PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia

Remote sensing and GIS have been increasingly used for air pollution monitoring in past decade. In this study the distribution of PM10 were measured at eight air quality monitoring stations in Klang Valley. The attempt was carried out in GIS environment. The data are belonging to the beginning of th...

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Main Authors: Amanollahi, Jamil, Abdullah, Ahmad Makmom, Farzanmanesh, Raheleh, Ramli, Mohammad Firuz, Pirasteh, Saeid
Format: Article
Published: Thai Society of Higher Education Institutes on Environment 2011
Online Access:http://psasir.upm.edu.my/id/eprint/44377/
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author Amanollahi, Jamil
Abdullah, Ahmad Makmom
Farzanmanesh, Raheleh
Ramli, Mohammad Firuz
Pirasteh, Saeid
author_facet Amanollahi, Jamil
Abdullah, Ahmad Makmom
Farzanmanesh, Raheleh
Ramli, Mohammad Firuz
Pirasteh, Saeid
author_sort Amanollahi, Jamil
building UPM Institutional Repository
collection Online Access
description Remote sensing and GIS have been increasingly used for air pollution monitoring in past decade. In this study the distribution of PM10 were measured at eight air quality monitoring stations in Klang Valley. The attempt was carried out in GIS environment. The data are belonging to the beginning of the week –Monday- and weekend –Saturday-. Aerosol optical thickness (AOT) values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) were interpolated in GIS for comparison with ground station PM10 data. The validation between AOT and amount of PM10 in the atmosphere were analyzed using non-linear correlation coefficient (NLCC) for 2004. Results showed that the amount of PM10 at the beginning of the week is higher than the weekend. Remote sensing data showed better distribution of PM10 than ground station data. The NLCC results had a range from (0.10) at Petaling Jaya to (0.61) at Shah Alam. This study shows that GIS is useful tool to generate distribution map of PM10. This study shows that MODIS AOT data are able to present the amount of PM10 over large spatial scales that there is no ground stations air quality monitoring.
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institution Universiti Putra Malaysia
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last_indexed 2025-11-15T10:06:16Z
publishDate 2011
publisher Thai Society of Higher Education Institutes on Environment
recordtype eprints
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spelling upm-443772023-12-24T17:18:59Z http://psasir.upm.edu.my/id/eprint/44377/ PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia Amanollahi, Jamil Abdullah, Ahmad Makmom Farzanmanesh, Raheleh Ramli, Mohammad Firuz Pirasteh, Saeid Remote sensing and GIS have been increasingly used for air pollution monitoring in past decade. In this study the distribution of PM10 were measured at eight air quality monitoring stations in Klang Valley. The attempt was carried out in GIS environment. The data are belonging to the beginning of the week –Monday- and weekend –Saturday-. Aerosol optical thickness (AOT) values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) were interpolated in GIS for comparison with ground station PM10 data. The validation between AOT and amount of PM10 in the atmosphere were analyzed using non-linear correlation coefficient (NLCC) for 2004. Results showed that the amount of PM10 at the beginning of the week is higher than the weekend. Remote sensing data showed better distribution of PM10 than ground station data. The NLCC results had a range from (0.10) at Petaling Jaya to (0.61) at Shah Alam. This study shows that GIS is useful tool to generate distribution map of PM10. This study shows that MODIS AOT data are able to present the amount of PM10 over large spatial scales that there is no ground stations air quality monitoring. Thai Society of Higher Education Institutes on Environment 2011 Article PeerReviewed Amanollahi, Jamil and Abdullah, Ahmad Makmom and Farzanmanesh, Raheleh and Ramli, Mohammad Firuz and Pirasteh, Saeid (2011) PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia. EnvironmentAsia, 4 (1). pp. 47-52. ISSN 1906-1714 https://tshe.org/ea/ea_jan2011.html 10.14456/ea.2011.7
spellingShingle Amanollahi, Jamil
Abdullah, Ahmad Makmom
Farzanmanesh, Raheleh
Ramli, Mohammad Firuz
Pirasteh, Saeid
PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title_full PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title_fullStr PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title_full_unstemmed PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title_short PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia
title_sort pm10 distribution using remotely sensed data and gis techniques; klang valley, malaysia
url http://psasir.upm.edu.my/id/eprint/44377/
http://psasir.upm.edu.my/id/eprint/44377/
http://psasir.upm.edu.my/id/eprint/44377/