Urban air pollution forecast based on the Gaussian and regression models

摘要

The results of the application of the Gaussian and regression model based on short term urban air pollution forecast are presented and discussed. The statistical data analysis was based upon hourly measurements taken over three months of the meteorological parameters and CO concentrations in the VIlnius city, Lithuania. From these data using regression analysis, a statistical model was developed to forecast hourly CO concentrations for 9 hour periods using meteorological parameters and the maximum CO concentration value of the previous air pollution peak. As an alternative forecast method the Gaussian model was used to calculate hourly distribution of COf concentrations using the meteorological data and dynamic emission database. It has been established that short time forecast based on statistically determined relationships is in better consistency with the measured data at a specific point than using the Gausian model. It is suggested to use routine TAF (Terminal Aerodrome Forecast) to calculate air pollution forecast needed meteorological parameters.