| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4482436 | Water Research | 2012 | 14 Pages |
Recent studies have demonstrated the presence of trace-level pharmaceutically active compounds (PhACs) and endocrine disrupting compounds (EDCs) in a number of finished drinking waters (DWs). Since there is sparse knowledge currently available on the potential effects on human health associated with the chronic exposure to trace levels of these Emerging Contaminants (ECs) through routes such as DW, it is suggested that the most appropriate criterion is a treatment criterion in order to prioritize ECs to be monitored during DW preparation. Hence, only the few ECs showing the lowest removals towards a given DW Treatment (DWT) process would serve as indicators of the overall efficiency of this process and would be relevant for DW quality monitoring. In addition, models should be developed for estimating the removal of ECs in DWT processes, thereby overcoming the practical difficulties of experimentally assessing each compound. Therefore, the present review has two objectives: (1) to provide an overview of the recent scientific surveys on the occurrence of PhACs and EDCs in finished DWs; and (2) to propose the potential of Quantitative-Structure-Activity-Relationship-(QSAR)-like models to rank ECs found in environmental waters, including parent compounds, metabolites and transformation products, in order to select the most relevant compounds to be considered as indicators for monitoring purposes in DWT systems.
Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (228 K)Download as PowerPoint slideHighlights► Pharmaceutically Active Compounds (PhACs) and Endocrine Disrupting Compounds (EDCs). ► Occurrence of Emerging Compounds (ECs) in finished Drinking Water (DW). ► Need for monitoring of ECs through conventional DW treatment processes. ► Removal criterion is a main criterion for selecting indicator ECs in DW treatment. ► Quantitative Structure Activity Relationship-like model to select indicator ECs.
