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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/771

Title: Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models
Authors: Sharma, Prateek
Khare, Mukesh
Keywords: Box-Jenkins models
Linear stochastic models
Extreme values
Time-series analysis
Real-time forecasting
Issue Date: 2000
Citation: Transportation Research Part D, 5(1), 59-69
Abstract: Historical data of the time-series of carbon monoxide (CO) concentration was analysed using Box-Jenkins modelling approach. Univariate Linear Stochastic Models (ULSMs) were developed to examine the degree of prediction possible for situations where only a limited data set, restricted only to the past record of pollutant data are available. The developed models can be used to provide short-term, real-time forecast of extreme CO concentrations for an Air Quality Control Region (AQCR), comprising a major traffic intersection in a Central Business District of Delhi City, India.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/771
Appears in Collections:Civil Engineering

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