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 - 9 of 9
  • Publication
    A hydrosuction siphon system to remove particles using fan blades
    (MDPI, 2023-01)
    Mohammed Hamid Rasool
    ;
    Mohd Remy Rozainy Mohd Arif Zainol
    ;
    Norazian Mohamed Noor
    ;
    Mohd Sharizal Abdul Aziz
    ;
    Mohd Hafiz Zawawi
    ;
    Muhammad Khairi A. Wahab
    ;
    Mohd Azmeer Abu Bakar
    Sedimentation in dam reservoirs can cause problems that lead to loss of storage capacity and decrease in the flood control volume. Hydrosuction sediment removal is one of the methods used to remove sediments from within a reservoir using the suction energy provided by the effective head. In this study, a new tool has been developed by attaching the reservoir to a suction pipe intake point and using a simple fan blade mechanism for the hydrosuction sediment removal system. This mechanism is used to create a vortex flow to suspend the settled particles. This paper investigated the effects of the fan blade angles, effective head, and inlet height from the surface of layer particles on the performance and efficiency of fan blades hydrosuction sediment removal (FBHSSR) and hydrosuction sediment removal (HSSR) systems based on the geometric scour hole parameters. Results from the experimental tests indicated the effectiveness of the FBHSSR system, with the fan blade angles of 30°, 45°, and 60° leading to approximately 800%, 200%, and 117%, respectively, removed particles greater than those of the HSSR system. Furthermore, the maximum depth and diameter of the scour hole were increased by 206%, 200%, and 137% and 135, 112%, and 117%, respectively, for each angle. The effective head or experiment time also enhanced system performance by increasing the suction discharge, but no change was observed in terms of efficiency. The critical inlet heights for the FBHSSR and HSSR systems are 1 time and 2.54 times, respectively, more than the diameter of the suction pipe. Thus, it can be concluded that using fan blades in HSSR systems is a good approach to improve the properties of the scour hole.
  • Publication
    Performance of Bayesian Model Averaging (BMA) for short-term prediction of PM10 concentration in the Peninsular Malaysia
    (MDPI, 2023)
    Norazrin Ramli
    ;
    Hazrul Abdul Hamid
    ;
    Ahmad Shukri Yahaya
    ;
    Ahmad Zia Ul-Saufie
    ;
    Norazian Mohamed Noor
    ;
    Ain Nihla Kamarudzaman
    ;
    György Deák
    ;
    Nor Amirah Abu Seman @ Haji Ahmad
    In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.
  • Publication
    Predicting Particulate Matter (PM₁₀) during High Particulate Event (HPE) using Quantile Regression in Klang Valley, Malaysia
    (IOP Publishing, 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.
  • Publication
    Analysis of air pollution in Malaysia: implications for environmental conservation using granger causality and pearson correlation
    (Universitatea Gheorghe Asachi din Iasi, 2025)
    Zulkifli Abd Rais
    ;
    Norazrin Ramli
    ;
    Hazrul Abdul Hamid
    ;
    Norazian Mohamed Noor
    ;
    Ahmad Zia Ul-Saufie
    ;
    Mohd Khairul Nizam MAHMAD
    This study investigates the relationships between air pollutants (PM₁₀, SO₂, NO₂, O₃, CO) and meteorological factors (temperature, relative humidity, wind speed) across five states in Malaysia: Seberang Perai, Shah Alam, Nilai, Larkin and Pasir Gudang. Using time-series data from 2017 to 2021, we applied Granger causality and Pearson correlation to explore the predictive relationships and linear associations between these variables. Granger causality provided insights into temporal precedence, revealing significant predictive relationships such as temperature Granger-causing PM₁₀ and O₃ in Nilai and Shah Alam. Meanwhile, Pearson correlation highlighted strong linear relationships, such as the positive correlation between PM₁₀ and wind speed in Shah Alam and the negative correlation between humidity and O₃ across several stations. By comparing both methods, we show how combining Granger causality with Pearson correlation can enhance environmental modelling, offering a comprehensive approach to air pollution prediction. This integration provides robust insights into the dynamics of air quality, which are critical for developing effective pollution control strategies.
  • Publication
    Characteristics of PM10 Level during haze events in Malaysia based on quantile regression method
    (MDPI, 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.
