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

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  • 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.
  • Publication
    Biotechnology of water treatment based on Algae cultures
    (IOP Publishing, 2020)
    Cristina Ileana Covaliu
    ;
    Ancuta Nedelcu
    ;
    Gyorgy Deak
    ;
    Oana Stoian
    ;
    Norazian Mohamed Noor
    In the current article, we will highlight on the role of algae in the treatment of wastewater. Algae cultures are an interesting solution to tertiary and quandary treatments because of their capacity to use inorganic nitrogen and phosphorus for their growth and to remove heavy metals, as well as some toxic organic compounds, thus, avoiding a secondary pollution. Our experiments were conducted using Chlorella algae for removing lead ions from wastewater. The wastewater treatment efficiency reached a maximum of 86.67% after 80 minutes.
  • Publication
    Assessment of antibiotics from natural water resources and the potential ecological risk associated with their presence in aquatic ecosystems for developing advanced technologies for removal of antibiotic
    (AIP Publishing, 2020)
    Mihaela Ilie
    ;
    György Deák
    ;
    Florica Marinescu
    ;
    Gina Ghita
    ;
    Carmen Tociu
    ;
    Marius Raischi
    ;
    Gabriel Cornățeanu
    ;
    Mădălina Boboc
    ;
    Norazian Mohamed Noor
    Aquatic ecosystems provide many services for society including water for drinking, irrigation, and recreational activities. Emergent contaminants such as antibiotics that are present mainly in urban wastewater have a substantial impact on environment and human health, such as: Potential genotoxic effects, disruption of aquatic ecosystems and development of antibiotic resistance. The main objective of this paper is to develop an advanced analytical method for identifying emergent pollutants within the antibiotic category by using high performance SPE-online-UHPLC-MS/MS techniques from different aqueous matrices, in order to develop technologies to remove them from wastewater. The ecological risk index (RI) associated with the presence of antibiotics in aquatic ecosystems was also calculated for potential ecological risk assessment, using the ratio between the measured concentration (MC) of antibiotics detected in surface water and predicted no-effect concentration (PNEC) values.
  • Publication
    Underground air quality monitoring in subway stations in Bucharest city
    (AIP Publishing, 2020)
    Deák György
    ;
    S. N. Raischi
    ;
    E. C. Pană
    ;
    Ș. A Zamfir
    ;
    M. C. Raischi
    ;
    L. Lumânăroiu
    ;
    C. Sîrbu
    ;
    Norazian Mohamed Noor
    As a result of the urban development in the cities, the underground transport has become a priority transit route used by the population, due to the fact that it is a faster route and also due to the need to reduce the road traffic from the surface in order to improve the quality of the air (especially degraded from traffic emissions). Given the large flow of passengers, especially during peak hours, studying the quality of ambient air in underground spaces has become a necessity. Therefore, this paper presents the results of the particulate matter (PM10) measurements, in 4 subway stations in Bucharest, selected according to the flow of people passing through the areas, choosing 2 stations with low flow and 2 very congested (which are intersection railway nodes). Also, in order to identify the influence of the atmospheric air quality on the underground air and the most vulnerable areas prone to lower air quality status, the results of the air pollutant dispersion scenarios realized with the Breeze AERMO software were taken into account. In addition to high level of PM10 concentrations (above the limit permitted by the national law for surface air quality), there were performed laboratory tests in order to identify metal concentration levels (lead, cadmium, manganese, copper and iron). Thus, it has been found that PM10 derives both from the erosion of the rails and the walls of the tunnels and also from the braking system of the subway trains. Furthermore, a connection was observed between the atmospheric air quality measured near the intake ventilation system and the quality of the underground air.
  • Publication
    Enhancing ecosystem biodiversity through air pollution concentrations prediction using support vector regression approaches
    (Universitatea Gheorghe Asachi din Iasi, 2023)
    Syaidatul Umairah Solehah
    ;
    Aida Wati Zainan Abidin
    ;
    Saiful Nizam Warris
    ;
    Wan Nur Shaziayani
    ;
    Balkish Mohd Osman
    ;
    Nurain Ibrahim
    ;
    Norazian Mohamed Noor
    ;
    Ahmad Zia Ul-Saufie
    Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems. © 2023 Universitatea "Alexandru Ioan Cuza" din Iasi. All rights reserved.
  • Publication
    Prediction of particulate matter (PM₁₀) during high particulate event in peninsular Malaysia using Novel Hybrid Model
    (EDP Sciences, 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.
  • 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.
