Concentration Estimation of Air Pollutants (PM2.5 and PM10) Using MODIS Satellite Data, Deep Neural Network and Random Forest

Document Type : Original Article

Author

Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

While many studies estimate air pollutants such as particulate matter (PM), PM2.5 and PM10, have used aerosol optical depth (AOD) products from satellite sensors, utilizing these products for mapping pollution in smaller cities like Tehran is not effective due to their coarse resolution. To address this problem, this study directly uses the Level 1 products of MODIS (instead of aerosol and AOD products). The proposed method employs a deep neural network and a random forest model to estimate the PM values using data from the first two bands of MODIS. The results show the superior performance of the proposed models compared to some state-of-the-art PM estimation methods in recent years. The outcome of this research is the development of a PM map generation software for Tehran (mapping PM2.5 and PM10 concentrations) using freely available MODIS images.

Keywords


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