Lina Gao1, Peng Yan This email address is being protected from spambots. You need JavaScript enabled to view it.1, Jietai Mao2, Xiaochun Zhang1, Xiaoling Zhang3,6, Yongxue Wu3, Junshan Jing1, Jianming Xu4, Xuejiao Deng5, Wenxue Chi1 

1 Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
2 School of Physics, Peking University, Beijing 100871, China
3 Beijing Meteorological Service, Beijing 100089, China
4 Yangtze River Delta Center for Environmental Meteorology Prediction and Warning, Shanghai 200030, China
5 Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China
6 School of Atmospheric Sciences Chengdu University of Information Technology, Chengdu 610225 China

Received: July 8, 2020
Revised: January 21, 2021
Accepted: February 2, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Gao, L., Yan, P., Mao, J., Zhang, X., Zhang, X., Wu, Y., Jing, J., Xu, J., Deng, X., Chi, W. (2021). Ambient Atmospheric Aerosol Extinction Coefficient Reconstruction from PM2.5 Mass Concentrations and Application to Haze Identification in China. Aerosol Air Qual. Res. 21, 200386.


  • An aerosol extinction coefficient reconstruction method is introduced in this study.
  • Linear regression is used to decrease the bias between calculations and observations.
  • The threshold of the extinction ratio are selected to identify the haze and fog weather phenomena.


This study developed a method of reconstructing the aerosol extinction coefficient based on hourly observations of the fine-particle (PM2.5) mass concentration, relative humidity (RH), and visibility at 9 stations in China between 2014 and 2015. First, we applied κ-Kӧhler theory to evaluate the number concentration distribution of the fine particles under ambient conditions from the PM2.5 mass and then used Mie theory to calculate the aerosol extinction coefficient. Second, we established the reconstruction model and identified reference values for the relevant parameters. After sensitivity tests confirmed good agreement between the extinction coefficients obtained through combinations of various values and those resulting from the reference values, linear regression was employed to reduce the discrepancy between the reconstructed and the observed coefficients. A closure study enabled us to determine the threshold of the extinction ratio (β/βObs) and identify haze and fog weather phenomena at the stations. Finally, we assessed the bias in the predicted number of hours with haze for 61 stations in China by comparing the estimates derived from different values for the model’s parameters with those derived from the reference values and found a relative bias of less than 15% for approximately 99.8% of the stations, indicating the feasibility of our approach for detecting haze.

Keywords: Aerosol extinction reconstruction, Haze and fog, Identification

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