Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models

NurIzzah M. Hashim, Norazian Mohamed Noor, Ahmad Zia Ul-Saufie, Andrei Victor Sandu, Petrica Vizureanu, György Deák and Marwan Kheimi
Additional contact information
NurIzzah M. Hashim: Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, d/a Pejabat Pos Besar, P.O. Box 77, Kangar 01007, Malaysia
Norazian Mohamed Noor: Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, d/a Pejabat Pos Besar, P.O. Box 77, Kangar 01007, Malaysia
Ahmad Zia Ul-Saufie: Faculty of Computer and Mathematical Sciences, Universiti Teknologi Mara (UiTM), Shah Alam 40450, Malaysia
Andrei Victor Sandu: Faculty of Materials Science and Engineering, Gheorghe Asachi Technical University of Iasi, 61 D. Mangeron Blvd., 700050 Iasi, Romania
Petrica Vizureanu: Faculty of Materials Science and Engineering, Gheorghe Asachi Technical University of Iasi, 61 D. Mangeron Blvd., 700050 Iasi, Romania
György Deák: National Institute for Research and Development in Environmental Protection INCDPM, Splaiul Independentei 294, 060031 Bucharest, Romania
Marwan Kheimi: Department of Civil Engineering, Faculty of Engineering—Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sustainability, 2022, vol. 14, issue 13, 1-23

Abstract: Ground-level ozone (O 3 ) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O 3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O 3 , CO, NO 2 , PM 10 , NmHC, SO 2 ) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O 3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R 2 ). Surface O 3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R 2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O 3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O 3 level in the specified selected areas with the range of R 2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O 3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.

Keywords: air quality modeling; ozone; multiple linear regression; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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