Particulate matter at surface: Northern Italy monitoring based on satellite remote sensing (Articolo in rivista)

Type
Label
  • Particulate matter at surface: Northern Italy monitoring based on satellite remote sensing (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Di Nicolantonio, W., A. Cacciari and C. Tomasi (2009)
    Particulate matter at surface: Northern Italy monitoring based on satellite remote sensing
    in IEEE journal of selected topics in signal processing
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Di Nicolantonio, W., A. Cacciari and C. Tomasi (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Carlo Gavazzi Space Spa, Bologna CNR Area Science Park, Institute of Atmospheric Science and Climate (ISAC-CNR), Bologna CNR Area Science Park, I-40129 Bologna, Italy (literal)
Titolo
  • Particulate matter at surface: Northern Italy monitoring based on satellite remote sensing (literal)
Abstract
  • Growing attention has been paid over recent years to the possibility of monitoring surface particulate matter (PM) concentrations through the use of satellite observations. Satellite remote sensing of both aerosol and trace gas constituents can be usefully employed in air quality monitoring (AQ). The MODIS (Terra- Aqua/NASA platforms) aerosol optical properties were used here in a semi-empirical approach to estimate PM2.5 content at ground level. PM2.5 samplings were employed to convert aerosol optical depth AOD into PM estimates, considering meteorological fields simulated by MM5. Thus, daily maps of satellite-based PM2.5 concentrations over Northern Italy were derived. Comparison with daily PM2.5, sampled on the ground during the QUITSAT project over six validation sites of the Po valley, showed good agreement (R2 = 0.68 and R2 = 0.59 for MODIS/Terra and MODIS/ Aqua, respectively), with the satellite-based concentrations tending to underestimate the values by at most around 20%. Monthly averaged values were also compared providing good agreement, with R2 = 0.7 for each platform. (literal)
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