Statistical analysis of PM10 concentrations at different locations in Malaysia

Sansuddin, Nurulilyana; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri Md; Ghazali, Nurul Adyani; Madhoun, Wesam Ahmed Al

doi:10.1007/s10661-010-1806-8

Cited by 42 publications

(26 citation statements)

References 30 publications

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“…Meanwhile, Sansuddin et al, [24] concluded that the best time series model at four different locations, which represented industrial (Nilai and Johor Bharu) and residential (Kota Kinabalu and Kuantan), had been the AR. The seasonal ARIMA was also used to predict the ozone precursor at Brazil [25].…”

Section: Univariate Time Series Modellingmentioning

confidence: 99%

A Review on Short-Term Prediction of Air Pollutant Concentrations

Raffee¹,

Rahmat²,

Hamid³

et al. 2018

IJET

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In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality. The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems. As such, the air pollution modelling appears to be an important tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future. Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series. Besides, this paper also presents comprehensive literature on univariate time series modelling. Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term prediction of air pollutant concentrations.

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Section: Univariate Time Series Modellingmentioning

confidence: 99%

A Review on Short-Term Prediction of Air Pollutant Concentrations

Raffee¹,

Rahmat²,

Hamid³

et al. 2018

IJET

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“…Sharma et al (2013b) have also observed that 24 hour average PM 10 concentrations follow lognormal distribution at three different locations in Delhi city. However, the topographical and geographical characteristics of the site may affect the particle distribution (Taylor et al, 1986;Sansuddin et al, 2011).…”

Section: The Study Location Aph-2mentioning

confidence: 99%

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

Gulia

Nagendra

Khare

2017

Aerosol Air Qual. Res.

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The occurrence of extreme events of air pollutant concentrations at urban hotspots is a routine phenomenon, particularly during the winter season. However, extreme events of reactive air pollutants are more frequent during the summer season. The assessment of air pollution extreme events will provide a platform to formulate an effective and efficient hotspot urban air quality management plan. The statistical distribution model (SDM) is widely used to describe the average as well as extreme air pollutant concentration in a more organized and efficient manner. In the present study, the best fit SDM has been evaluated for hourly average PM 2.5 and NO 2 concentrations at one of the busiest traffic intersections in Delhi city (air pollution hotspot 1: APH-1) and for PM 2.5 at one of the heavily trafficked road corridors in Chennai city (air pollution hotspot 2: APH -2). The SDMs were developed for different seasons to evaluate the impacts of climatic conditions on the air pollution events. Results indicate that NO 2 concentrations were best fitted with lognormal and log logistic distribution models respectively, for winter and summer seasons at APH-1. However, lognormal distribution was best fitted to PM 2.5 concentration of winter and summer seasons at both APHs.

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“…The PDF plot was used to identify the skewness of the distribution and it plot by using the value of parameter estimation [18]. The derivation of PDF was used for prediction of exceedences [19] and the CDF was used to determine the probability of air pollutant concentration [20]. Table 1 shows the PDF and the parameter estimation used [21].…”

Section: Probability Distributionmentioning

confidence: 99%

Fitting Statistical Distributions Functions on Ozone Concentration Data at Coastal Areas

Nasir¹,

Ghazali²,

Mokhtar³

et al. 2016

MJAS

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Ozone is known as one of the pollutant that contributes to the air pollution problem. Therefore, it is important to carry out the study on ozone. The objective of this study is to find the best statistical distribution for ozone concentration. There are three distributions namely Inverse Gaussian, Weibull and Lognormal were chosen to fit one year hourly average ozone concentration data in 2010 at Port Dickson and Port Klang. Maximum likelihood estimation (MLE) method was used to estimate the parameters to develop the probability density function (PDF) graph and cumulative density function (CDF) graph. Three performance indicators (PI) that are normalized absolute error (NAE), prediction accuracy (PA), and coefficient of determination (R 2 ) were used to determine the goodness-of-fit criteria of the distribution. Result shows that Weibull distribution is the best distribution with the smallest error measure value (NAE) at Port Klang and Port Dickson is 0.08 and 0.31, respectively. The best score for highest adequacy measure (PA: 0.99) with the value of R 2 is 0.98 (Port Klang) and 0.99 (Port Dickson). These results provide useful information to local authorities for prediction purpose.Keywords: ozone concentration, coastal area, statistical distributions, goodness-of-fit, performance indicator Abstrak Ozon merupakan salah satu pencemar yang banyak menyumbang kepada masalah pencemaran udara. Maka kajian tentang ozon adalah penting untuk dijalankan. Objektif kajian ini adalah mencari taburan statistik yang terbaik untuk mewakili data kepekatan ozon. Tiga fungsi taburan yang digunakan dalam kajian ini adalah Gaussian songsang, Weibull dan Lognormal telah dipilih bagi menentukan taburan statistik yang terbaik untuk mewakili data taburan ozon per jam pada tahun 2010 di Port Dickson dan Port Klang. Kaedah penganggar kebolehjadian maksimum (MLE) telah digunakan untuk mengira parameter yang membentuk graf fungsi taburan kebarangkalian (PDF) dan fungsi taburan kumulatif (CDF). Tiga penunjuk prestasi (PI) iaitu ralat mutlak dinormalkan (NAE), kejituan ramalan (PA) dan pekali penentuan (R 2 ) telah digunakan untuk menguji prestasi kriteria taburan yang terbaik. Hasil kajian menunjukkan taburan Weibull adalah yang terbaik untuk mewakili data kepekatan ozon dengan nilai ukuran ralat terkecil (NAE) di Port Klang dan Port Dickson masing-masing ialah 0.08 dan 0.031. Skor terbaik juga terhasil untuk pengiraan kejituan tertinggi (PA: 0.99) dengan nilai R 2 di kedua-dua tempat ialah 0.98 (Port Klang) dan 0.99 (Port Dickson). Hasil kajian ini membekalkan maklumat penting kepada penguatkuasa tempatan untuk tujuan ramalan kepekatan pada masa akan datang.

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Statistical analysis of PM10 concentrations at different locations in Malaysia

Cited by 42 publications

References 30 publications

A Review on Short-Term Prediction of Air Pollutant Concentrations

A Review on Short-Term Prediction of Air Pollutant Concentrations

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

Fitting Statistical Distributions Functions on Ozone Concentration Data at Coastal Areas

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