26 October 2016 Improvement of retrieval algorithms for severe air pollution
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Abstract
Increased emissions of anthropogenic aerosols associated with economic growth can lead to increased concentrations of hazardous air pollutants. Furthermore, dust storms or biomass burning plumes can cause serious environmental hazards, yet their aerosol properties are poorly understood. Our research group has worked on the development of an efficient algorithm for aerosol retrieval during hazy episodes (dense concentrations of atmospheric aerosols). It is noted that near UV measurements are available for detection of carbonaceous aerosols. The biomass burning aerosols (BBA) due to large-scale forest fires and/or burn agriculture exacerbated the severe air pollution. It is known that global warming and climate change have caused increasing instances of forest fires, which have in turn accelerated climate change. It is well known that this negative cycle decreases the quality of the global environment and human health.

The Japan Aerospace Exploration Agency (JAXA) has been developing a new Earth observing system, the GCOM (Global Change Observation Mission) project, which consists of two satellite series: GCOM-W1 and GCOM-C1. The first GCOM-C satellite will board the SGLI (second generation GLI [global imager]) to be launched in early 2017. The SGLI is capable of multi-channel (19) observation, including a near UV channel (0.380 μm) and two polarization channels at red and near-infrared wavelengths of 0.67 and 0.87 μm. Thus, global aerosol retrieval will be achieved with simultaneous polarization and total radiance.

In this study, algorithm improvement for aerosol remote sensing, especially of BBA episodes, is examined using Terra/MODIS measurements from 2003, when the GLI and POLDER-2 sensors were working onboard the Japanese satellite ADEOS-2.
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Sonoyo Mukai, Sonoyo Mukai, Itaru Sano, Itaru Sano, Makiko Nakata, Makiko Nakata, } "Improvement of retrieval algorithms for severe air pollution", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 1000811 (26 October 2016); doi: 10.1117/12.2240485; https://doi.org/10.1117/12.2240485
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