Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques

This research involves the analyses of secondary air quality data collected at twelve monitoring stations in Malaysia between 2001 and 2009. Several environmetric techniques were applied on this nine-year daily average database. The environmetric techniques incorporated discriminant analysis (DA) t...

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Main Author:	Dominick, Doreena
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Air quality management - Malaysia Air - Pollution - Malaysia Environmental sciences - Statistical methods
Online Access:	http://psasir.upm.edu.my/id/eprint/41233/1/FPAS%202013%208R.pdf
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id	my-upm-ir.41233
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spelling	my-upm-ir.412332016-02-05T04:24:37Z Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques 2013-06 Dominick, Doreena This research involves the analyses of secondary air quality data collected at twelve monitoring stations in Malaysia between 2001 and 2009. Several environmetric techniques were applied on this nine-year daily average database. The environmetric techniques incorporated discriminant analysis (DA) to investigate the significant discriminating air quality variables, hierarchical agglomerative cluster analysis (HACA) to access the spatial air quality patterns and principal component analysis (PCA) to determine the probable sources of air pollutants. The artificial neural network (ANN) analysis used to determine the air quality model structure as well as the most significant variable that influenced the Air Pollutant Index (API). A combined receptor model (PCA/MLR) was then used is to assess the source apportionment of the significant variables. The DA computed nine significant variables to discriminate the five levels of air quality. HACA grouped the twelve air monitoring stations into three different clusters. The PCA results showed that the probable sources of air pollution within the study areas were combustion of fuels in all modes of transportation, offshore oil installation, agriculture operations, combustion of wood and industrial activities. For the overall air quality spatial assessment, ANN produced the best fit model with high R2 values (0.803 ≤ R2 ≤0.807, p<0.05). It also revealed that more than 80% of the air quality variability is explained by the nine significant variables (CO, O3, PM10, NO2, SO2, temperature,humidity and wind speed). Further, the ANN analysis showed that among the nine significant variables, PM10 was the most important variable that influenced the API value variation. In addition, the combined receptor model (PCA/MLR) showed that in all three clusters, more than 70% of the API values were influenced by ozone, O3 (secondary gas pollutant) and particulate matter with diameter of less than 10 micrometers, PM10 (non-gas air pollutants). The research verifies that environmetric techniques are highly viable and effective for analyzing large amounts of complex data to glean vital knowledge about air quality, especially the behavior characteristics of specific air pollutants and air pollution patterns. This knowledge can be employed as decision tools for policy makers in planning for more effective air quality monitoring programs. Air quality management - Malaysia Air - Pollution - Malaysia Environmental sciences - Statistical methods 2013-06 Thesis http://psasir.upm.edu.my/id/eprint/41233/ http://psasir.upm.edu.my/id/eprint/41233/1/FPAS%202013%208R.pdf application/pdf en public masters Universiti Putra Malaysia Air quality management - Malaysia Air - Pollution - Malaysia Environmental sciences - Statistical methods
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Air quality management - Malaysia Air - Pollution - Malaysia Environmental sciences - Statistical methods
spellingShingle	Air quality management - Malaysia Air - Pollution - Malaysia Environmental sciences - Statistical methods Dominick, Doreena Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
description	This research involves the analyses of secondary air quality data collected at twelve monitoring stations in Malaysia between 2001 and 2009. Several environmetric techniques were applied on this nine-year daily average database. The environmetric techniques incorporated discriminant analysis (DA) to investigate the significant discriminating air quality variables, hierarchical agglomerative cluster analysis (HACA) to access the spatial air quality patterns and principal component analysis (PCA) to determine the probable sources of air pollutants. The artificial neural network (ANN) analysis used to determine the air quality model structure as well as the most significant variable that influenced the Air Pollutant Index (API). A combined receptor model (PCA/MLR) was then used is to assess the source apportionment of the significant variables. The DA computed nine significant variables to discriminate the five levels of air quality. HACA grouped the twelve air monitoring stations into three different clusters. The PCA results showed that the probable sources of air pollution within the study areas were combustion of fuels in all modes of transportation, offshore oil installation, agriculture operations, combustion of wood and industrial activities. For the overall air quality spatial assessment, ANN produced the best fit model with high R2 values (0.803 ≤ R2 ≤0.807, p<0.05). It also revealed that more than 80% of the air quality variability is explained by the nine significant variables (CO, O3, PM10, NO2, SO2, temperature,humidity and wind speed). Further, the ANN analysis showed that among the nine significant variables, PM10 was the most important variable that influenced the API value variation. In addition, the combined receptor model (PCA/MLR) showed that in all three clusters, more than 70% of the API values were influenced by ozone, O3 (secondary gas pollutant) and particulate matter with diameter of less than 10 micrometers, PM10 (non-gas air pollutants). The research verifies that environmetric techniques are highly viable and effective for analyzing large amounts of complex data to glean vital knowledge about air quality, especially the behavior characteristics of specific air pollutants and air pollution patterns. This knowledge can be employed as decision tools for policy makers in planning for more effective air quality monitoring programs.
format	Thesis
qualification_level	Master's degree
author	Dominick, Doreena
author_facet	Dominick, Doreena
author_sort	Dominick, Doreena
title	Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
title_short	Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
title_full	Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
title_fullStr	Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
title_full_unstemmed	Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques
title_sort	spatial distribution and source apportionment of air pollution in malaysia through environmetric techniques
granting_institution	Universiti Putra Malaysia
publishDate	2013
url	http://psasir.upm.edu.my/id/eprint/41233/1/FPAS%202013%208R.pdf
_version_	1747811860423376896

Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques

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