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

TL;DR: 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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Summary

  • 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.
  • Air pollutant index; HACA; DA; PCA/FA; ANN; MLR, also known as Keyword.

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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
(20052007) 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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Journal ArticleDOI
TL;DR: In this article, the authors established the definition of air pollution, the motivation to study it, and its impacts and sources of pollution and climate change in Malaysia, and discussed the air quality monitoring system in Malaysia and compared Malaysian ambient air quality standards with global standards.
Abstract: Air pollution is strongly tied to climate change. Industrialization and fossil fuel combustion are the main contributors leading to climate change, also being significant sources of air pollution. Malaysia is a developing country with a focus on industrialization. The preference of using private cars is a common practice in Malaysia, resulting in the after-effects of haze and transboundary air pollution. Hence, air pollution has become a severe issue in Malaysia in recent times. Exposure to air pollutants such as ozone and airborne particles is associated with increases in hospital admissions and mortality. For the past few years, the focus of the research is moving towards air quality and the impacts of air pollution on health in Malaysia. In this study, we establish the definition of air pollution, the motivation to study it, and its impacts and sources of air pollution and climate change. We discuss the air quality monitoring system in Malaysia and compare Malaysian ambient air quality standards with global standards. We also look comprehensively on the health impacts of air pollution globally and in the Malaysian context. We discuss where the health impact studies in Malaysia are lacking and what are the gaps in the research. The role of the Malaysian government concerning air pollution and its impacts is discussed. Lastly, we look into the future work and research opportunities with a focus on engineering, estimation, predictive models and lack of research projects.

67 citations


Cites background or methods from "Spatial Analysis of the Air Polluta..."

  • ...…et al. 2014a) - CH4, NmHC, T HC, O3 and PM10 are the most significant 2005–2011 - PCA-ANN showed predictive ability with limited parameters 2014 (Azid et al. 2014b) - Artificial neural network provided better API prediction than multiple linear regression 2005–2007 2014 (Ahamad et al. 2014) -…...

    [...]

  • ...There has been some work in air quality prediction in Malaysia (Azid et al. 2014a; Azid et al. 2013), but it can be extended, and the accuracy can be improved upon as it has globally (Cabaneros et al. 2019; Ma et al. 2019)....

    [...]

  • ...…contribute to diurnal variations of major air pollutants Table 5 (continued) Year Key result(s) and recommendation(s) Dataset DOE data 2014 (Azid et al. 2014a) - CH4, NmHC, T HC, O3 and PM10 are the most significant 2005–2011 - PCA-ANN showed predictive ability with limited…...

    [...]

Journal ArticleDOI
TL;DR: In this article, a panoramic overview of the Malaysian energy sector, the energy policy revolution and the power sector expansion strategy towards secure sustainability is presented, with the aim of stimulating further discussion and research on the environmental ramifications of the plan.
Abstract: Sustainable energy supply is essential for actualizing Malaysia׳s vision to become a high-income country. The current power production and demand trends show that Malaysia has a reserve margin that will only last for the next few years. This calls for further investment, research and development in the country׳s power sector in order to meet the ever increasing energy demand. The government׳s diversification policy and power sector expansion plan emphasizes on the incorporation of renewable energy sources (RESs) and other less CO2 emitting sources like nuclear into the national energy mix. However, the environmental ramifications of this policy should be part of any future expansion plan of national grid. This paper presents a panoramic overview of the Malaysian energy sector, the energy policy revolution and the power sector expansion strategy towards secure sustainability. We want to bring into focus the benefits and challenges of Malaysia׳s power sector expansion plan with the aim of stimulating further discussion and research on the environmental ramifications of the plan.

54 citations

Journal ArticleDOI
TL;DR: In this paper, the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA), and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition were applied.
Abstract: This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.

32 citations


Cites background or methods from "Spatial Analysis of the Air Polluta..."

  • ...Most of the pollutants in the MPS region are originated from burning of biomass and fossil fuels, particularly from industrial, residential and vegetation areas, motor vehicles, and natural emission sources (Mutalib et al., 2013; Azid et al., 2014b)....

    [...]

  • ...The index is important in evaluating the air quality of different sources (Azid et al., 2014a)....

    [...]

  • ...The status of air quality in Malaysia is monitored by the establishment of Recommended Malaysian Air Quality Guideline (RMAQG) issued by the Malaysian Department of Environment (DOE) since 1989 (Dominick et al., 2012; Mutalib et al., 2013; Azid et al., 2014a)....

    [...]

  • ...…0.75 (> 0.75) is considered as “strong”, the values range from 0.50–0.75 (0.50 ≥ factor loading ≥ 0.75) is considered as “moderate”, and the values range from 0.30–0.49 (0.30 ≥ factor loading ≥ 0.49) is considered as “weak” factor loadings (Liu et al., 2003; Azid et al., 2014a; Azid et al., 2015)....

