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Conditions for deriving air quality information from satellite data

Stirnberg, Roland; Cermak, Jan

This contribution presents an approach to map urban air quality using passive-sensor satellite data.
Air pollution is inherently a spatial phenomenon: airborne transport of substances yields a multi-source mixture
of pollutants at any given location, at and beyond source regions like cities. Thus, satellite data may provide infor-
mation on the distribution of pollutants relative to geophysical parameters pertaining to surface and atmospheric
states. We develop a method to estimate spatial patterns of street-level particulate matter (PM) concentrations
based on satellite-retrieved aerosol optical depth (AOD). AOD data is obtained based on the Multi-Angle
Implementation of Atmospheric Correction (MAIAC) algorithm applied on data from the Moderate Resolution
Imaging Spectroradiometer (MODIS) instrument.
The relationship between vertically integrated AOD and PM at street level is modulated by variability in
geophysical conditions. Hence, we focus on the detection of sets of conditions that allow a reliable analysis of
street-level air pollution based on satellite-derived AOD. AOD and ground-based (PM) measurements are paired
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Volltext §
DOI: 10.5445/IR/1000089021
Veröffentlicht am 03.01.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Poster
Publikationsmonat/-jahr 04.2018
Sprache Englisch
Identifikator urn:nbn:de:swb:90-890219
KITopen-ID: 1000089021
Veranstaltung EGU General Assembly (2018), Wien, Österreich, 08.04.2018 – 13.04.2018
Bemerkung zur Veröffentlichung EGU2018-13917
Externe Relationen Abstract/Volltext
Schlagwörter Remote Sensing; Air Quality; Particulate Matter
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