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

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 (SO2, NO2, and PM10 (Particulate Matter < 10 μm)). The model was parameterized by using a large set of data collected in European cities (especially, Paris and its suburbs, Athens, Krakow, Prague, and Rome) during field exposure campaigns (French, European, and international programs). This statistical model, called NEUROPT-Glass, comes from an artificial neural network with two hidden layers and uses a non-linear parametric regression named Multilayer Perceptron (MLP). The results display a high determination coefficient (R2 = 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.

► Glass optical properties (haze) are impaired by the atmospheric pollution.
► We modeled the haze as a function of time and main pollutants (NO2, SO2, PM10).
► A neural network model gives better results than previous existing models.
► Soiling in cities and benefits of pollution reduction policies can be assessed.