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    <identifier>10.57760/sciencedb.space.00549</identifier>
    <datestamp>2023-07-27T09:42:20Z</datestamp>
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  <dc:date>2023-07-27</dc:date>
  <dc:title>Dataset for Paper &amp;quot;Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO&amp;ldquo;</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.space.00549</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The dataset is generated for near- surface ail pollution estimates (PM2.5&amp;nbsp;, PM10&amp;nbsp;, O3&amp;nbsp;, NO2&amp;nbsp;, SO2&amp;nbsp;, and CO) from satellite measurements using LAPSO. The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques.&amp;nbsp;</dc:description>
  <dc:subject>Air pollution; deep learning; remote sensing</dc:subject>
  <dc:creator>Jian Xu</dc:creator>
  <dc:rights>RESTRICTED</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1109/TGRS.2023.3248180</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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