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Spatial Analysis of the Air Pollutant Index in the Southern Region of Peninsular Malaysia Using Environmetric Techniques

Azman Azid, +7 more
- pp 307-312
TLDR
In this paper, environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models.
Abstract: 
Air pollution is becoming a major environmental issue in the southern region of Peninsular Malaysia. Environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years (2005–2007) were applied in this study. HACA clustered three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO, and NO2) gave the most significant variables after stepwise backward mode. PCA/FA identify that the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study concluded that the environmetric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment, and interpretation of complex dataset with a view to get better information about the air quality, and can be setbacks in designing an API monitoring network for effective air pollution resources management.

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Spatial analysis of the air pollutant index in the southern region of Peninsular Malaysia
using environmetric techniques
ABSTRACT
Air pollution is becoming a major environmental issue in the southern region of Peninsular
Malaysia. Environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the
spatial variations in the southern region of Peninsular Malaysia, followed by API prediction
comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years
(2005–2007) were applied in this study. HACA clustered three different groups of similarity
based on the characteristics of air quality parameters. DA shows all seven parameters (CO,
O3, PM10, SO2, NOx, NO, and NO2) gave the most significant variables after stepwise
backward mode. PCA/FA identify that the major source of air pollution is due to combustion
of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better
prediction compared to the MLR model with R2 values equal to 0.819 and 0.773
respectively. This study concluded that the environmetric techniques and modelling become
an excellent tool in API assessment, air pollution source identification, apportionment, and
interpretation of complex dataset with a view to get better information about the air quality,
and can be setbacks in designing an API monitoring network for effective air pollution
resources management.
Keyword: Air pollutant index; HACA; DA; PCA/FA; ANN; MLR
Citations
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References
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TL;DR: Artificial Neural Networks are used for modelling ozone, and for simulating its behaviour in relation to other atmospheric parameters of interest, for the city of Thessaloniki, Greece, and their results suggest the operational capabilities and research potential in the application of computational intelligence methods for the environmental sector.
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Tropospheric sources of NOx: lightning and biology

Joel S. Levine, +3 more
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TL;DR: These laboratory experiments, as well as other studies, suggest that the global production of NOx by lightning probably ranges between 2 and 20 MT(N)y-1 of NO and is strongly dependent on the total energy deposited by lightning, a quantity not well-known.
Journal ArticleDOI

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

Sharifah Norsukhairin Syed Abdul Mutalib, +7 more
- 21 Aug 2013 - 
TL;DR: The necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations are presented.
Proceedings ArticleDOI

Prediction of Ambient Air Quality Based on Neural Network Technique

Mahanijah Md Kamal, +2 more
TL;DR: In this paper, the authors investigated the effectiveness of Artificial Neural Network (ANN) model with back propagation neural network (BPNN) for predicting the ambient air quality for air quality monitoring in states of Malaysia.
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Environmetric techniques ( HACA, DA, and PCA/FA ) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years ( 2005–2007 ) were applied in this study. This study concluded that the environmetric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment, and interpretation of complex dataset with a view to get better information about the air quality, and can be setbacks in designing an API monitoring network for effective air pollution resources management.