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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Remote Sensing

Background:

  • The Beijing-Tianjin-Hebei region faces persistent atmospheric pollution challenges.
  • Understanding air pollution mechanisms is crucial for effective environmental governance.
  • Mixing Layer Height (MLH) significantly impacts pollutant diffusion and heavy pollution event evolution.

Purpose of the Study:

  • To compare the performance of three widely used MLH retrieval algorithms using Mie-lidar data.
  • To identify the most effective algorithm for MLH retrieval under various pollution conditions.
  • To investigate the evolution of MLH during a heavy pollution episode in December 2016.

Main Methods:

  • Utilized Mie-lidar data collected in Beijing.
  • Compared three distinct MLH retrieval algorithms, including image edge detection.
  • Applied the optimal algorithm to analyze MLH variations during a specific pollution event.

Main Results:

  • Multi-layer structures near the surface can introduce errors in MLH detection.
  • The image edge detection algorithm demonstrated superior performance, especially during heavy pollution episodes.
  • MLH showed a positive correlation with particulate matter concentration at the onset of the pollution episode.

Conclusions:

  • The image edge detection method is reliable for MLH retrieval, even in polluted conditions.
  • Elevated pollution levels were not solely due to decreased MLH.
  • Increased particulate matter can raise the boundary layer, trapping pollutants near the surface.