Alias, S. N., Hamid, N. Z. A., Saleh, S. H. M., & Bidin, B. (2021). Predicting Carbon Monoxide Time Series Between Different Settlements Area in Malaysia Through Chaotic Approach. Journal of Science and Mathematics Letter, 9, 45–54.
Azmi, S. Z., Latif, M. T., Ismail, A. S., Juneng, L., & Jemain, A. A. (2010). Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual Atmos Health, 3, 53–64. https://doi.org/10.1007/s11869-009-0051-1
Jusoh, K. C., Hamid, N. Z. A., & Side, S. (2021). Forecasting Through the Chaotic Approach on Carbon Monoxide Time Series in Industrial Area. Journal of Science and Mathematics Letters, 9, 55–62.
Malaysia, O. (2023). Carbon monoxide poisoning : undetected. Retrieved from https://www.osha.gov/sites/default/files/publications/carbonmonoxide-factsheet.pdf
Noor, N. M., Yahaya, A. S., Ramli, N. A., & Al Bakri Abdullah, M. M. (2015). Filling the Missing Data of Air Pollutant Concentration Using Single Imputation Methods. Applied Mechanics and Materials, 754–755(March), 923–932. https://doi.org/10.4028/www.scientific.net/amm.754-755.923
Ruslan, A. B., & Hamid, N. Z. A. (2019). Application of Improved Chaotic Method in Determining Number of K-Nearest Neighbor for CO Data Series. International Journal of Engineering and Advanced Technology, 8(3), 10–14. https://doi.org/10.35940/ijeat.F1003.0986S319
Ruslan, A. B., Hamid, N. Z. A., & Jusoh, K. C. (2020). Nonlinear Prediction of Carbon Monoxide Time Series in Highly Populated Area in Sabah. Journal of Quality Measurement and Analysis, 16(2), 157–170.
Saeipourdizaj, P., Sarbakhsh, P., & Gholampour, A. (2021). Application of imputation methods for missing values of pm10 and o3 data: Interpolation, moving average and k-nearest neighbor methods. Environmental Health Engineering and Management, 8(3), 215–226. https://doi.org/10.34172/EHEM.2021.25
Shahizam, S. I., Hamid, N. Z. A., & Side, S. (2021). Forecasting Carbon Monoxide (CO) Pollutant in Putrajaya using the Local Mean Approximation Method. Journal of Science and Mathematics Letters, 9, 63–71.
Shuhaili, A. F. A., Ihsan, S. I., & Faris, W. F. (2013). Air Pollution Study of Vehicles Emission In High Volume Traffic : Selangor , Malaysia As A Case Study. WSEAS Transactions on Systems, 12(2), 67–84.
Sukatis, F. F., Noor, N. M., Zakaria, N. A., Ul-Saufie, A. Z., & Suwardi, A. (2019). Estimation of missing values in air pollution dataset by using various imputation methods. International Journal of Conservation Science, 10(4), 791–804.
Zainuri, N. A., Jemain, A. A., & Muda, N. (2015). A comparison of various imputation methods for missing values in air quality data. Sains Malaysiana, 44(3), 449–456. https://doi.org/10.17576/jsm-2015-4403-17
Zakaria, N. A., & Noor, N. M. (2016). Imputation Methods for Filling Missing Data in Urban Air Pollution Data for Malaysia. Urbanism, 9(2), 159–166.
- Abstract viewed - 456 times
- Pdf downloaded - 230 times
Affiliations
Nor Zila binti Abd Hamid
Universiti Pendidikan Sultan Idris
Nur Syaza binti Awang Omar Ali
Affiliation not stated
Rawdah Adawiyah binti Tarmizi
Affiliation not stated
Nor Suriya binti Abd Karim
Affiliation not stated
Noor Wahida binti Md. Junus
Affiliation not stated
Nor Hafizah Binti Md Husin
Affiliation not stated
Nur Hamiza binti Adenan
Affiliation not stated
Nurul Bahiyah binti Abd Wahid
Affiliation not stated
How to Cite
Imputation of Carbon Monoxide (CO) Missing Data in Petaling Jaya Using Basic Statistical Methods
- Nor Zila binti Abd Hamid ,
- Nur Syaza binti Awang Omar Ali ,
- Rawdah Adawiyah binti Tarmizi ,
- Nor Suriya binti Abd Karim ,
- Noor Wahida binti Md. Junus ,
- Nor Hafizah Binti Md Husin ,
- Nur Hamiza binti Adenan ,
- Nurul Bahiyah binti Abd Wahid
Vol 6 No 3 (2023): September
Submitted: Sep 16, 2023
Published: Sep 25, 2023
Abstract
Air pollution is the act of contaminating the cleanliness of the air, which deteriorates the air quality and severely affects human health and the environment. This study has examined the imputation of carbon monoxide (CO) missing data, conducted in the metropolitan area of Petaling Jaya, Malaysia using basic statistical methods. The purpose of this study is to determine the best method among the five basic statistical methods for the imputation of missing CO data in the study area. The five basic statistical methods used to impute the missing data are Linear Interpolation (LI), Top Bottom Mean (TBM), Daily Mean (DM), 12-Hour Mean (M12), and 6-Hour Mean (M6). Annual hourly data for CO gas pollutants from Petaling Jaya were taken in 2017, and the data used is complete and continuous. The percentages of missing data applied in this study are 10%, 20%, and 30%. The performance indices used to evaluate basic statistical methods are Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Overall, the best basic statistical method is ; it is hoped that this study can help the Malaysian Department of Environment (DOE) in imputing missing data for CO air pollutants in the future.