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This paper examines the influence of COVID-19-related factors on PM2.5 concentrations (PM2.5) in Singapore, Indonesia, and Thailand from January 2018 to December 2021. Using data from four sources, cluster analysis based on six socioeconomic indices was employed to select these countries for focused analysis. Generalized Additive Mixed Models (GAMM) were applied to assess associations between PM2.5 and COVID-19 factors, including new cases, deaths, vaccinations, stringency index, time series (STOL), and COVID-19 status (dummy variable). Results show that PM2.5 levels in Singapore and Indonesia were significantly impacted by COVID-19 measures, with F-statistics for new cases (22.875, p < 0.001), deaths (12.563, p = 0.012), as well as significant associations for vaccinations (t = 5.976, p < 0.001), stringency index (t = 5.124, p < 0.001), and the dummy variable (t = 6.624, p < 0.001). In contrast, PM2.5 levels in Thailand were unaffected by these factors, likely due to seasonal pollution sources. The model explains 90.3% of the variation in PM2.5 (adjusted R² = 0.872). This paper offers important insights for policymakers on incorporating air quality into health policies and highlights how pandemic responses varied across countries. By examining the impact of COVID-19 factors on PM2.5 in different nations, the study enhances understanding through detailed data and averaging periods. It reveals differences in how countries’ air quality responded to the pandemic, contributing to discussions on environmental management and public health. These findings inform policy decisions and facilitate discussions on better managing environmental and health challenges during global crises.
Czasopismo
Rocznik
Tom
Strony
116--126
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wykr.
Twórcy
autor
- Krirk University, Thailand
autor
- Quanzhou University of Information Engineering, China
autor
- JinWen University of Science and Technology, Taiwan
autor
- Hangzhou Dianzi University, China
Bibliografia
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- 16. Lee, M. & Finerman, R. (2021). COVID-19, commuting flows, and air quality. Journal of Asian Economics, 77, 101374. DOI:DOI:10.1016/j.asieco.2021.101374
- 17. Li, J., Hallsworth, A. G. & Coca‐Stefaniak, J. A. (2020). Changing Grocery Shopping Behaviours Among Chinese Consumers At The Outset Of The COVID‐19 Outbreak. Tijdschrift Voor Economische En Sociale Geografie, 111(3), pp. 574-583. DOI:10.1111/tesg.12420
- 18. Liao, Q., Yuan, J., Dong, M., Yang, L., Fielding, R. & Lam, W. W. T. (2020). Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data. J Med Internet Res, 22(5), e18796. DOI:10.2196/18796
- 19. Lim, Y. K., Kweon, O. J., Kim, H. R., Kim, T.-H. & Lee, M.-K. (2021). The impact of environmental variables on the spread of COVID-19 in the Republic of Korea. Scientific Reports, 11(1), 5977. DOI:10.1038/s41598-021-85493-y
- 20. Liu, Q., Xu, S. & Lu, X. (2021). Association between air pollution and COVID-19 infection: evidence from data at national and municipal levels. Environ Sci Pollut Res Int, 28(28), pp. 37231-37243. DOI:10.1007/s11356-021-13319-5
- 21. Lorenzo, J. S. L., Tam, W. W. S. & Seow, W. J. (2021). Association between air quality, meteorological factors and COVID-19 infection case numbers. Environmental Research, 197, 111024. DOI:10.1016/j.envres.2021.111024
- 22. Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C., Giattino, C. & Rodés-Guirao, L. (2021). A global database of COVID-19 vaccinations. Nature Human Behaviour, 5(7), pp. 947-953. DOI:10.1038/s41562-021-01122-8
- 23. Meo, S. A., Abukhalaf, A. A., Alessa, O. M., Alarifi, A. S., Sami, W. & Klonoff, D. C. (2021). Effect of Environmental Pollutants PM2.5, CO, NO2, and O3 on the Incidence and Mortality of SARS-CoV-2 Infection in Five Regions of the USA. International Journal of Environmental Research and Public Health, 18(15), 7810. DOI:10.3390/ijerph18157810
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- 25. Tuerlinckx, F., Rijmen, F., Verbeke, G. & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. 59(2), pp. 225-255. DOI:10.1348/000711005X79857
- 26. Valdés Salgado, M., Smith, P., Opazo, M. A. & Huneeus, N. (2021). Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020. International Journal of Environmental Research and Public Health, 18(14), 7409. https://www.mdpi.com/1660-4601/18/14/7409
- 27. Wang, J., Wang, J. X. & Yang, G. S. (2020). The Psychological Impact of COVID-19 on Chinese Individuals. Yonsei Med J, 61(5), pp. 438-440. DOI:10.3349/ymj.2020.61.5.438
- 28. Webster, K. (2020). How COVID 19 changed consumers’ daily lives. Retrieved August 4, 2021 from https://www.pymnts.com/coronavirus/2020/consumer-spending-behavior-covid19-study/
- 29. Weisberg, H. F. (1992). Central tendency and variability. Sage Publications.
- 30. Wetchayont, P. (2021). Investigation on the Impacts of COVID-19 Lockdown and Influencing Factors on Air Quality in Greater Bangkok, Thailand. Advances in Meteorology, 6697707. DOI:10.1155/2021/6697707
- 31. Wong, W. M., Tzeng, S.-Y., Mo, H.-F. & Su, W. (2024). Predicting air quality trends in Malaysia’s largest cities: the role of urban population dynamics and COVID-19 effects. Archives of Environmental Protection, 50(2), pp. 65-74. DOI:10.24425/aep.2024.150553
- 32. Wood, S. N. (2006). Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics, 62(4), pp. 1025-1036. DOI:10.1111/j.1541-0420.2006.00574.x
- 33. Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), pp. 3-36. DOI:10.1111/j.1467-9868.2010.00749.x
- 34. Wood, S. N. (2017). Generalized Additive Models: An introduction with R (2nd ed.). Taylor & Francis. (DOI:10.1201/9781315370279)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-902e2e61-3d79-436a-8c52-0fb368aedb5c
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