Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran


Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 dispersion in Tehran City. Factors which are influencing the predicted value consist of weather-related and air pollution-related data, i.e. wind speed, humidity, temperature, SO2, CO, NO2, and PM2.5 as target values. These factors have been considered in 19 measuring stations (zones) over urban area across Tehran City during four years, from March 2011 to March 2015. The results indicate that the network with hidden layer including six neurons at training epoch 113, has the best performance with the lowest error value (MSE=0.049438) on considering PM2.5 concentrations across metropolitan areas in Tehran. Furthermore, the “R” value for regression analysis of training, validation, test, and all data are 0.65898, 0.6419, 0.54027, and 0.62331, respectively. This study also represents the artificial neural networks have satisfactory implemented for resolving complex patterns in the field of air pollution.

Graphical Abstract

Artificial neural network forecast application for fine particulate matter concentration using meteorological data


  • Critical importance of Tehran's air particulate matters pollution using artificial neural network
  • PM2.5 temporal prediction has been estimated in Great Tehran
  • Using ANN approach for modeling and the data was divided into three groups of training, validating, and testing of the network
  • The function optimization technique used is the scaled conjugate gradient algorithm


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