Air pollution prediction using Matérn function based extended fractional Kalman filtering

Abstract

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 NO x 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 Kaiman Filter (EFKF) based on a Matern covariance function. Here, the ozone concentration is predicted in the airshed of Sydney and surrounding areas, where the length scale parameter I 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.