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Sains Malaysiana 52(12)(2023): 3603-3618
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 airquality tremendously. In Malaysia, an instrument named as air pollution index (API) is utilizedin determining the quality of air, which is influenced by the presence of haze. API values arecalculated by considering the concentration of harmful particles in haze. So, any haze episodeheavily affects the API values and can be considered as a determining factor. Since Malaysiais prone to haze, it is crucial to identify and quantify the haze effect on the API values.Therefore, four models – an autoregressive integrated moving average (ARIMA), regressionmodel with ARIMA errors (ARIMAX), time series regression and Prophet models areemployed. It is found that ARIMAX (4,0,1) with non-zero mean is the best model in describingthe API data with presence of haze as external regressor based on the smallest adequacy anderror measures for training and test datasets. In conclusion, the effect of haze is significant indescribing the API values and thus, proper health management 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 satu faktor penentu. Memandangkan Malaysia cenderung untuk mengalami jerebu, adalah penting untuk mengenal pasti dan mengukur kesan jerebu terhadap nilai IPU. Oleh itu, empat model – purata bergerak terintegrasi auto regresif (ARIMA), regresi dengan ralat ARIMA (ARIMAX), regresi siri masa dan model Prophet 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. Prophet. 2022. Automatic Forecasting Procedure.https://github.com/facebook/prophet R Core Team. 2022. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ 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. Taylor, S.J. & Letham, B. 2018. Forecasting at scale. The American Statistician 72(1): 37-45.
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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