Air pollution index evaluation based on haze phenomena in East Malaysia using Giovanni satellite database
DOI:
https://doi.org/10.24191/mjcet.v7i2.886Keywords:
Air Pollution Index , Giovanni , Haze , Linear Regression Analysis , Satellite DatabaseAbstract
This work evaluates air pollution related to haze phenomena in Malaysia from 2000 to 2019. The main objective of this work is to evaluate the relationship between the Air Pollution Index (API) during haze occurrences using the Giovanni satellite database. The collected data of the API was denoted as ground-based data, while that of GIOVANNI was denoted as satellite-based data. The air pollutants targeted in the study include PM2.5, SO2, CO, and O3. Sarawak and Sabah were chosen as the study areas due to the high levels of hazardous haze pollutants observed in these regions. The data analysis utilised a linear regression approach to examine the correlation and relationship between ground-based and satellite-based measurements. Factors contributing to haze occurrence were also investigated by gathering meteorological data from GIOVANNI, including wind speed and surface temperature. The analysis's correlation coefficient (R) values range from weak, moderate and strong, with all p-values below 0.05, indicating statistical significance. Notably, wind speed shows a strong negative correlation with API, with an R-value of −0.8750, demonstrating an inverse relationship between the two variables. Similarly, temperature exhibits a moderate negative correlation with API, reflected in an R-value of −0.7270. The findings indicate a strong inverse relationship between the factors and haze pollution, with correlations from the GIOVANNI database serving as a benchmark for identifying causes of high API during haze.
References
Afroz, R., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impacts in Malaysia. Environmental Research, 92(2), 71–77. https://doi.org/10.1016/S0013-9351(02)00059-2
Akhtar, R., & Palagiano, C. (Eds.). (2018). Climate change and air pollution: The impact on human health in developed and developing countries. Springer. https://doi.org/10.1007/978-3-319-61346-8
Al-Alola, S. S., Alkadi, I. I., Alogayell, H. M., Mohamed, S. A., & Ismail, I. Y. (2022). Air quality estimation using remote sensing and GIS-spatial technologies along Al-Shamal train pathway, Al-Qurayyat City in Saudi Arabia. Environmental and Sustainability Indicators, 15, Article 100184. https://doi.org/10.1016/j.indic.2022.100184
Australian Government; Department of the Environment and Energy (2019). Particulate Matter (PM10 and PM2.5). National Pollutant Inventory. https://www.dcceew.gov.au/environment/protection/npi/substances/fact-sheets/particulate-matter-pm10-and-pm25
Binti Udin, Q. A., Binti Kasim, A., Binti Rassa, H., Ul, A., Binti, H., & Nasir, A. (2021). Factors that contribute to air pollution in Malaysia. Malaysian Journal of Business and Economics, 8(2), 43–58. https://doi.org/10.51200/mjbe.vi.3662
Christopherson, J. B., Ramaseri, C., & Quanbeck, J. Q. (2019). 2019 Joint Agency Commercial Imagery Evaluation - Land Remote Sensing Satellite Compendium. U.S. Geological Survey Circular 1455. https://doi.org/10.3133/cir1455
Dai, Z., Liu, D., Yu, K., Cao, L., & Jiang, Y. (2020). Meteorological variables and synoptic patterns associated with air pollutions in Eastern China during 2013–2018. International Journal of Environmental Research and Public Health, 17(7), Article 2528. https://doi.org/10.3390/ijerph17072528
Deng, T., Ouyang, S., He, G., Zhang, X., Leung, J. C. H., Chen, X., Wang, Q., Zhang, Z., Zou, Y., Mai, B., Liu, L., & Deng, X. (2024). Impact of aerosol actinic radiative effect on ozone during haze pollution in the Pearl River Delta region. Atmospheric Environment, 332, Article 120610. https://doi.org/10.1016/j.atmosenv.2024.120610
Department of Environment Malaysia. (2016a). Haze air pollution phenomena. In Department of Environment, Ministry of Natural Resources & Environment (pp. 1–6). Jabatan Alam Sekitar Malaysia, Department of Environment. https://www.doe.gov.my/en/2021/10/26/haze-air-pollution-phenomenon/
Department of Environment Malaysia. (2016b). Final Report: Review of Air Pollutant Index (API). https://enviro2.doe.gov.my/ekmc/wp-content/uploads/2016/11/API-FINAL-REPORT.pdf
Julfikar, S.K., Ahamed, S., Rehena, Z. (2021). Air quality prediction using regression models. In A. Choudhary, A.P. Agrawal, R. Logeswaran, &B. Unhelkar (Eds.), Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering (vol 778). https://doi.org/10.1007/978-981-16-3067-5_19
Kan, H. (2022). World Health Organization air quality guidelines 2021: Implication for air pollution control and climate goal in China. Chinese Medical Journal, 13(5), 513–515. https://doi.org/10.1097/CM9.0000000000002014
Latif, M. T., & Hamzah, W. P. (2016). Air Quality & Haze Episodes in Malaysia (Issue May). Akademi Sains Malaysia.