  • Publication
    Assessment of indoor air quality of daycare centres in northern area of peninsular Malaysia: a case study in Perlis
    (IOP Publishing, 2020)
    P Chinathamby
    ;
    Norazian Mohamed Noor
    ;
    Sara Yasina Yusuf
    ;
    S Annas
    ;
    A Abd Kadir
    Indoor air quality (IAQ) in day care centers (DCCs) is an emerging research topic nowadays. The paper presents both quantitative and qualitative assessment of IAQ in DCCs in Northern Peninsular Malaysia especially at Perlis. Indoor air pollutants such as total volatile organic compound, carbon monoxide, and ozone have been linked to many health effects in babies, toddlers and kids below 4 years old. The aim of this study was to investigate and quantify the exposure level of indoor air contaminants within the chosen DCCs at Perlis and to analyse the survey on the health effects on Indoor Air Quality of DCCs in the District of Perlis, Malaysia. IAQ monitoring was carried out at two DCCs located in different areas of Perlis which are Kangar and Pauh. The selected parameters measured were total volatile organic compound, carbon monoxide and ozone using the Department of Occupational Health and Safety analytical method. Modified validated questionnaires were distributed to parents to obtain their children’s health symptoms. DCC at Kangar was exposed to high air pollutants compared to DCC at Pauh because it may enter their buildings from various adjacent sources as it is situated beside the busy roadside area. Anyway, the selected indoor air pollutants examined at both DCCs are not exceeding the acceptable level of standard guidelines, thereby the kids’ health are not likely to affected by these selected indoor air pollutants.
  • Publication
    Exploring the potential of agricultural waste as natural resource-based adsorbents for methylene blue removal
    ( 2024-01-01)
    Nur Liza Rahim
    ;
    Muhamad Farid Idham Sulaiman
    ;
    Ain Nihla Kamarudzaman
    ;
    Norlia Mohamad Ibrahim
    ;
    Roshazita Che Amat
    ;
    Syakirahafiza Mohammed
    ;
    Norazian Mohamed Noor
    ;
    Deák G.
    ;
    Liyana Ahmad Sofri
    Excessive agricultural waste in the agricultural industry leads to various forms of pollution, including water pollution. To address this issue, there's a growing interest in finding alternative methods. One approach is to utilize agricultural waste as natural resource-based adsorbents to eliminate contaminants, such as the case of methylene blue (MB) in this study. The study specifically focuses on using rice husk (RH) from a local rice mill in Perlis, Malaysia, to absorb methylene blue. The structure of rice husk, characterized by scanning electron microscopy (SEM), reveals a coarser and more compact outer area, contributing to its absorption capacity for methylene blue. The study on rice husk involves three main aspects: contact time, adsorbent dosage, and dye concentration. The removal percentage of MB increased as the three studied adsorption parameters increased. The adsorption data were analyzed using Langmuir and Freundlich adsorption isotherms, with the the Freundlich Isotherms were found to be more suitable based on higher coefficient of correlation (R2) values compared to Langmuir. The pseudo-second-order kinetics model demonstrated a higher R2value (1.00) compared to the pseudo-first-order model (0.747). The results indicate promising potential for addressing pollution through sustainable means and provide insights into the adsorption process under varying conditions.
      2
  • Publication
    Optimization of copper adsorption from synthetic wastewater by oil palm-based adsorbent using Central Composite Design
    ( 2020-06-10)
    Wong H.W.
    ;
    Naimah Ibrahim
    ;
    Muhammad Adli Hanif
    ;
    Norazian Mohamed Noor
    ;
    Sara Yasina Yusuf
    ;
    Masitah Hasan
    Oil palm empty fruit bunch (EFB) was chemically activated by phosphoric acid and heat treatment to produce porous activated carbon (AC) for adsorption of copper ions from synthetic wastewater using static batch test. Copper adsorption process was optimized using Response Surface Method (RSM) by varying four operating parameters i.e. pH (A), initial concentration (B), adsorbent dosage (C) and contact time (D) through a quadratic model developed based on Central Composite Design (CCD) approach. Within the tested parameter range, copper adsorption was found to be at optimum condition at pH 5, initial concentration of 200 mg/L, adsorbent dosage of 0.55 g per 200 mL copper solution and contact time of 2.5 hours, yielding 52.5% of copper removal. A good agreement was achieved by comparing the predicted model with experimental data (R2=0.9618). All four operating parameters tested are significant in affecting the adsorption process, with pH being the most significant with an F-value of 171.70. The interaction between pH and initial concentration (AB) has the most significant interacting effects (F-value of 18.30), while quadratic effects of pH (A2) and adsorbent dosage (C2) are most significant with F-values of 62.80 and 42.58 respectively.
      3  3