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
Main Affiliation
Scopus Author ID
25221616600
Researcher ID
M-6956-2019

Research Output
Now showing 1 - 7 of 7
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Predicting Particulate Matter (PM₁₀) during High Particulate Event (HPE) using Quantile Regression in Klang Valley, Malaysia

2023 , Nur Alis Addiena A. Rahim , Norazian Mohamed Noor , Izzati Amani Mohd Jafri , Norazrin Ramli , Mohamad Anuar Kamaruddin , György Habil Deák

Particulate matter (PM₁₀) is the key indicator of air quality index (API) during high particulate event (HPE). The presence of PM₁₀ is believed to have an adverse effect on human health and environment. Therefore, the prediction of future PM₁₀ concentration is very important because it can aid the local authorities to implement precautionary actions to limit the impact of air pollution. This study aims to compare the performances of two predictive models, which include Multiple Linear Regression (MLR) and Quantile Regression (QR) in predicting the next-day PM10 concentration during HPE. The hourly dataset of PM₁₀ concentration with other trace gases and weather parameters at Kelang and Petaling Jaya from the year of historic haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the performances of the predictive models. From the results, QR model at quantile 0.3 and 0.6 was chosen as the best predictive tools for predicting the next day PM₁₀ concentration during haze event in Kelang and Petaling Jaya, respectively. showed better performance for the prediction of next-day PM₁₀ concentration in Kelang. These results indicate that QR can be used as one of predictive tool to forecast air pollution concentration especially during unusual condition of air quality.

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Characteristics of PM10 Level during haze events in Malaysia based on quantile regression method

2023 , Siti Nadhirah Redzuan , Norazian Mohamed Noor , Nur Alis Addiena A. Rahim , Izzati Amani Mohd Jafri , Syaza Ezzati Baidrulhisham , Ahmad Zia Ul-Saufie , Andrei Victor Sandu , Petrica Vizureanu , Mohd Remy Rozainy Mohd Arif Zainol , György Deák

Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m3. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R2 value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model.

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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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Modified linear regression for predicting ambient particulate pollutants (PM₁₀) during High Particulate Event

2023 , Izzati Amani Mohd Jafri , Norazian Mohamed Noor , Nur Alis Addiena A. Rahim , Syaza Ezzati Baidrulhisham , Norazrin Ramli , Ahmad Zia Ul-Saufie , György Deák Habil

Particulate Matter (PM₁₀) is one of the most significant contributors towards haze or high particulate event (HPE) that occurs in Malaysia. HPE can severely affect human health, environment and economic so it is important to create a reliable prediction model in predicting future PM₁₀ concentration especially during HPE. Therefore, the aim of this study is to investigate the performance of modified linear regression models in predicting the next-day Particulate Matter (PM₁₀+24) concentration at two areas in the peninsular Malaysia namely, Bukit Rambai and Nilai. Hourly air quality dataset during historic HPE in 1997, 2005, 2013 and 2015 were used for analysis. Pearson correlation was used to select the input of the PM₁₀ prediction model where only parameters with moderate (0.6 > r > 0.3) and strong (r > 0.6) correlation with PM₁₀ concentration were selected as independent variables input in creating the multiple linear regression (MLR) model. The performance of modified linear regression model was evaluated by using several performance indicator which is Prediction Accuracy (PA), Index of Agreement (d 2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that the modified MLR (parameter with r > 0.6 included as input) gave the best prediction model for the next-day PM₁₀ concentration in both Bukit Rambai and Nilai.

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Prediction of particulate matter (PM₁₀) during high particulate event in peninsular Malaysia using Novel Hybrid Model

2023 , Izzati Amani Mohd Jafri , Norazian Mohamed Noor , Nur Alis Addiena A Rahim , Ahmad Zia Ul Saufie , György Deak Habil

High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles present can be inhaled, leading to respiratory diseases and other health problem. Knowing the adverse effects of air pollution episodes to human health, it is crucial to create suitable models that can effectively and accurately predict air pollution concentration. This study proposed a hybrid model for forecasting the next day PM₁₀ concentration in peninsular Malaysia namely Shah Alam, Nilai, Bukit Rambai and Larkin. Hourly air pollutant concentration (PM₁₀, NOx, NO₂, SO₂, CO, O₃) and meteorological parameters (RH, T, WS) during the HPE events in 1997, 2005, 2013 and 2015 were used. Support Vector Machine (SVM) and Quantile Regression (QR) was combined to construct a hybrid models (SVM-QR) to reduce the number of input variables. Performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Index of Agreement (d2) were used to evaluate the performance of the predictive models. SVM-QR model resulted good performance in all areas. SVM-3 was selected as the best model at Bukit Rambai (MAE=5.72, RMSE=9.71) and Shah Alam (MAE=11.89, RMSE=22.66), while SVM-1 as the best model at Larkin and Nilai with the value (MAE=7.22, RMSE=13.38) and (MAE=6.88, RMSE=11.84), respectively. This strategy was proven to help reducing the complexity of the model and enhance the predictive capacity of the model.

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Prediction of missing data in rainfall dataset by using simple statistical method

2020 , Izzati Amani Mohd Jafri , Norazian Mohamed Noor , Ahmad Zia Ul-Saufie , Annas Suwardi

Almost all of the data obtained from hydrological station contains missing data. Usually, this problem occurs due to equipment failures, maintenance work and human error. Incomplete dataset will reduce the ability of a statistical analysis and can cause a bias estimation due to systematic differences between observed and unobserved data. In this study, four simple statistical method such as Series Mean, Average Mean Top Bottom, Linear Interpolation and Nearest Neighbour were applied to predict the missing values in a rainfall dataset. An annual daily data for rainfall from nine selected monitoring station (from 2009 until 2018) were described using descriptive statistic. Then, the dataset were randomly simulated into 4 percentages of missing (5%, 10%, 15% and 20%) by using statistical package for social sciences software. The performance of this imputation methods were evaluated by using four performance indicators namely Mean Absolute Error, Root Mean Squared Error, Prediction Accuracy, and Index of Agreement. Overall, Linear Interpolation method was selected as the best imputation method to predict the missing data in the rainfall dataset.

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Prediction of PM₁₀ level during high particulate event in Malaysia using modified model

2023 , Nur Alis Addiena A Rahim , Norazian Mohamed Noor , Izzati Amani Mohd Jafri , Ahmad Zia Ul Saufie , Boboc Madalina

Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the citizen especially during HPE. This study aims to develop a modified Quantile Regression (QR) model to forecast the PM10 concentration during HPE in Malaysia. The performances of three predictive models namely Multiple Linear Regression (MLR), Quantile Regression (QR) and a modified QR models i.e. combination of QR with Relief-based were compared. The hourly dataset of PM10 concentration with other gaseous pollutants and weather parameters at Klang from the year with severe haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the accuracy of the predictive models. This study found that the Relief-QR model showed the best performance compared to MLR and QR models. The prediction of future PM10 concentration is very important because it can aid the local authorities to implement precautionary measures to limit the impact of air pollution.

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