were identified. Biotransformation rate constants determined in this study were used to assess removal of compounds from other treatment plants with similar operational conditions, using data gathered from the literature. The commercial software Catalogic was applied to predict environmental fate of chemicals. The software program consisted of four models able to simulate molecular transformations and to generate degradation trees. In order to assess the accuracy of this program in predicting biotransformation, one biodegradation model is used to contrast predicted degradation pathway with metabolic pathways reported in the literature. The predicted outcome was correct for more than 40 percent of the 29 targeted substances, while 38 percent of the chemicals exhibited some degree of lower agreement between predicted and observed pathways. Percent removal data determined for the two treatment facilities was compared with transformation probability output from Catalogic. About 80 percent of the 29 compounds exhibited a good correlation between probability of transformation of the parent compound and percent removal data from the two treatment plants (R2 = 0.82 and 0.9). Based upon findings for 29 trace organic chemicals regarding removal during activated sludge treatment, attacked fragments present in their structures, predicted data from Catalogic, and peer-reviewed pathways, possible indicator compounds capable of representing the removal of other compounds based on similar structures were identified. In conclusion, nine among the 29 select compounds were grouped into two categories showing similarities between removal, probability of transformation and attacked fragments. If more chemicals are evaluated, this approach could be useful to establish other indicator compounds based on identification of groups of chemicals with similar fate, properties, and structures.
Advisors:
Drewes, Jörg E.
Committee Member:
Amy, Gary L.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Program:
Chemical and Biological Engineering
Issue Date:
Jul-2011
Type:
Thesis
Appears in Collections:
Theses; Physical Sciences and Engineering (PSE) Division; Chemical and Biological Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.advisorDrewes, Jörg E.en
dc.contributor.authorBertolini, Mariaen
dc.date.accessioned2012-02-04T08:23:10Z-
dc.date.available2012-02-04T08:23:10Z-
dc.date.issued2011-07en
dc.identifier.urihttp://hdl.handle.net/10754/209395en
dc.description.abstractTrace organic chemicals present in household products, pesticides, pharmaceuticals and personal care products may have adverse ecotoxicological effects once they are released to the environment. These chemicals are usually transported with the sewage to wastewater treatment facilities, where they might be attenuated depending on the degree of treatment applied prior to discharge to receiving streams. This study evaluates the removal performance of 29 trace organic compounds during two different activated sludge treatment systems. Predominant attenuation processes such as biotransformation and sorption for the target compounds were identified. Biotransformation rate constants determined in this study were used to assess removal of compounds from other treatment plants with similar operational conditions, using data gathered from the literature. The commercial software Catalogic was applied to predict environmental fate of chemicals. The software program consisted of four models able to simulate molecular transformations and to generate degradation trees. In order to assess the accuracy of this program in predicting biotransformation, one biodegradation model is used to contrast predicted degradation pathway with metabolic pathways reported in the literature. The predicted outcome was correct for more than 40 percent of the 29 targeted substances, while 38 percent of the chemicals exhibited some degree of lower agreement between predicted and observed pathways. Percent removal data determined for the two treatment facilities was compared with transformation probability output from Catalogic. About 80 percent of the 29 compounds exhibited a good correlation between probability of transformation of the parent compound and percent removal data from the two treatment plants (R2 = 0.82 and 0.9). Based upon findings for 29 trace organic chemicals regarding removal during activated sludge treatment, attacked fragments present in their structures, predicted data from Catalogic, and peer-reviewed pathways, possible indicator compounds capable of representing the removal of other compounds based on similar structures were identified. In conclusion, nine among the 29 select compounds were grouped into two categories showing similarities between removal, probability of transformation and attacked fragments. If more chemicals are evaluated, this approach could be useful to establish other indicator compounds based on identification of groups of chemicals with similar fate, properties, and structures.en
dc.language.isoenen
dc.titleEvaluation of Predicted and Observed Data on Biotransformation of Twenty-Nine Trace Organic Chemicalsen
dc.typeThesisen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberAmy, Gary L.en
thesis.degree.disciplineChemical and Biological Engineeringen
thesis.degree.nameMaster of Scienceen
dc.person.id101844en
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