This paper presents knowledge-based planning of the most appropriate treatment schemes for drinking water supply (DWS) systems, jointly applying statistical tools and experimental tests. Milan City was chosen as the case study, its DWS system being composed of more than 20 DWS units, widespread in the urban area. First, multivariate statistical techniques (factor analysis and cluster analysis) were applied to groundwater monitoring data, to identify specific contaminations of the captured groundwater and their spatial distribution. In detail, eight typical compositions of captured groundwater and their distribution in DWS units were identified, leading to the selection of different treatment processes to be adopted in DWS units. Then, process performances were experimentally evaluated on representative groundwater samples. Heterotrophic denitrification was tested at pilotscale on groundwater contaminated by nitrate, identifying optimal operating and design parameters for complying with regulation limits. Granular activated carbon (GAC) adsorption was tested at laboratory-scale for the removal of volatile organic compounds (VOCs) and pesticides, present in various ratios in groundwater samples: removal efficiencies were obtained and the occurrence of competition phenomena was highlighted. Air stripping was tested at pilot-scale for removing the most volatile VOCs (trichloroethylene, tetrachloroethylene), highlighting its usefulness as pre-treatment before adsorption to increase GAC lifetime.
|Titolo:||Knowledge-based planning of groundwater treatment trains for an efficient drinking water supply system in urban areas|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.1 Articolo in Rivista|
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|2017 - Antonelli - WST-WS - Knowledge-based planning.pdf||Articolo principale||PDF editoriale||Accesso riservato|