A Review of PM10 Concentrations Modelling in Malaysia

Shaziayani, Wan Nur; Ul-Saufie, Ahmad Zia; Libasin, Zuraira; Shukri, Fuziatul Norsyiha Ahmad; Abdullah, Sharifah Sarimah Syed; Noor, Norazian Mohamed

doi:10.1088/1755-1315/616/1/012008

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Studies dealing with PM estimations in Malaysia are rather limited [18,30]. Shaziayani et al [85] has reviewed PM 10 modelling studies in Malaysia, and only four studies used ML techniques in predicting PM 10 . On the other hand, PM 2.5 studies are even fewer and most of them in Malaysia have been performed at small spatial scales [86,87].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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“…Since air pollution can seriously affect daily life in Far East countries, there are many studies conducted in these regions as well. For example, Nur Shaziayani et al (2020) reviewed previously used methods for PM 10 levels prediction in Malaysia. They stated that %72 of the studies employed statistical methods for the studies conducted in Malaysia.…”

Section: Literaturementioning

confidence: 99%

Application of Statistical Distributions to PM10 Concentrations: Van, Türkiye

Bagci

2023

Yüzüncü Yıl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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Air pollution is one of the most serious environmental pollution problems that adversely affect human health and the environment. Industrializing and frequent use of low-quality coals for heating purposes and incorrect combustion techniques may cause intense air pollution in the winter season. There are several studies on air quality employing different methods using various air pollutants (carbon monoxide, nitrogen oxides, ground-level ozone, and particle pollution) in the related literature. In this study PM_10 (concentration of 10 micrometers or smaller size of air pollutants) levels in Van province which is one of the most crowded provinces in Eastern Anatolia, Türkiye. Due to the fuels used for heating in Van, the air quality may be higher than limits set by regulations several times during the year. In this study, PM_10 levels of Van are modeled using lognormal, Weibull, and Gamma distributions. Information and goodness of fit criteria are used to compare their performance. In addition, predictions of exceedances are provided for the PM_10 concentration higher than given limits. According to the results, the Gamma distribution performed better than the other two distributions in modeling the PM_10 concentrations in Van and predicted the exceedances accurately.

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Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression

Balogun

Tella

2022

Chemosphere

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A Review of PM10 Concentrations Modelling in Malaysia

Cited by 3 publications

References 15 publications

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Application of Statistical Distributions to PM10 Concentrations: Van, Türkiye

Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression

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