Modelling and Simulation of Ambient Carbon Monoxide
Sudhir Nigam1, Rashmi Nigam2, Sangeeta Kapoor3
1Sudhir Nigam, Department of Civil Engineering, LNCTS, Raisen Road, Bhopal (M P), India.
2Rashmi Nigam, Department of Mathematics, UIT,Rajiv Gandhi Technical University, Bhopal, (M.P.) India.
3Sangeeta Kapoor, Department of Engg Physics LNCTS, Raisen Road Bhopal, (M P), India.
Manuscript received on November 27, 2013. | Revised Manuscript received on December 13, 2013. | Manuscript published on December 30, 2013. | PP: 403-409 | Volume-3, Issue-2, December 2013. | Retrieval Number: B2499123213/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Air pollution affects both the health and environment of living organisms. In large urban cities the emissions of carbon monoxide (CO) gas from the transport sector pose unprecedented risks being a silent and lethal killer. In order to eradicate the adverse impact of CO pollution, there exists a need for an early warning system, which may be of immense help to manage and regulate ambient CO concentrations. CO emission and its dispersion is a non-linear problem which can be vividly expressed using artificial neural network (ANN) computations. In this paper an attempt is made to simulate concentration of CO gas based on historical data using ANN. Eleven years (1996-2006), morning time (06.00hrs-14.00hrs) CO emission data from ITO square of Delhi has been employed for modelling and simulation. The ANN are regarded as an efficient and optimised architectures for capturing the inherited codes of processes and technique for estimation as compare traditional statistical techniques. The modelling result shows comparable matching with the measured ambient values of CO.
Keywords: Simulation, Modelling, Concentration, Artificial Neural Network (ANN), Real time analysis.