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Compact Algorithms for Predicting of Atmospheric Visibility Using PM2.5, Relative Humidity and NO2

Category: Optical/Radiative Properties and Remote Sensing

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DOI: 10.4209/aaqr.2019.06.0286
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To cite this article:
Yi, H., Zhang, J., Xiao, H., Tong, L., Cai, Q., Lin, J., Yu, W. and Johnson, M.S. (2020). Compact Algorithms for Predicting of Atmospheric Visibility Using PM2.5, Relative Humidity and NO2. Aerosol Air Qual. Res., doi: 10.4209/aaqr.2019.06.0286.

Hui Yi1,2,3, Jingjing Zhang1,2,3, Hang Xiao 1,3, Lei Tong1,3, Qiuliang Cai1,2,3, Jiamei Lin1,2,3, Weijia Yu4, Matthew S. Johnson4

  • 1 Center for Excellence in Regional Atmospheric Environment & Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
  • 2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 3 Ningbo Urban Environment Observation and Research Station, Chinese Academy of Sciences, Ningbo 315800, China
  • 4 Department of Chemistry, University of Copenhagen, 2100 Copenhagen, Denmark


  • Two compact algorithms are proposed for predicting atmospheric visibility.
  • Predicting atmospheric visibility with the data of PM2.5, RH and NO2.
  • PM2.5 and RH totally account for 97%–98% of the visibility variation.


Visibility is a key parameter of the atmospheric environment which has attracted increasing public attention. Despite its importance there are very few descriptions of methods for predicting visibility using widely available information in the literature. In this paper we derive and evaluate two compact algorithms (Model I and II) for measuring and predicting visibility using records of PM2.5, relative humidity (RH) and NO2 from 16 cities around the world. Model I and II are simplified algorithms which were derived from the Pitchford’s algorithm. The analysis result shows that Model I is more consistent with observations and can accurately predict the changes in visibility. In a separate part of the study the two algorithms were trained using data sets from single cities. Better results were obtained when these two models were trained with the data of London, Sydney and the Chinese mainland cities. Model II has broader applicability when simulated using a single city data set. This study indicates that atmospheric visibility could be well quantified based on the measurements of PM2.5, RH and NO2 concentrations.


Atmospheric visibility Light extinction coefficient Algorithm PM2.5 Relative humidity NO2

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