SHORT-TERM, REAL-TIME PREDICTION OF THE EXTREME AMBIENT CARBON MONOXIDE CONCENTRATIONS DUE TO VEHICULAR EXHAUST EMISSIONS USING TRANSFER FUNCTION-NOISE MODEL.

Abstract The Box–Jenkins transfer function-noise (TFN) models (Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis: Forecasting and Control, 3rd ed. Prentice-Hall, Englewood Cliffs, NJ.) have been used to provide short-term, real-time forecast of the extreme carbon monoxide for an air quality control region (AQCR) comprising a major traffic intersection in the centre of the capital city of Delhi. The time series of the surface wind speed and ambient temperature have been used as “explaining” exogenous variables in the TFN models. When compared with the results of univariate ARIMA model of the endogenous series, the forecast performance is found to improve with the inclusion of the wind speed as input series; however, no significant improvement is observed in the forecast with the inclusion of temperature as input series.