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Título:
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SO2 trajectories in a complex terrain environment using CALPUFF dispersion model, OMI and MODIS data
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Autores:
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SAGAN, VASIT, Autor ;
PASKEN, ROBERT, Autor ;
JORGE ZARAUZ, Autor ;
KROTKOV, NICKOLAY, Autor
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Tipo de documento:
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documento electrónico
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Fecha de publicación:
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2018
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ISBN/ISSN/DL:
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69289
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Dimensiones:
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pp. 99-109
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Nota general:
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En: International Journal of Applied Earth Observation and Geoinformation No.69, 2018
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Langues:
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Inglés
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Clasificación:
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ANÁLISIS
DIÓXIDO DE SULFURO
MEDIO AMBIENTE
PUBLICACIONES TÉCNICOS LATU
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Resumen:
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Latest improvements in the resolution of atmospheric satellite sensors that measure chemical constituents from space have led to enhanced detection of trace gases. This paper explores the use of sulfur dioxide (SO2) level 2 dataset from OMI instrument, in conjunction with aerosol optical depth and Ångström exponent data from MODIS spectroradiometer, to estimate SO2 loads under clear and turbid atmospheres. The spatial patterns of SO2 loads in polluted atmospheric conditions are compared with a regional pollutant dispersion model (CALPUFF) and field observations near the Andes Peruvian city La Oroya, which is one of the most polluted places in the world. The efficacy of this methodology is further examined incorporating synchronous wind vectors. Results show that the spatial-temporal dynamics of OMI SO2 is in agreement with field measurements and CALPUFF. The SO2 satellite data obtained under optimal viewing conditions and clear skies are also compared with field observations. A correlation is found between in-situ measurements and OMI estimations. The correlation increases for days with predominantly fine aerosols when Ångström exponents are between 0.7 and 1. Moreover, pixel averaging techniques and low and high spatial frequency filtration, applied to OMI SO2 data, results in a more reliable representation of the mean SO2 plume. The paper concludes that anthropogenic SO2 can be monitored from space, even for turbid sky conditions. This demonstrates the potential for the use of satellite products to improve the air quality prediction models.
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