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- W2286085010 abstract "Environmental context The safeguarding of water supplies and drinking water is a major issue when considering human health risk management. In this context, an automatic and on-site fractionation system for the detection of organic contaminants has been developed. The main goal of this system is to establish an initial diagnosis by identifying a class of substances involved in a case of pollution. Abstract This paper proposes a new approach for the preconcentration, fractionation, prediction and detection of organic micropollutants in water. The main aim of this study was to implement an innovative fractionation method and a prediction model based on the physicochemical properties of compounds and interactions with the sorbent of solid-phase extraction cartridges. Two sorbents (Strata-SAX and Oasis-HLB) and three specific eluting solvents (mixture of methanol and sodium chloride and mixtures of acetonitrile and ultrapure water) were used to separate organic compounds into five specific fractions according to their physicochemical properties (anionic and cationic or neutral compounds with various polarity). More than 75 molecules (43 molecules individually studied and a mixture with 43 molecules, including 11 molecules individually studied) including pesticides, pharmaceuticals, endocrine disruptors and polycyclic aromatic hydrocarbons, with various properties were studied, and the results showed that the elution fraction can be predicted for more than 85% of the compounds. This methodology could simplify the analytical chain by reducing detailed analysis on limited categories of compounds, and could be used for a rapid and on-site screening of organic compounds." @default.
- W2286085010 created "2016-06-24" @default.
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- W2286085010 date "2016-01-01" @default.
- W2286085010 modified "2023-10-10" @default.
- W2286085010 title "Automatic and predictive fractionation of organic micropollutants in contaminated water" @default.
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- W2286085010 doi "https://doi.org/10.1071/en15135" @default.
- W2286085010 hasPublicationYear "2016" @default.
- W2286085010 type Work @default.