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Part I. Identifying anthropogenic markers in surface waters influenced by treated effluents: a tool in potable water reuse
Authors:Sirivedhin Tanita  Gray Kimberly A
Affiliation:Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
Abstract:In potable water reuse, treated wastewater becomes part of the drinking water supply. An important question associated with this practice is whether or not the organic quality of the treated wastewater is chemically different from that of non-human impacted water. This question was addressed in a case study of indirect potable water reuse where the organic matrix of the South Platte River was analyzed upstream and downstream of the discharge of treated wastewater effluent using conventional water quality parameters combined with pyrolysis-GC/MS. Effluent-derived organic material (EfOM) was found to be more aliphatic and had higher organic nitrogen and halogen content compared to organic material derived from "natural" (non-anthropogenic) sources (NOM). Seasonal changes that resulted from the change in the contributions of aquatic and terrestrial sources were not observed in EfOM; but they were strongly observed in NOM under the control of natural processes. Using principal component and factor analyses, the pyrolysis fragments of phenol, alkyl-phenols, and acetic acid were identified as the seasonal indicators for the NOM set of samples. In contrast, benzaldehyde, benzonitrile, chlorobutanoic acid, furancarboxaldehyde, and methylfurancarboxaldehyde were identified as the indicators for wastewater inputs for the EfOM set of samples. Overall, the results from conventional water quality parameters and pyrolysis-GC/MS revealed that: (1) EfOM bears a chemical signature distinct from NOM and (2) under the conditions of this study, EfOM discharged to the South Platte River persisted and controlled organic quality at downstream points.
Keywords:Pyrolysis-GC/MS  Potable water reuse  Wastewater markers  Anthropogenic markers  Principal component analysis  Factor analysis
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