METODE ESTIMASI PENYEBARAN POLUTAN DI UDARA

Abstract

Air pollution is the real problem in the metropolitan city and industrial area. Estimation of air pollution distribution is important for recommending emission minimization. Three estimation methods for air pollution distribution, namely numerical method (Euler and Runge-Kutta method), Recursive Least Square method and data assimilation (Kalman Filter) method were applied in this research. The algorithms and the simulations were described, the accuracy of each method was not compared, but the advantages and disadvantages of these methods were described. Distribution of carbon monoxide in Surabaya was estimated using these methods.  This research showed that numerical method could not be applied in real condition. The RLS method needed a lot of time series data of concentration of pollution. The data assimilation method could be applied in real condition with a few time series pollutant data, and for estimating pollutant concentrations in some locations.