Water-soluble organic compounds (WSOCs) are an important group of chemical tracers that may give relevant information on the relative strengths of primary emission sources and secondary photochemical processes on air quality. In fact, they can be primarily emitted into the atmosphere by a multiplicity of sources -- including power plants, vehicular circulation, meat cooking operations and biomass burning -- or secondarily produced by photochemical atmosphere reactions from both biogenic and anthropogenic precursors. This paper describes the development of a GC-MS procedure for the simultaneous analysis of WSOCs in atmospheric aerosols. The extraction operating conditions must be properly selected to achieve the highest yield for several target analytes with a wide range of water solubility. The response surface methodology (RSM) including central composite design (CCD) was applied to optimize solvent extraction for a large number of dicarboxylic acids and sugars. The factors considered were the solvent type (characterized by solvent polarity parameter ) and volume (10-20 ml). On the basis of RSM and experimental evidence, the optimum extraction solvent was a mixture of metane:dichlorometane (90:10) using a volume of 10 ml. The optimized procedure provides the low detection limits (lower than 2 ngm-3) and the good reproducibility (RSD lower than 13%) required by environmental monitoring. The developed protocol was extended to 22 target analytes that are relevant chemical markers, i.e., 15 carboxylic acids and 7 sugars. In addition, the suitability of the optimized procedure was verified by application to PM filters collected under different conditions, i.e., different seasons (summer vs. winter), different sampling sites (urban vs. rural), different particle size dimensions (PM2.5 vs. PM1).

GC-MS METHOD FOR SIMULTANEOUS ANALYSIS OF DICARBOXYLIC ACIDS AND SUGARS IN ATMOSPHERIC AEROSOL: RESPONSE SURFACE METHODOLOGY FOR OPTIMIZING SOLVENT EXTRACTION.

PIETROGRANDE, Maria Chiara;CHIEREGHIN, Sara;BACCO, Dimitri
2012

Abstract

Water-soluble organic compounds (WSOCs) are an important group of chemical tracers that may give relevant information on the relative strengths of primary emission sources and secondary photochemical processes on air quality. In fact, they can be primarily emitted into the atmosphere by a multiplicity of sources -- including power plants, vehicular circulation, meat cooking operations and biomass burning -- or secondarily produced by photochemical atmosphere reactions from both biogenic and anthropogenic precursors. This paper describes the development of a GC-MS procedure for the simultaneous analysis of WSOCs in atmospheric aerosols. The extraction operating conditions must be properly selected to achieve the highest yield for several target analytes with a wide range of water solubility. The response surface methodology (RSM) including central composite design (CCD) was applied to optimize solvent extraction for a large number of dicarboxylic acids and sugars. The factors considered were the solvent type (characterized by solvent polarity parameter ) and volume (10-20 ml). On the basis of RSM and experimental evidence, the optimum extraction solvent was a mixture of metane:dichlorometane (90:10) using a volume of 10 ml. The optimized procedure provides the low detection limits (lower than 2 ngm-3) and the good reproducibility (RSD lower than 13%) required by environmental monitoring. The developed protocol was extended to 22 target analytes that are relevant chemical markers, i.e., 15 carboxylic acids and 7 sugars. In addition, the suitability of the optimized procedure was verified by application to PM filters collected under different conditions, i.e., different seasons (summer vs. winter), different sampling sites (urban vs. rural), different particle size dimensions (PM2.5 vs. PM1).
2012
GC-MS METHOD; ANALYSIS OF DICARBOXYLIC ACIDS AND SUGARS; ATMOSPHERIC AEROSOL; METHOD OPTIMIZATION
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1700505
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