Norazrin Ramli
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Norazrin Ramli
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Norazrin, Ramli
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Ramli, Norazrin
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Researcher ID
HTP-4661-2023

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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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Analysis of air pollution in Malaysia: implications for environmental conservation using granger causality and pearson correlation

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.

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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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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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Unravelling the deposition of indoor microplastics at various heights across rooms

2023 , Chen Sin Yee , Sara Yasina Yusuf , Syazwaana Mohd Noor , Nor Ruwaida Jamian , Norazrin Ramli , Dewika Naidu , Matei Monica

Microplastics (MP) are widely present in both outdoor and indoor environments. Extensive research has thoroughly documented the potential negative impacts of MPs on human health. This study utilized a deposited sample method for 3 weeks, with eight-hour daily exposures, using funnels and bottles to investigate the properties of MPs in the office and laboratory settings of the Faculty of Civil Engineering & Technology (FCET), Universiti Malaysia Perlis. The characteristics examined included the deposition rate, size, form, and colour of the microplastics. Samples were collected at three different heights. The samples underwent pre-treatment procedures, such as physical counting and categorization (size, colour and shape). Micro-Raman analysis was performed to determine the primary polymer types. The deposition rate in the office was found to be 4,960 counts/(m2.h), while the rate in the laboratory was 6,940 counts/(m2.h). Human activities and the appearance of synthetic materials, especially from textiles, play a big role in the deposition rate of MPs in the environment. During the day, the rates were higher than at night. The results of the study showed that indoor MPs come in many different colours, with transparent and black being the most common. About 42% of the size range of fibrous MPs was between 200 μm and 2000 μm, and more than 15% of the particles were between 20 μm and 200 μm. Most of the time, fragments were smaller than strands. The most abundance polymers detected in both rooms were polycarbonate (PC), pigments and polymethyl methacrylate (PMMA).

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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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Short-term predictions of PM₁₀ using Bayesian Regression Models

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.

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