Norazian Mohamed Noor
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Preferred name
Norazian Mohamed Noor
Official Name
Norazian, Mohamed Noor
Alternative Name
Noor, Norazian Mohamed
Norazian, M. N.
Noor, Norazian Mohamad
Noor, N. M.
Mohamed, Norazian N.
Mohamed, N. N.
Zizi, Na Mohd
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Scopus Author ID
25221616600
Researcher ID
M-6956-2019

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Publication

Assessment of time series model for predicting long-interval consecutive missing values in air quality dataset

2025 , Daniel Kim Boon Bong , Norazian Mohamed Noor , Ahmad Zia Ul-Saufie , Faizal Ab Jalil , György Deák

Air pollutant concentration in Malaysia is continuously monitored using the Continuous Ambient Air Quality Machine (CAAQM). During the observation phase by CAAQM, some air pollutant datasets were detected missing due to machine failure, maintenance, position changes and human error. Incomplete datasets especially with the longer gaps of consecutive missing observation may lead to several significant problems including loss of efficiency, difficulties in using some computational software and bias estimation due to differences of observed and predicted dataset. This study aim evaluates the performance of the time series method i.e. Auto Regression Integrated Moving Average (ARIMA) for filling long hours of missing data in an air pollution dataset. The dataset of PM10, SO2, NO2, O3, CO, wind speed, relative humidity and ambient temperature for Pegoh and Kota Kinabalu in 2018 were used for analysis. Monte Carlo Markov Chain (MCMC) and Expectation-Maximization (EM) were employed to compare with ARIMA's effectiveness in filling the simulated missing gaps in air quality dataset. Existing missing data in the raw data were pre-treated and then simulated into 5%, 10% and 15% of missing data ranging from 24-hour to 120-hour intervals. The performance of the imputation approach was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Prediction Accuracy (PA) and Index of Agreement (IA). Overall, the Expectation-Maximization technique was selected the most effective at filling in simulated long gaps of missing data of air pollutant dataset with the range of IA from 0.74 to 0.77. In contrast, the ARIMA approach performed poorly in this research with range of IA value of 0.44 to 0.48. This was because of it requires past time-series data to generalize a forecast or impute missing data, hence, the forecast becomes a straight line and performed poorly at predicting series with long hours of missing observation.

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Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: domestic or solely transboundary factor?

2023 , Nur Alis Addiena A Rahim , Norazian Mohamed Noor , Izzati Amani Mohd Jafri , Ahmad Zia Ul-Saufie , Norazrin Ramli , Ain Nihla Kamarudzaman , Nor Amirah Abu Seman @ Haji Ahmad , Mohd Remy Rozainy Mohd Arif Zainol , Sandu Andrei Victor , Gyorgy Deak

Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM10 concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM10, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM10 concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m3 except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM10 concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM10 concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM10 level with SO2 was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM10-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.

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