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

ABSTRACT 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 PM2.5, relative humidity (RH) and NO2 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 PM2.5, RH and NO2 concentrations.