Sains Malaysiana 52(12)(2023): 3893-3906

http://doi.org/10.17576/jsm-2023-5212-20

 

Quantifying Haze Effect using Air Pollution Index Data

(Pengukuran Kesan Jerebu menggunakan Data Indeks Pencemaran Udara)

 

RAZIK RIDZUAN MOHD TAJUDDIN* & NURULKAMAL MASSERAN

 

Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

 

Received: 13 July 2023/Accepted: 7 December 2023

 

Abstract

Malaysia has been misfortunate with intermittent haze episodes since 1997 which affect the air quality tremendously. In Malaysia, an instrument named air pollution index (API) is utilized in determining the quality of air, which is influenced by the presence of haze. API values are calculated by considering the concentration of harmful particles in haze. Therefore, any haze episode heavily affects the API values and can be considered as a determining factor. Since Malaysia is prone to haze, it is crucial to identify and quantify the haze effect on the API values. Therefore, a regression model with autoregressive integrated moving average errors (ARIMAX) is employed. It is found that ARIMAX (4,0,1) with non-zero mean is the best model in describing the API data with presence of haze as external regressor based on the smallest adequacy and error measures for training and test datasets. In conclusion, the effect of haze is significant in describing the API values and thus, proper health managements is required during haze episodes.

 

Keywords: ARIMAX; haze effect; regression with ARIMA errors

 

Abstrak

Malaysia mengalami nasib malang dengan episod jerebu yang berterusan sejak tahun 1997 yang memberi kesan yang besar terhadap kualiti udara. Di Malaysia, terdapat satu pengukur yang dikenali sebagai indeks pencemaran udara (IPU) yang digunakan untuk menentukan kualiti udara yang dipengaruhi oleh kehadiran jerebu. Nilai IPU dihitung berdasarkan kepekatan zarah berbahaya dalam jerebu. Oleh itu, apa-apa episod jerebu akan memberi kesan yang besar kepada nilai IPU dan boleh dianggap sebagai suatu faktor penentu. Memandangkan Malaysia cenderung untuk mengalami jerebu, adalah penting untuk mengenal pasti dan mengukur kesan jerebu terhadap nilai IPU. Oleh itu, satu model regresi dengan ralat purata bergerak terintegrasi auto regresif (ARIMAX) digunakan. Didapati bahawa ARIMAX (4,0,1) dengan min bukan sifar merupakan model terbaik dalam menerangkan data IPU dengan kehadiran jerebu sebagai regresor luaran berdasarkan ukuran kecukupan serta ralat terkecil untuk set data latihan dan set data ujian. Kesimpulannya, kesan jerebu adalah signifikan dalam menerangkan nilai IPU dan oleh yang demikian, pengurusan kesihatan yang betul diperlukan sepanjang jerebu berlaku.

 

Kata kunci: ARIMAX; kesan jerebu; regresi dengan ralat ARIMA

 

REFERENCES

Abdulali, B.A.A. & Masseran, N. 2021. Artificial Neural Network (ANN) and Arima Models for better forecast of the air pollution data in Malaysia. Scholars Journal of Physics, Mathematics and Statistics 10: 184-196.

Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6): 716-723.

Al-Dhurafi, N.A., Masseran, N., Zamzuri, Z.H. & Razali, A.M. 2018. Modeling unhealthy air pollution index using a peaks-over-threshold method. Environmental Engineering Science 35(2): 101-110.

Albahar, S., Li, J., Al-Zoughool, M., Al-Hemoud, A., Gasana, J., Aldashti, H. & Alahmad, B. 2022. Air pollution and respiratory hospital admissions in Kuwait: The epidemiological applicability of predicted PM2.5 in arid regions. International Journal of Environmental Research and Public Health 19(10): 5998.

Alyousifi, Y., Masseran, N. & Ibrahim, K. 2018. Modeling the stochastic dependence of air pollution index data. Stochastic Environmental Research and Risk Assessment 32: 1603-1611.

Alyousifi, Y., Othman, M., Sokkalingam, R., Faye, I. & Silva, P.C. 2020. Predicting daily air pollution index based on fuzzy time series markov chain model. Symmetry 12(2): 293.

Bakar, M.A.A., Ariff, N.M., Bakar, S.A. & Ramyah, G. 2022. Peramalan kualiti udara menggunakan kaedah pembelajaran mendalam Rangkaian Perlingkaran Temporal (TCN). Sains Malaysiana 51(11): 3785-3793.

California Air Resources Board. Inhalable Particulate Matter and Health (PM2.5 and PM10). https://ww2.arb.ca.gov/resources/inhalable-particulate-matter-and-health#:~:text=PM10%20also%20includes%20dust%20from,pollen%20and%20fragments%20of%20bacteria. Accessed 13 July 2023.

Department of Environment. 2021. Kronologi Episod Jerebu di Malaysia. https://www.doe.gov.my/2021/10/04/kronologi-episod-jerebu-di-malaysia-2/ Accessed 13 July 2023

Department of Environment. 2019. Air Pollutant Index (API) Calculation.http://apims.doe.gov.my/pdf/API_Calculation.pdf Accessed 13 July 2023.

