Air Quality Forecasting through Coupled Model over Delhi

Pramila Goyal, Dhirendra Mishra, and Anikender Kumar


Pollutants, AERMOD, Artificial Neural Network, Integrated System


This paper describes the development of a coupled model for hourly forecasting of air quality one day in advance in terms of concentration of respirable suspended particulate matter (RSPM/PM10) for Delhi. The coupled model is formed by coupling an air quality dispersion model (AERMOD) and Artificial Neural Network (ANN). The previous hour’s predicted concentrations of AERMOD and the meteorological parameters (wind speed, temperature, humidity and wind direction) are used as input to ANN. A comparative study of concentration of RSPM, resulted from AERMOD, ANN, COUPLED model has been made at different locations, which shows that forecasted RSPM concentration of COUPLED model is in better agreement with observed values than the values of AERMOD and ANN.

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