Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks

Abstract

The probability of exceeding EU limit values for NO2 concentrations has increased in many European cities. Meteorological parameters have an extremely important role in evaluating the dispersion of pollutants in various city areas. This paper focuses on meteorological variations and their impact on urban background NO2 concentrations in the city of Braila for 2009-2013. The dependence between measured NO2 data and meteorological parameters are analyzed using two modeling methods: multiple linear regression and artificial neuronal networks. The dataset calculated using the proposed models indicate that artificial neural networks can be applied in the analysis and forecasting of air quality.