Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Rahman, Ezahtulsyahreen Ab.; Hamzah, Firdaus Mohamad; Latif, Mohd Talib; Dominick, Doreena

doi:10.4209/aaqr.210161

Cited by 15 publications

(14 citation statements)

References 41 publications

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“…Moreover, air pollution trends in Malaysia are predominantly in uenced by PM 2.5 and PM 10 (DOE, 2018-2021; Sentian et al, 2019; Ab. Rahman et al, 2022), consistent with the ndings of this study. Therefore, this study highly suggests that model ANN-PC2019 is most suitable for forecasting API.…”

Section: Forecasting Air Quality Using Annsupporting

confidence: 93%

“…According toRahman et al (2015), PM 10 and PM 2.5 stem from motor vehicles, factories, power generators, construction sites, quarries, and incinerators, collectively contributing to atmospheric pollutant level. Furthermore, Malaysia encountered a haze event characterized by the highest recorded intensity of PM 2.5 , attributable to both open burning and haze from bordering countries such as Sumatera and Kalimantan, Indonesia(Latif et al, 2018;Liyana Zakri et al, 2018;Ab. Rahman et al, 2022).…”

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confidence: 99%

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Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

SHAFI'I,

JUAHIR

2024

Preprint

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Air pollution poses significant risks to human health, the environment, and the economy. Therefore, striving for cleaner air through efficient air quality monitoring is imperative for fostering a healthier and more sustainable future. Predicting air quality is essential to enhance the quality of life, maintain environmental sustainability, and reduce the economic burden associated with poor air quality issues. The artificial neural network (ANN) is widely recognized as a predominant computational tool in air quality studies due to its capabilities in predicting gaseous and particulate pollutant concentrations, as well as forecasting the air pollutant index (API). This study aimed to investigate the predictive performance of ANN in determining the API by utilizing identified potential sources of air pollutants. Five prediction models were created, namely ANN-PC2018, ANN-PC2019, ANN-PC2020, ANN-PC2021, and ANN-PC2022. Principal component analysis (PCA) was conducted to identify the most significant sources of air pollution, and the results were employed to predict the API using ANN. The ANN-PC2019 model exhibited the highest performance with an R2 value of 0.8612 and RMSE of 7.7467, utilizing four major pollutants as input variables. These findings suggest that forecasting air quality using fewer parameters yields reliable outcomes.

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Section: Forecasting Air Quality Using Annsupporting

confidence: 93%

mentioning

confidence: 99%

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

SHAFI'I,

JUAHIR

2024

Preprint

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“…44 Urban economic activity increases air pollution. 45 Consequently, a significant number of local monitoring stations have been installed in urban areas to regulate air quality levels and combat air pollution, and rural areas received less attention. Thus, the uneven, non-random Open access distribution of monitoring stations between urban and rural regions will influence the spatial distribution of pollutants.…”

Section: Basic Characteristicsmentioning

confidence: 99%

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

Karim,

Hod,

Wahab

et al. 2024

BMJ Open

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ObjectivesClimate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air pollution under different climate change scenarios.DesignThis systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow checklist. A population-exposure-outcome framework was established. Population referred to the general global population of all ages, the exposure of interest was air pollution and its projection, and the outcome was the occurrence of NCDs attributable to air pollution and burden of disease (BoD) based on the health indices of mortality, morbidity, disability-adjusted life years, years of life lost and years lived with disability.Data sourcesThe Web of Science, Ovid MEDLINE and EBSCOhost databases were searched for articles published from 2005 to 2023.Eligibility criteria for selecting studiesThe eligible articles were evaluated using the modified scale of a checklist for assessing the quality of ecological studies.Data extraction and synthesisTwo reviewers searched, screened and selected the included studies independently using standardised methods. The risk of bias was assessed using the modified scale of a checklist for ecological studies. The results were summarised based on the projection of the BoD of NCDs attributable to air pollution.ResultsThis review included 11 studies from various countries. Most studies specifically investigated various air pollutants, specifically particulate matter <2.5 µm (PM2.5), nitrogen oxides and ozone. The studies used coupled-air quality and climate modelling approaches, and mainly projected health effects using the concentration–response function model. The NCDs attributable to air pollution included cardiovascular disease (CVD), respiratory disease, stroke, ischaemic heart disease, coronary heart disease and lower respiratory infections. Notably, the BoD of NCDs attributable to air pollution was projected to decrease in a scenario that promotes reduced air pollution, carbon emissions and land use and sustainable socioeconomics. Contrastingly, the BoD of NCDs was projected to increase in a scenario involving increasing population numbers, social deprivation and an ageing population.ConclusionThe included studies widely reported increased premature mortality, CVD and respiratory disease attributable to PM2.5. Future NCD projection studies should consider emission and population changes in projecting the BoD of NCDs attributable to air pollution in the climate change era.PROSPERO registration numberCRD42023435288.

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“…The ability to measure the homogeneity of risk using Ward's method and Euclidean distance (Ab. Rahman et al, 2022) is the reason to employ this approach. The result of the analysis is best displayed in the dendrogram, as Lau et al, 2009, this diagram illustrates the degree of similarity for the spatial classification perfectly.…”

Section: Hierarchical Agglomerative Cluster Analysis (Haca)mentioning

confidence: 99%

Untitled

2024

Int Internal Med J

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The research investigated the spatial distribution of air quality in Peninsular Malaysia to offer crucial insights for mitigating air quality degradation. Utilizing historical data obtained from the Malaysian Department of Environment, eleven years ' worth (2011-2021) of daily readings of PM 10 , O 3 , SO 2 , NO 2 , and CO from thirty-four monitoring stations were subjected to environmetric techniques and Artificial Neural Network analysis. HACA successfully classified seven stations into the High Pollution Cluster (HPC), seven stations into the Moderate Pollution Cluster (MPC), and twenty stations into the Low Pollution Cluster (LPC).The discriminant analysis demonstrated a correct assignment rate of 91.87%, suggesting that all five parameters were able to discriminate with a significance level of p<0.0001. Principal Component Analysis (PCA) revealed two Variance Factors (VFs) across all clusters, with cumulative variances of 69.43% (HPC), 82.32% (MPC), and 62.16% (LPC) respectively. Furthermore, employing a Multi-Layer Perceptron Feed-Forward Artificial Neural Network (MLP-FF-ANN) model to predict API readings yielded a significant and strong correlation, as indicated by R 2 =0.7774 and RMSE=9.9048. These findings hold significant potential in informing the adoption of effective preventative and management strategies.

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Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Cited by 15 publications

References 41 publications

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

Forecasting Air Quality in Peninsular Malaysia: Unveiling the Power of Artificial Neural Networks

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

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