Predicting changes of glass optical properties in polluted atmospheric environment by a neural network model

Abstract Soiling results from the deposition of pollutants on materials. On glass, it leads to an alteration of its intrinsic optical properties. The nature and intensity of this phenomenon mirrors the pollution of an environment. This paper proposes a new statistical model in order to predict the evolution of haze ( H ) (i.e. diffuse/direct transmitted light ratio) as a function of time and major pollutant concentrations in the atmosphere (SO 2 , NO 2 , and PM 10 (Particulate Matter  R 2  = 0.88) between the measured and the predicted hazes and minimizes the dispersion of data compared to existing multilinear dose–response functions. Therefore, this model can be used with a great confidence in order to predict the soiling of glass as a function of time in world cities with different levels of pollution or to assess the effect of pollution reduction policies on glass soiling problems in urban environments.