Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

Abstract

Spatial and temporal distribution of PM10 is modeled by Bayesian Maximum Entropy (BME) method. It is the spatiotemporal estimation method which combines exact measurements with the secondary information by considering local uncertainties. In this study, daily average PM10 data are used to generate spatial and temporal PM10 maps. Both annual and seasonal estimations have been realized.  This is the first study which concentrates on spatiotemporal distribution of PM10 for all regions of Turkey by using Bayesian Maximum Entropy method. Error variances are used as performance criteria in both seasonal and annual predictions. All prediction results stay within the limits of the confidence intervals. In addition, unknown PM10 values are estimated, including PM10 values over the seas. It is thought that the PM10 maps which show all regions of Turkey in detail are quite invaluable and informative. 

Keywords

• Air pollution,Bayesian Maximum Entropy (BME),PM10,Prediction,Spatiotemporal mapping

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