Latif, M. T., Othman, M., Idris, N., Juneng, L., Abdullah, A. M., Hamzah, W. P., Khan, M. F., Nik Sulaiman, N. M., Jewaratnam, J., Aghamohammadi, N., Sahani, M., Xiang, C. J., Ahamad, F., Amil, N., Darus, M., Varkkey, H., Tangang, F., & Jaafar, A. B. (2018). Impact of regional haze towards air quality in Malaysia: A review. Atmospheric Environment, 177, 28–44. https://doi.org/10.1016/j.atmosenv.2018.01.002
Latiffah, N., Rani, A., Azid, A., Khalit, S. I., Juahir, H., & Samsudin, M. S. (2018). Air pollution index trend analysis in Malaysia, 2010–15. Journal of Environmental Study, 27(2), 801–807. https://doi.org/10.15244/pjoes/75964
Li, S., Li, X., Deng, Z., Xia, X., Ren, G., An, D., Ayikan, M., & Zhong, Y. (2023). Characteristics of atmospheric boundary layer and its relation with PM2.5 during winter in Shihezi, an Oasis city in Northwest China. Atmospheric Pollution Research, 14(11), Article 101902. https://doi.org/10.1016/j.apr.2023.101902
Lu, H. C., & Fang, G. C. (2002). Estimating the frequency distributions of PM10 and PM2.5 by the statistics of wind speed at Sha-Lu, Taiwan. Science of the Total Environment, 298(1–3), 119–130. https://doi.org/10.1016/S0048-9697(02)00164-X
MyGoverment (2016). Malaysia Information - Climate https://malaysia.gov.my/portal/content/144
FMcLeod, S. (2019, May). What a p-value tells you about statistical significance. Simply Psychology. https://www.simplypsychology.org/p-value.html
Mindrila, D., & Balentyne, P. (2013). Scatterplots and Correlation. The Basic Practice of Statistics (6th ed., Vol. 3, Issue 1, pp. 73–90). W. H. Freeman and Company. https://doi.org/10.3109/08958379109145275
Muzammil, M., & Hanan, Z. (2017). Application of remote sensing instruments in air quality monitoring. Pertanika Journal of Scholarly Research Review, 3, 93–112.
New Straits Times. (2019, September 22). Haze crisis: Still no breather for much of Malaysia. New Straits Times. https://www.nst.com.my/news/nation/2019/09/523413/haze-crisis-still-no-breather-much-malaysia
Othman, J., Sahani, M., Mahmud, M., & Sheikh Ahmad, M. K. (2014). Transboundary smoke haze pollution in Malaysia: Inpatient health impacts and economic valuation. Environmental Pollution, 189, 194–201. https://doi.org/10.1016/j.envpol.2014.03.010
Padmanabhamurty, B. (2012). The role of wind in pollution dispersion. Journal of the Air Pollution Control Association, 25(9), 956–957. https://doi.org/10.1080/00022470.1975.10468120
Picardo, E. (2019). Negative Correlation: How it Works, Examples and FAQ. https://www.investopedia.com/terms/n/negative-correlation.asp
Prados, A. I., Leptoukh, G., Lynnes, C., Johnson, J., Rui, H., Chen, A., & Husar, R. B. (2010). Access, visualization, and interoperability of air quality remote sensing data sets via the Giovanni Online Tool. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3), 359–370. https://doi.org/10.1109/JSTARS.2010.2047940
Rajab, J. M., Lim, H. S., & Matjafri, M. Z. (2013). Monthly distribution of diurnal total column ozone based on the 2011 satellite data in Peninsular Malaysia. Egyptian Journal of Remote Sensing and Space Science, 16(1), 103–109. https://doi.org/10.1016/j.ejrs.2013.04.003
Redzuan, S. N., Noor, N. M., Rahim, N. A. A. A., Jafri, I. A. M., Baidrulhisham, S. E., Ul-Saufie, A. Z., Sandu, A. V., Vizureanu, P., Zainol, M. R. R. M. A., & Deák, G. (2023). Characteristics of PM10 level during haze events in Malaysia based on quantile regression method. Atmosphere, 14(2), 407. https://doi.org/10.3390/atmos14020407
Shafii, N. Z., Saudi, A. S. M., Mahmud, M., & Rizman, Z. I. (2017). Spatial assessment on ambient air quality status: a case study in Klang, Selangor. Journal of Fundamental and Applied Sciences, 9(4S), 9643–977. https://doi.org/10.4314/jfas.v9i4s.58
Shenfeld, L. (1970). Meteorological aspects of air pollution control. Atmosphere, 8(1), 3–13. https://doi.org/10.1080/00046973.1970.9676578
Vaughn, B. (2019, September 16). Indonesia haze: Why do forests keep burning? BBC News. https://www.bbc.com/news/world-asia-34265922
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