State-space modeling for atmospheric pollution

Abstract

A method for forecasting the pollutant concentration is proposed. It is based on discretizing the rectified advection-diffusion (RAD) equation by means of a finite-differences scheme and transforming the resultant numerical algorithm into a state-space form. The state-space model uses an optimum estimator algorithm called the Kalman filter to forecast the air pollutant spatial distribution. The state-space modeling defines two basic equations: system state and measurement equations. With regard to the second aspect, state-space methodology is applied to forecast atmospheric aerosol lead (Pb) concentration including wind speed and wind direction as exogenous variables of the models. -from Authors