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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Performance of Bayesian Model Averaging (BMA) for short-term prediction of PM10 concentration in the Peninsular Malaysia

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.

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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

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.

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Morphological changes analysis using 3D bathymetric surveys in Chilia Branch - Bystroe Channel bifurcation area

2023 , Georgeta Tudor , György Deák , Marius Raischi , Miruna Arsene , Elena Holban , Norazian Mohamed Noor , Roshazita Che Amat

The Bystroe Channel project with transboundary environmental impact in the Danube Delta, area of great ecological significance that has already a strong anthropic footprint, requires close monitoring of water quality parameters in order to determine their tendencies and their impact on the ecosystem components. Riverbed bathymetry surveys using multibeam echo-sounders are of high interest due to the data resolution and coverage capabilities that surpass the single-beam methods. Two riverbed elevation datasets, recorded in consecutive years, have been used to carry out morphological comparative analysis for the area where Chilia branch bifurcates in Bystroe Channel and Old Stambul. The analysis has been performed both on the bathymetry grids as a whole and on 3 longitudinal and 9 transversal river sections, the morphological changes values being in majority included in [-0.5 m;+0.5 m], with a minimum of -2.4 m and a maximum of 2.2 m, showing the bifurcation influence on the erosion/deposition processes results.

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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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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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Assessment of the state of Ichthyofauna from Danube River – Caleia Branch, Romania: a sustainable development context

2020 , Tiberius-Marcel Danalache , György Deák , Elena Holban , Cosmin Parlog , Carmen Georgeta Nicolae , Stelian Matei , Mihai-Alexandru Cristea , Norazian Mohamed Noor

Evaluating the state of ichthyofauna at both the Lower Danube level and at the national level contains knowledge gaps regarding species dynamics, with the most complex studies regarding species composition being undertaken more than 50 years ago. Over time, the Danube River - an important navigation route that connects Western Europe with Asia - has suffered a series of anthropogenic interventions that led to river discharge regularization, interruptions of longitudinal/latitudinal connectivity and reductions in floodplain area. These anthropogenic activities may negatively impact suitable fish habitats leading to demographical effects. The Danube is regarded as a river with high species richness that provides a source of income for the local population by the practice of commercial fishing. The area of interest for this study was selected taking into account the fact that, in the last decade, it was subject to hydrotechnical works that aim to redistribute the river discharge to improve navigation conditions. The ichthyofauna population dynamics is analyzed using an 8 year-long dataset that includes baseline data before the project started and a monitoring period after the project ended. The results indicate the presence of 38 fish species (excluding anadromous fish species – sturgeons and shads). The identified fish species are classified in two categories: 1) species of commercial interest and 2) species of Community interest. This study provides evidence that the high mobility capacity of the fish species is the main factor affecting species dynamics as support of the national efforts in action to stop the degradation of aquatic habitats and biodiversity, in response to goal 15 “Life Earth” of the UN 2030 AGENDA for sustainable development.

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