Air Pollution Prediction Using Matern Function Based Extended Fractional Kalman Filtering

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
The 13th International Conference on Control, Automation, Robotics and Vision, 2014, pp. 758 - 763
Issue Date:
2014-12-10
Full metadata record
Files in This Item:
Filename Description Size
ThumbnailTh22.5-P0372.pdf Published version954.79 kB
Adobe PDF
It is essential to maintain air quality standards and inform people when air pollutant concentrations exceed permissible limits. For example, ground-level ozone, a harmful gas formed by NOX and VOCs emitted from various sources, can be estimated through integration of observation data obtained from measurement sites and effective air-quality models. This paper addresses the problem of predicting air pollution emissions over 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 Mat´ern covariance function. Here, the ozone concentration is predicted in the airshed of Sydney and surrounding areas, where the length scale parameter l is calculated using station coordinates. For improvement of the air quality prediction, the fractional order of the EFKF is tuned by using a Genetic Algorithm (GA). The proposed methodology is validated at monitoring stations and applied to obtain a spatial distribution of ozone over the region.
Please use this identifier to cite or link to this item: