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.