Department of Environment. 1997. A Guide to Air Pollutant Index in Malaysia (API). https://aqicn.org/images/aqi-scales/malaysia-api-guide.pdf Accessed on 10 July 2023.

Glover, D. & Jessup, T. 2006. Indonesia's Fires and Haze: The Cost of Catastrophe. ISEAS, IDRC.

Gourav, Rekhi, J.K., Nagrath, P. & Jain, R. 2020. Forecasting air quality of Delhi using ARIMA model. Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, Vol. 612, edited by Jain, V., Chaudhary, G., Taplamacioglu, M. & Agarwal, M. Singapore: Springer

Hyndman, R.J. 2022. The ARIMAX model muddle. https://robjhyndman.com/hyndsight/arimax/

Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L. & O’Hara-Wild, M. 2020. Package ‘forecast’.https://Cran.r-Project.Org/Web/Packages/Forecast/Forecast.pdf

Isaifan, R.J. 2023. Air pollution burden of disease over highly populated states in the Middle East. Frontiers in Public Health 10: 1002707.

Ismail, M.S. & Masseran, N. 2023. Modeling the characteristics of unhealthy air pollution events using bivariate copulas. Symmetry 15(4): 907.

Leong, W., Kelani, R. & Ahmad, Z. 2020. Prediction of air pollution index (API) using support vector machine (SVM). Journal of Environmental Chemical Engineering 8(3): 103208.

Liu, J-B. & Yuan, X-Y. 2023. Prediction of the air quality index of Hefei based on an improved ARIMA model. AIMS Mathematics 8(8): 18717-18733.

Liu, S-K., Cai, S., Chen, Y., Xiao, B., Chen, P. & Xiang, X-D. 2016. The effect of pollutional haze on pulmonary function. Journal of Thoracic Disease 8(1): E41.

Liu, T., Lau, A.K., Sandbrink, K. & Fung, J.C. 2018. Time series forecasting of air quality based on regional numerical modeling in Hong Kong. Journal of Geophysical Research: Atmospheres 123(8): 4175-4196.

Masseran, N. 2022. Power-law behaviors of the severity levels of unhealthy air pollution events. Natural Hazards 112(2): 1749-1766.

Masseran, N. 2021. Power-law behaviors of the duration size of unhealthy air pollution events. Stochastic Environmental Research and Risk Assessment 35: 1499-1508.

Masseran, N. & Safari, M.A.M. 2020a. Intensity–duration–frequency approach for risk assessment of air pollution events. Journal of Environmental Management 264: 110429.

Masseran, N. & Safari, M.A.M. 2020b. Risk assessment of extreme air pollution based on partial duration series: IDF approach. Stochastic Environmental Research and Risk Assessment 34: 545-559.

Mohd Nadzir, M.S., Mohd Nor, M.Z., Mohd Nor, M.F.F., A Wahab, M.I., Ali, S.H.M., Otuyo, M.K., Abu Bakar, M.A., Saw, L.H., Majumdar, S. & Ooi, M.C.G. 2021. Risk assessment and air quality study during different phases of COVID-19 lockdown in an urban area of Klang Valley, Malaysia. Sustainability 13(21): 12217.

Mun, C., Abd Rahman, N.H. & Ilias, I.S.C. 2022. Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting. Sains Malaysiana 51(8): 2645-2654.

Priyankara, S., Senarathna, M., Jayaratne, R., Morawska, L., Abeysundara, S., Weerasooriya, R., Knibbs, L.D., Dharmage, S.C., Yasaratne, D. & Bowatte, G. 2021. Ambient PM2.5 and PM10 exposure and respiratory disease hospitalization in Kandy, Sri Lanka. International Journal of Environmental Research and Public Health 18(18): 9617.

Rahim, N.A.A.A., Noor, N.M., Jafri, I.A.M., Ul-Saufie, A.Z., Ramli, N., Seman, N.A.A., Kamarudzaman, A.N., Zainol, M.R.R.M.A., Victor, S.A. & Deak, G. 2023. Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: Domestic or solely transboundary factor? Heliyon 9(6): e17472.

Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6(2): 461-464.

Sugiura, N. 1978. Further analysis of the data by Akaike's information criterion and the finite corrections: Further analysis of the data by Akaike's. Communications in Statistics-theory and Methods 7(1): 13-26.

Taşpınar, F. 2015. Time series models for air pollution modelling considering the shift to natural gas in a Turkish city. CLEAN–Soil, Air, Water 43(7): 980-988.

Zhang, Z., Wang, J., Chen, L., Chen, X., Sun, G., Zhong, N., Kan, H. & Lu, W. 2014. Impact of haze and air pollution-related hazards on hospital admissions in Guangzhou, China. Environmental Science and Pollution Research 21: 4236-4244.

 

*Corresponding author; email: rrmt@ukm.edu.my

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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