Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia

The insufficient number of ground-based stations for measuring Particulate Matter less than 10µm (PM10), especially in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimates from space over Malaysia using Aerosol Op...

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Main Author:	Kamarul Zaman, Nurul Amalin Fatihah
Format:	Thesis
Language:	English
Published:	2016
Subjects:	G70.212-70.215 Geographic information system
Online Access:	http://eprints.utm.my/id/eprint/78377/1/NurulAmalinFatihahMFGHT2016.pdf
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spelling	my-utm-ep.783772018-08-26T04:56:13Z Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia 2016-09 Kamarul Zaman, Nurul Amalin Fatihah G70.212-70.215 Geographic information system The insufficient number of ground-based stations for measuring Particulate Matter less than 10µm (PM10), especially in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimates from space over Malaysia using Aerosol Optical Depth (AOD550) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer/Advanced Along-Track Scanner Radiometer (MERIS/AATSR) synergy algorithm and meteorological data that include surface temperature, relative humidity and atmospheric stability from 2007-2011. Accuracy of meteorological parameters that have been used in the estimation of PM10 are examined. The estimated relative humidity and surface temperature using satellite data agree well with ground data where coefficient of determination (R2) = 0.78 and 0.49 and Root Mean Square Error (RMSE) = 5.14% and 2.68?C for relative humidity and surface temperature respectively. Multiple Linear Regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models. The models were developed using PM10 data measured at 29 stations over Malaysia. Result of the research reveals that the ANN using MODIS AOD550 provide higher accuracy with R2 = 0.71 and RMSE = 11.61?gm-3 compared to the MLR method where R2 = 0.66 and RMSE = 12.39?gm-3 or models that use MERIS/AATSR AOD data. Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD550 is the most important parameter for PM10 predictions where R2 = 0.59 and RMSE = 13.61?gm-3. However, the inclusion of the meteorological parameters in the MLR increases the accuracy of the PM10 estimations. The significance of the meteorological parameters in prediction of PM10 concentrations is in the order of (i) atmospheric stability, (ii) relative humidity and (iii) surface temperature. The estimated PM10 concentrations are validated against another 16 stations dataset of measured PM10 with the ANN model to result in higher accuracy (R2= 0.58, RMSE = 10.16?gm-3) compared to the MLR technique (R2 = 0.56, RMSE = 10.58?gm-3). The higher accuracy that has been attained in PM10 estimations from space allows (i) to map the PM10 distribution at large spatial and temporal scales and (ii) permits for future estimates of PM2.5 concentrations from space for monitoring of the Environmental Performance Index (EPI). 2016-09 Thesis http://eprints.utm.my/id/eprint/78377/ http://eprints.utm.my/id/eprint/78377/1/NurulAmalinFatihahMFGHT2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95498 masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.212-70.215 Geographic information system
spellingShingle	G70.212-70.215 Geographic information system Kamarul Zaman, Nurul Amalin Fatihah Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
description	The insufficient number of ground-based stations for measuring Particulate Matter less than 10µm (PM10), especially in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimates from space over Malaysia using Aerosol Optical Depth (AOD550) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer/Advanced Along-Track Scanner Radiometer (MERIS/AATSR) synergy algorithm and meteorological data that include surface temperature, relative humidity and atmospheric stability from 2007-2011. Accuracy of meteorological parameters that have been used in the estimation of PM10 are examined. The estimated relative humidity and surface temperature using satellite data agree well with ground data where coefficient of determination (R2) = 0.78 and 0.49 and Root Mean Square Error (RMSE) = 5.14% and 2.68?C for relative humidity and surface temperature respectively. Multiple Linear Regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models. The models were developed using PM10 data measured at 29 stations over Malaysia. Result of the research reveals that the ANN using MODIS AOD550 provide higher accuracy with R2 = 0.71 and RMSE = 11.61?gm-3 compared to the MLR method where R2 = 0.66 and RMSE = 12.39?gm-3 or models that use MERIS/AATSR AOD data. Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD550 is the most important parameter for PM10 predictions where R2 = 0.59 and RMSE = 13.61?gm-3. However, the inclusion of the meteorological parameters in the MLR increases the accuracy of the PM10 estimations. The significance of the meteorological parameters in prediction of PM10 concentrations is in the order of (i) atmospheric stability, (ii) relative humidity and (iii) surface temperature. The estimated PM10 concentrations are validated against another 16 stations dataset of measured PM10 with the ANN model to result in higher accuracy (R2= 0.58, RMSE = 10.16?gm-3) compared to the MLR technique (R2 = 0.56, RMSE = 10.58?gm-3). The higher accuracy that has been attained in PM10 estimations from space allows (i) to map the PM10 distribution at large spatial and temporal scales and (ii) permits for future estimates of PM2.5 concentrations from space for monitoring of the Environmental Performance Index (EPI).
format	Thesis
qualification_level	Master's degree
author	Kamarul Zaman, Nurul Amalin Fatihah
author_facet	Kamarul Zaman, Nurul Amalin Fatihah
author_sort	Kamarul Zaman, Nurul Amalin Fatihah
title	Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
title_short	Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
title_full	Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
title_fullStr	Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
title_full_unstemmed	Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia
title_sort	estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in malaysia
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2016
url	http://eprints.utm.my/id/eprint/78377/1/NurulAmalinFatihahMFGHT2016.pdf
_version_	1747817974365945856

Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia

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