Aerosol Optical Thickness (AOT) Assessment Using GIS & Remote Sensing

Authors

  • Shuvra Sikder Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Bangladesh, Author
  • M Abrar Rubaiyat Islam Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Bangladesh, Author

Keywords:

Aerosol Optical Thickness (AOT), Geographic Information System (GIS), Remote Sensing, , Landsat, Image processing

Abstract

Atmospheric aerosol particles are one of the  significant agents of air quality degradation. MODIS,  GIS and Remote Sensing techniques have made the way  of AOT assessment easiest over historic manual  systems. This paper concerns itself with the AOT  assessment using GIS and Remote Sensing over Dhaka,  Bangladesh in 2017. Required observations for AOT  assessment are taken considering seasonal variability.  Considered three seasons for this research are winter  (January, February and December), Pre-monsoon  (March and April) and Post-monsoon (October and  November). Monsoon variations are not considered to  avoid excessive cloud correction. The multispectral  algorithm model is used to detect AOT considering the  surface condition homogenous. Basically, Landsat 8+  OLI images are used for AOT assessment and NASA  Earth Observation (NEO) data are used for data  validation. It is found that the winter season has the  highest concentration of AOT compared to pre and post  monsoon. This is due to the meteorological factors like  cloud, rainfall, humidity, wind pressure and speed,  temperature etc.  

Downloads

Download data is not yet available.

References

Y.J. Kaufman, D. Tanre, and O. Boucher. “A satellite view of aerosols in the climate system.” Nature, vol. 419, Sep. 2002, pp. 215 – 223.

N. Luo, M. S. Wong,, W. Yan, and F. Xiao, “Improved aerosol retrieval algorithm using Landsat images and its application for PM10 monitoring over urban areas.” Atmospheric Research. vol. 153, Feb. 2015, pp. 264 – 275.

B. N. Holben, T. F. Eckt, I. Slutskertt, D. Tanress, J. P. Buis, A. Setzer and E, J. A. Vermote , “AERONET–A federated instrument network and data archive for aerosol characterization.” Remote Sensing Environment, vol. 66 (1), Oct. 1998, pp.1–16.

S. A. Hashim, S. Alsultan, M. Z. Matjafri, K. Abdullah, N. M. Salleh,. “The measurement of aerosol optical thickness in Mina during the Hajj season 1426H.” International Journal of Astronomy, vol. 5(1), 1990, pp.1-6.

N. Othman, M. Z. M. Jafri, and L. H. San, “Estimating Particulate Matter Concentration over Arid Region Using Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia.” Modern Applied Science, vol. 4(11), Oct. 2010, pp. 131-142.

N. H. Nguyen, and V.A. Tran, “Estimation of PM10 from AOT of satellite Landsat 8 image over Hanoi City.” International Symposium on Geoinformtics for Spatial Infrastructure Development in Earth and Allied Sciences, Danang, Vietnam, Nov. 2014.

S. V. Henriksson, A. Laaksonen, V.M. Kerminen, P. Raisanen, H. Jardine, A. M. Sundstrom, G. Leeuw, “Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model.” Atmospheric Chemistry and Physics, vol. 11(15), Aug. 2011, pp. 7975–7990.

B. A. Begum, P. K. Hopke, and A. Markwitz, “Air Pollution by Fine Particulate Matter in Bangladesh.”

Atmospheric Pollution Research, vol. 4 (1), Jan. 2013, pp. 75 – 86.

S. Roychoudhury, (2015, December). Thirteen out of 20 most polluted cities in world are from India. [Online]. Available: https://qz.com/307176/thirteen-of-the-20-most-pollut

ed-cities-in-the-world-are-indian/

M. Khalikuzzaman, S. K. Biswas, S.A. Tarafdar, A. Islam and A. H. Khan, “Trace Element Composition of Airborne Particulate Matter in Urban and Rural Areas of Bangladesh. Dhaka, Bangladesh.”, vol. 27(24), Nov. 2015, pp. 12.

Downloads

Published

2018-07-01

Issue

Vol. 6 No. 4 (2018): International Journal of Innovative Research in Computer Science & Technology (july) 2018

Section

Articles

How to Cite

Aerosol Optical Thickness (AOT) Assessment Using GIS & Remote Sensing . (2018). International Journal of Innovative Research in Computer Science & Technology, 6(4), 69–72. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13418