- 著者
- Masayuki Takigawa Prabir K. Patra Yutaka Matsumi Surendra K. Dhaka Tomoki Nakayama Kazuyo Yamaji Mizuo Kajino Sachiko Hayashida
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.16, pp.86-91, 2020 (Released:2020-05-27)
- 参考文献数
- 39
- 被引用文献数
- 3 16
The severe air pollution events continue to occur every year during late October and early November in Delhi, forcing air/land traffic disruptions and anxiety in the daily life of the citizens. We analyze the behaviors of the air pollution events in October and November 2019 that arose from the crop-residue burning as seen using remote sensing techniques. Transport pathways and the mean transit time from the fire hotspots are evaluated using the FLEXPART (FLEXible PARTicle dispersion model). Our results suggest that the polluted regions in Delhi are partly influenced by the crop-residue burning. The uncertainty of our evaluation can be attributable to insufficient information on emission sources because the biomass burning emission based on daily-basis fire radiative power (FRP) of Moderate-Resolution Imaging Spectroradiometry (MODIS) is significantly degraded by the existence of hazy clouds. In future, it is desirable to establish a dense measurement network between Punjab and Delhi for the early detection of the source signals of aerosol emissions and their transport in this region. The FLEXPART model simulation shows the transport of emission signals from Punjab to Delhi, which further expands toward the Bengal region within a span of two days.
- 著者
- Masayuki Takigawa Prabir K. Patra Yutaka Matsumi Surendra K. Dhaka Tomoki Nakayama Kazuyo Yamaji Mizuo Kajino Sachiko Hayashida
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.2020-015, (Released:2020-04-06)
- 被引用文献数
- 16
The severe air pollution events continue to occur every year during late October and early November in Delhi, forcing air/land traffic disruptions and anxiety in the daily life of the citizens. We analyze the behaviors of the air pollution events in October and November 2019 that arose from the crop-residue burning as seen using remote sensing techniques. Transport pathways and the mean transit time from the fire hotspots are evaluated using the FLEXPART (FLEXible PARTicle dispersion model). Our results suggest that the polluted regions in Delhi are partly influenced by the crop-residue burning. The uncertainty of our evaluation can be attributable to insufficient information on emission sources because the biomass burning emission based on daily-basis fire radiative power (FRP) of Moderate-Resolution Imaging Spectroradiometry (MODIS) is significantly degraded by the existence of hazy clouds. In future, it is desirable to establish a dense measurement network between Punjab and Delhi for the early detection of the source signals of aerosol emissions and their transport in this region. The FLEXPART model simulation shows the transport of emission signals from Punjab to Delhi, which further expands toward the Bengal region within a span of two days.
- 著者
- Kaho NITTA Prakhar MISRA Sachiko HAYASHIDA
- 出版者
- The Remote Sensing Society of Japan
- 雑誌
- 日本リモートセンシング学会誌 (ISSN:02897911)
- 巻号頁・発行日
- vol.42, no.1, pp.36-50, 2022-02-10 (Released:2022-04-26)
- 参考文献数
- 36
In this study, the advantage of the state-of-the-art sensor TROPOspheric Monitoring Instrument (TROPOMI) for air pollution research in Indian subcontinent is examined by comparing it with the conventional sensor Ozone Monitoring Instrument (OMI), which has been utilized for more than 15 years since its launch in 2004. The OMI nitrogen dioxide (NO2) dataset was used for comparison, namely, version 4.0 of the standard product developed by NASA (named OMNO2). As our focus is the application of satellite sensors to the study of air pollution, only the areas with high NO2 concentration were extracted for the analysis. A one-year comparison between July 2018 and June 2019 showed strong positive correlation between TROPOMI and the OMI product, with Pearson correlation coefficient of 0.76. The difference between OMI and TROPOMI was generally random. Compared with OMNO2 version 4.0, the annually averaged difference of TROPOMI was found to be (−0.8±1.1)×1015 (1σ) molecules cm−2, which is −22 %±24 % (1σ) as a relative value. The good agreement between TROPOMI and OMI confirmed the compatibility of the observed values. The high resolution of TROPOMI enables the observation of small-scale sources of NO2 that cannot be detected by OMI, which allowed the identification of some examples of NO2 hotspots over power plants in India. The recent identification of a rapid decrease in NO2 after the COVID-19 lockdown in March 2020 in India using TROPOMI data demonstrates the potential of this sensor to detect rapid changes in anthropogenic activities. Our analysis demonstrates usefulness of the NO2 data from TROPOMI, and fruitful scientific results are expected in the future.