Dhirendra Mishra , Pramila Goyal

  • Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi-110016, India

Received: April 22, 2015
Revised: July 27, 2015
Accepted: September 28, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2015.04.0249  

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Cite this article:
Mishra, D. and Goyal, P. (2016). Neuro-Fuzzy Approach to Forecast NO2 Pollutants Addressed to Air Quality Dispersion Model over Delhi, India. Aerosol Air Qual. Res. 16: 166-174. https://doi.org/10.4209/aaqr.2015.04.0249


  • Combination of ANN and Fuzzy logic has been used for air quality forecasting.
  • AERMOD improves the forecasting ability of models.
  • Statistical analysis reflects that NF is performing better than others.



Air pollution forecasting is the most important environmental issue in urban areas as it is useful to assess the effects of air pollutants on human health. It has been observed that the air pollution has been increased above the standard level in the urbanized area of Delhi and will be a major problem in the next few years. Therefore, the main objective of the present study is to develop the model that can forecast daily concentrations of air pollutions in one-day advance. In the present study, the artificial intelligence based Neuro-Fuzzy (NF) model has been proposed for air quality forecasting and the concentration of nitrogen dioxide (NO2) pollutant has been chosen for analysis. The available meteorological variables viz. temperature, pressure, relative humidity, wind speed and direction, visibility and the estimated concentrations through AERMOD. The application of introducing AERMOD aims to improve the forecasting ability of model on the basis the emissions from anthropogenic sources. The training and validation have been made with the eight and two year’s available seasonal daily data respectively. The evaluation of the model has been made by comparing its results with observed values as well as other statistical models like MLR and ANN, which reveals that the NF model is performing well and can be used for operational use.

Keywords: AERMOD; Artificial neural network; Delhi; Neuro-Fuzzy; NO2; Statistical measure

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