Atmosphere (Nov 2017)

A Quantitatively Operational Judging Method for the Process of Large Regional Heavy Haze Event Based on Satellite Remote Sensing and Numerical Simulations

  • Qiao Wang,
  • Qing Li,
  • Zhongting Wang,
  • Hui Chen,
  • Huiqin Mao,
  • Cuihong Chen

DOI
https://doi.org/10.3390/atmos8110222
Journal volume & issue
Vol. 8, no. 11
p. 222

Abstract

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In recent years, large-area heavy haze pollution cases occur frequently in eastern China, especially evident in Beijing-Tianjin-Hebei and the surrounding regions. In order to operationally monitor the process of larger regional heavy haze events, a type of quantitative method based on satellite remote sensing and numerical simulations was first established and applied in multiple heavy haze processes in the research area. First, this study proposed the operational haze aerosol optical depth (HOD) method by combining Terra, Aqua satellite and WRF-NAQPM numerical simulation in haze days. Second, based on the coupled HOD data, we proposed the quantitative method for obtaining the process and severity degree for larger regional heavy haze events. Finally, this study used the method applying it to several typical heavy pollution events which occurred in Beijing-Tianjin-Hebei and its three surrounding provinces in the winter season from 1 November 2015 to 4 January 2016. The validation for HOD retrieval results showed that the couple HOD from this study have good accuracy, the linear correlation coefficient between retrieval HOD and the AERONET Beijing station data reached over 0.8, and the linear correlation coefficient between the retrieval HOD and the regional ground monitoring station PM2.5 data reached over 0.7. The applied results showed that the method in this study is feasible to reflect the whole process of heavy haze events. Analysis of the typical heavy haze pollution events showed that the set of quantitative haze judging method in this study was consistent with the meteorological conditions in haze days also verifying that the method for haze inversion and the process analysis is reliable.

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