    [...]

  • ...Most of the air pollution sources derived from land transportation (mobile source), industrial emissions (stationary source), and open burning sources (Afroz et al., 2003; Azmi et al., 2010; Abdullah et al., 2012; Azid et al., 2013, 2014a, b)....

    [...]

Journal ArticleDOI
TL;DR: This study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data.
Abstract: An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

17 citations

Journal ArticleDOI
01 Oct 2019-Heliyon
TL;DR: A comprehensive review of relevant scientific journals concerning on the major environmental issues in Malaysia, published between 2013 and 2017, suggested that chemometrics techniques have a greater accuracy, flexibility and efficiency to be applied in environmental modelling.

13 citations


Cites background or methods from "Spatial Analysis of the Air Polluta..."

  • ...…with others studies that concluded the chemometrics technique could be a simple and efficient alternative model to provide reliable estimation of API not only with limited information but with a large and complex database (Azid et al., 2013, 2014b; Mutalib et al., 2013; Amran et al., 2015)....

    [...]

  • ...The efficacy of these studies had supported the previous study by Azid et al. (2014b) for determining the identification and apportionment of pollution sources from a complex air quality database....

    [...]

  • ...…scopes including air quality, air pollution, water quality, water pollution, flood pattern, land use changes, sedimentation and erosion (Azid et al., 2014a, 2015a; Saudi et al., 2014a, 2015a, 2017; Kamaruddin et al., 2015; Isiyaka and Juahir, 2015; Ismail et al., 2016; Rwoo et al.,…...

    [...]

  • ...A study was conducted in Malaysia, which focused on the prediction of the air pollution level using PCA and ANN techniques (Table 2) (Azid et al., 2014a)....

    [...]

  • ...Azid et al. (2014b) proposed that the chemometric techniques and modelling is an excellent tool in API assessment as it has better predictive ability in determination of API with fewer sampling parameters and stations thus can be setbacks in designing a novelty air quality monitoring network…...

    [...]

References
More filters
Journal ArticleDOI
TL;DR: In this article, Tropospheric ozone (O 3 ) in the Indian sub-continent from Afghanistan in the west ( 60 ∘ E ) to parts of Southeast Asian countries in the east ( 105 ∘E ) and parts of China in the north ( 45 ∘ N ) to Sri Lanka in the south ( 0 ∘ n ) is simulated with an episodic chemical transport model christened HANK for the spring and summer months (February-May 2000).

85 citations


"Spatial Analysis of the Air Polluta..." refers background in this paper

  • ...869), which originated from burning of biomass and fossil fuels as well as from motor vehicles and natural emission sources (Mittal et al. 2007; Mutalib et al. 2013)....

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Journal ArticleDOI
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.

60 citations

Journal ArticleDOI
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.

56 citations

Journal ArticleDOI
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.
Abstract: The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents 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.

50 citations


"Spatial Analysis of the Air Polluta..." refers background in this paper

  • ...956), which resulted from the motor vehicles combustions, coal powered power plants and uncontrolled burning of forests, volcano activities and dust storm from neighbouring country due to various activities (Mutalib et al. 2013)....

    [...]

  • ...869), which originated from burning of biomass and fossil fuels as well as from motor vehicles and natural emission sources (Mittal et al. 2007; Mutalib et al. 2013)....

    [...]

  • ...Air pollution is becoming a major environmental issue in the southern region of Peninsular Malaysia due to the increasing number of transportations (mobile sources), trans-boundary pollution from neighbouring countries and the industrial activities (stationary sources), and they are the main sources of air pollution in Malaysia (Mutalib et al. 2013)....

    [...]

Proceedings ArticleDOI
27 Jun 2006
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.
Abstract: Air Quality Index (AQI) system lays an important role in conveying to both decision-makers and the general public the status of ambient air quality, ranging from good to hazardous. Five types of air pollutants will be studied which consists of ozone (O 3 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ) and suspended particulate matter less than 10 micron in size (PM 10 ). The objective of this paper were to investigate 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. The measurement activities are carried at Jalan Tasek in Perak, Nilai in Negeri Sembilan and Jerantut in Pahang. The data collected comprises of data for the previous two months, beginning from November 2004. The ambient air quality plays an important role in evaluating the air quality. The artificial neural network simplifies and speeds up the computation of the ambient air quality, as compared to the currently existing method. For this purposes, neural network model provides an interesting alternative to air quality monitoring. The comparison between data from model predictions and actual observations is coherent which shows that promising result based on the developed ANN model in predicting Ambient Air Quality (AAQ) is effective and accurate.

27 citations

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Frequently Asked Questions (1)
Q1. What are the contributions in "Spatial analysis of the air pollutant index in the southern region of peninsular malaysia using environmetric techniques" ?

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.