Compact Algorithms for Predicting the Atmospheric Visibility Using PM_(2.5), Relative Humidity and NO_2

• Published in: Aerosol and Air Quality Research Vol. 20; no. 4; pp. 679 - 687+ap1-5
• Main Authors: Hui Yi, Jingjing Zhang, Hang Xiao, Lei Tong, Qiuliang Cai, Jiamei Lin, Weijia Yu, Matthew S. Johnson
• Format: Journal Article
• Language: English
• Published: 社團法人台灣氣膠研究學會 01.04.2020
• Subjects: Relative humidity; NO_2; Light extinction coefficient; PM_(2.5); Algorithm; Atmospheric visibility
• ISSN: 1680-8584
• DOI: 10.4209/aaqr.2019.06.0286

Visibility is a key parameter of the atmospheric environment that has attracted increasing public attention. Despite its importance, very few descriptions of methods for predicting visibility using widely available information in the literature exist. In this paper, we derive and evaluate two compact algorithms (Models I and II) for measuring and predicting visibility using records of PM_(2.5), relative humidity (RH) and NO_2 from 16 cities around the world. Models I and II are simplified algorithms derived from Pitchford's algorithm. Our analysis shows that Model I is more consistent with the observations and can accurately predict changes in visibility. In a separate part of the study, the two algorithms are trained using data sets from individual cities. Better results are obtained when the models are trained with the data from London, Sydney and the Chinese mainland cities. Model II displays broader applicability when it is simulated using a single city's data set. This study indicates that atmospheric visibility can be well quantified based on measurements of PM_(2.5), RH and NO_2 concentrations.