Modelling urban air quality using artificial neural network

  • S. M. Shiva Nagendra
  • Mukesh Khare
Original paper


This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models’ formulation. Three scenarios were considered—considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data.


Urban air quality Vehicular pollution Neural network Back-propagation Model performance 



Authors are thankful to the Central Pollution Control Board, Indian Meteorological Department and Central Road Research Institute, New Delhi for providing the necessary data in carrying out this study.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology DelhiHauz KhasIndia

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