PREDICTION OF PM2.5 CONCENTRATIONS USING TEMPERATURE INVERSION EFFECTS BASED ON AN ARTIFICIAL NEURAL NETWORK

Abstract. Today, air pollutant is a big challenge for busy and big cities due to its direct effect on both human health and the environment. Tehran, as the capital city of Iran, concludes 12 million people and is one of the most polluted cities in Iran. According to the reports, the main cause of Tehran's pollution is particle matters. The main factors affecting the density and distribution of pollution in Tehran are topography, traffic, and meteorological parameters including wind speed and direction, environment temperature, cloud cover, relative humidity, the sunshine overs a day, the rainfall, pressure, and temperature inversion. To help the urban management of Tehran, in this paper, a novel method is proposed to predicted PM2.5 concentration for upcoming 72 hours. The results show that the proposed model has high capability in predicting PM2.5 concentration and the achieved statistic coefficient of determination (R²) was equal to 0.61–0.79, which indicates the goodness of fit of our proposed model supports the prediction of PM2.5 concentration.