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

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dc.contributor.authorSharma, Prateek-
dc.contributor.authorKhare, Mukesh-
dc.identifier.citationTransportation Research Part D, 5(1), 59-69en
dc.description.abstractHistorical 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.en
dc.format.extent275524 bytes-
dc.subjectBox-Jenkins modelsen
dc.subjectLinear stochastic modelsen
dc.subjectExtreme valuesen
dc.subjectTime-series analysisen
dc.subjectReal-time forecastingen
dc.titleReal-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic modelsen
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