Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering

Abstract

It is essential to maintain air-quality standards and to take necessary measures when air-pollutant concentrations exceed permissible limits. Pollutants such as ground-level ozone ( $\text{O}_3$ ), nitrogen oxides ( $\text{NO}_X$ ), and volatile organic compounds (VOCs) emitted from various sources can be estimated at a particular location through integration of observation data obtained from measurement sites and effective air-quality models, using emission inventory data as input. However, there are always uncertainties associated with the emission inventory data as well as uncertainties generated by a meteorological model. This paper addresses the problem of improving the inverse air pollution emission and prediction over the urban and suburban areas using the air-pollution model with chemical transport model (TAPM-CTM) coupled with the extended fractional Kalman filter (EFKF) based on a Matern covariance function. Here, nitrogen oxide (NO), nitrogen dioxide ( $\text{NO}_2$ ), and $\text{O}_3$ concentrations are predicted by TAPM-CTM in the airshed of Sydney and surrounding areas. For improvement of the emission inventory, and hence the air-quality prediction, the fractional order of the EFKF is tuned using a genetic algorithm (GA). The proposed methodology is verified with measurements at monitoring stations and is then applied to obtain a better spatial distribution of $\text{O}_3$ over the region.