Publications

Imani, M (2021). Particulate matter (PM2.5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 281, 111888.

Abstract
Most studies about particulate matter (PM) estimation have been done based on satellite-derived optical depth aerosol (AOD) products. But, the use of AOD products having coarse resolution is not possible for PM map generation in small spatial coverage such as local cities. To solve this issue, a PM estimation framework is proposed in this work which accepts the original calibrated radiance of MODIS-Level 1 images as input. There are no intermediate computations for atmospheric reflectance or aerosol thickness calculation. A deep neural network consisting of recurrent layers is proposed to extract the relationship between the grey level values of the satellite image bands and the PM measurements in different days and locations. Two individual networks are trained for PM2.5 and PM(10 )concentrations. The PM2.5 map and PM10 map of Tehran city are generated. The performance of the proposed method is compared with several recently published air pollution studies. The results show that the proposed method is a simple, low cost and efficient approach for PM generation of small-scaled coverage using free available Moderate Resolution Imaging Spectroradiometer (MODIS) images.

DOI:
10.1016/j.jenvman.2020.111888

ISSN:
0301-4797