Norazrin Ramli
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Norazrin Ramli
Official Name
Norazrin, Ramli
Alternative Name
Ramli, Norazrin
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Researcher ID
HTP-4661-2023

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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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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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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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