Applications of adaptive digital filtering to the data processing for the environmental system

In this paper a least mean-square (LMS) adaptive digital filter (ADF) is used in order to detect the extraordinary levels of air pollution data, to predict the future air pollution levels, and to identify the unknown parameters in the environmental system. The technique used here is based on the recursive adaptive digital filtering method proposed by White, which is an extension of the usual ADF by Widrow. For the Oxdata developed at Sooka, Koshigaya, Kasukabe, and Kawaguchi, Japan, the extraordinary levels of the Oxdata are detected by using the recursive ADF. For the SO2data at Komatsushima, Japan, the predicted values of the SO2levels are obtained by using the ADF as the predictor. Finally, a new identification method is proposed to find the unknown parameters of the AR, MA, and ARMA processes and is applied to identify the environmental system.