      1  9
  • Publication
    The replacement of missing values of continous air pollution monitoring data using Mean Top Bottom Imputation technique
    ( 2006)
    Norazian Mohamed Noor
    ;
    Ahmad Shukri Yahaya
    ;
    Nor Azam Ramli
    ;
    Mohd. Mustafa Al Bakri Abdullah
    Air pollutants data such as PM10 carbon monoxide, sulphur dioxide and ozone concentration were obtained from automated monitoring stations. These data usually contain missing values that can cause bias due to systematic differents between observed and unobserved data. Therefore, it is impirtant to find the best way to estimate these missing values to ensure that the data analyzed are of high precision. This paper focuses on the usage of mean top bottom imputation technique to replace the missing values. Three performance indicators were calculated in order to describe the goodness of fit of this technique. In order to test the efficiency of the method applied, PM10 monitoring dataset for Kuala Lumpur was used as case study. Three distributions that are Weibull, gamma and lognormal were fitted to the datasets after replacement of missing values using mean top bottom method and performance indicators were calculated to describe the qualities of the distributions. The results show that mean top bottom method gives very good performances at low percentage of missing data but the performances slightly decreased at higher degree of complexity. It was found that gamma distribution is the most appropriate distribution representing PM10 emissions in Kuala Lumpur.
      4  10
  • Publication
    Short-term predictions of PM₁₀ using Bayesian Regression Models
    (EDP Sciences, 2023)
    Norazrin Ramli
    ;
    Hazrul Abdul Hamid
    ;
    Ahmad Shukri Yahaya
    ;
    Norazian Mohamed Noor
    ;
    Holban Elena
    One of the air pollutants that poses the greatest threat to human health is PM10. The objectives of this study are to develop a prediction model for PM10. The Multiple Linear Regression (MLR) and Bayesian Regression (BRM) models were constructed to forecast the following dayâ s (Day 1) and next two daysâ (Day 2) PM10 concentration. To choose the optimal model, the performance metrics (NAE, RMSE, PA, IA, and R2) are applied to each model. Jerantut, Nilai, Shah Alam, and Klang were chosen as monitoring sites. Data from the Department of Environment Malaysia (DOE) was utilised as a case study for five years, with seven parameters (PM10, temperature, relative humidity, NO2, SO2, CO, and O3) chosen. According to the findings, the key factors responsible for the unhealthy levels of air quality at the Klang station include carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) from industrial and maritime activities, which are thought to influence PM10 concentrations in the area. When compared to MLR models, the results demonstrate that BRM are the best model for predicting the next day and next two days PM10 concentration at all locations.
      11  2
  • Publication
    Investigating the relationship between the Manning Coefficients (n) of a perforated subsurface stormwater drainage pipe and the hydraulic parameters
    (MDPI, 2023)
    Junaidah Abdullah
    ;
    Mohd Remy Rozainy Mohd Arif Zainol
    ;
    Ali Riahi
    ;
    Nor Azazi Zakaria
    ;
    Mohd Fazly Yusof
    ;
    Syafiq Shaharuddin
    ;
    Muhammad Nurfasya Alias
    ;
    Muhammad Zaki Mohd Kasim
    ;
    Mohd Sharizal Abdul Aziz
    ;
    Norazian Mohamed Noor
    ;
    Mohd Hafiz Zawawi
    ;
    Jazaul Ikhsan
    Subsurface perforated pipes drain infiltrated stormwater runoff while attenuating the peak flow. The Manning roughness coefficient (n) was identified as a fundamental parameter for estimating roughness in various subsurface channels. Hence, in this work, the performance of a six-row non-staggered sand-slot perforated pipe as a sample of the subsurface drainage is investigated experimentally in a laboratory flume at Universiti Sains Malaysia (USM) aimed at determining the Manning roughness coefficients (n) of the pipe and assessing the relationship between the Manning’s n and the hydraulic parameters of the simulated runoff flow under the conditions of the tailgate channel being opened fully (GFO) and partially (GPO), as well as the pipe having longitudinal slopes of 1:750 and 1:1000. Water is pumped into the flume at a maximum discharge rate of 35 L/s, and the velocity and depth of the flow are measured at nine points along the inner parts of the pipe. Based on the calculated Reynolds numbers ranging from 38,252 to 64,801 for both GFO and GPO conditions, it is determined that most of the flow in the perforated pipe is turbulent, and the calculated flow discharges and velocities from the outlets under GFO are higher than the flow and velocity rates under GPO with similar pipe slopes of 1:750 and 1:1000. The Manning coefficients are calculated at nine points along the pipe and range from 0.004 to 0.009. Based on the ranges of the calculated Manning’s n, an inverse linear relationships between the Manning coefficients and the flow velocity under GFO and GPO conditions are observed with the R2 of 0.975 and 0.966, as well as 0.819 and 0.992 resulting from predicting the values of flow velocities with the equations v = ((0.01440 − n)/0.009175), v = ((0.01330 − n)/0.00890), v = ((0.02007 − n)/0.01814), and v = ((0.01702 − n)/0.01456) with pipe slopes of 1:750 and 1:1000, respectively. It is concluded that since the roughness coefficient (Manning’s n) of the pipe increases, it is able to reduce the flow velocity in the pipe, resulting in a lower peak of flow and the ability to control the quantity of storm water in the subsurface urban drainages.
      13  1
  • Publication
    Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: domestic or solely transboundary factor?
    (Elsevier, 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.
      24  2