MIXTURE DISTRIBUTION ANALYSIS OF DAIRY AVERAGE PM2.5 CONCENTRATIONS IN BANGKOK, THAILAND
DOI:
https://doi.org/10.55766/sujst-2024-05-e05095Keywords:
Fine particulate matter (PM2.5), Information criteria, Mixture distribution, Statistical distributionAbstract
Air pollution, specifically PM2.5, poses significant health risks and global environmental challenges. Bangkok, the capital city of Thailand, also experiences severe levels of PM2.5. The objective of this study is to determine the optimal probability distribution for PM2.5 concentration in Bangkok. Daily average PM2.5 concentrations from January 1, 2018, to December 31, 2022, were analyzed at 10 monitoring sites in the Bangkok area. The concentration patterns of PM2.5 were characterized using several statistical distributions, including lognormal, gamma, and Weibull distributions, as well as 2-mixture and 3-mixture distributions. Akaike and Bayesian information criteria were utilized to identify the optimal statistical distribution fit. Results indicate that the lognormal-lognormal distribution is most suitable for the PM2.5 concentration data from stations situated along roadways. However, stations in residential areas exhibit variability in statistical distributions, likely due to distinct factors at each location. Nevertheless, it is observed that each station in Bangkok is matched to a distribution with lognormal components.
References
Aquilanti, L., Cacace, S., Camilli, F., and Maio, R.D. (2021). A mean field games model for finite mixtures of Bernoulli and categorical distributions. Journal of Dynamics and Games, 8(1):35-59. https://doi.org/10.3934/jdg.2020033
Balmes, J.R. and Eisner, M.D. (2016). Indoor and Outdoor Air Pollution. In: Murray and Nadel’s Textbook of Respiratory Medicine. 6th ed., Elsevier, p. 1331-1342. https://doi.org/10.1016/B978-1-4557-3383-5.00074-9
Bernardi, M. (2013). Risk measures for skew normal mixtures. Statistics and Probability Letters, 83(8):1819-1824. https://doi.org/10.1016/j.spl.2013.04.016
Channoufi, I., Bourouis, S., Bouguila, N., and Hamrouni, K. (2018). A flexible statistical model for image denoising. In: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27-29, 2018, p. 30-38. https://doi.org/10.1007/978-3-319-93000-8_4
Ding, J., Tarokh, V., and Yang, Y. (2018). Model selection techniques: an overview. In: IEEE Signal Processing Magazine, 35(6):16-34. https://doi.org/10.1109/MSP. 2018.2867638
Everitt, B.S. and Hand, D.J. (1981). Finite Mixture Distributions. 1st ed. Chapman & Hall, 143p. https://doi.org/10.1007/978-94-009-5897-5
Hogg, R.V., McKean, J.W., and Craig, A.T. (2019). Introduction to Mathematical Statistics. 8th ed. Pearson Education, 746p.
IQAir AirVisual. (2018). 2018 World Air Quality Report: Region & City PM2.5 Ranking. 22p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2018-en.pdf
IQAir AirVisual. (2019). 2019 World Air Quality Report: Region & City PM2.5 Ranking. 33p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2019-en.pdf
IQAir AirVisual. (2020). 2020 World Air Quality Report: Region & City PM2.5 Ranking. 41p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2020-en.pdf
IQAir AirVisual. (2021). 2021 World Air Quality Report: Region & City PM2.5 Ranking. 43p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2021-en.pdf
IQAir AirVisual. (2022). 2022 World Air Quality Report: Region & City PM2.5 Ranking. 47p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2022-en.pdf
IQAir AirVisual. (2023). 2023World Air Quality Report: Region & City PM2.5 Ranking. 45p. Available from: https://www.iqair.com/world-most-polluted-cities/world-air-quality-report-2023-en.pdf
Korolev, V., Gorshenin, A., Gulev, S., and Belyaev, K. (2015). Statistical modeling of air-sea turbulent heat fluxes by finite mixtures of Gaussian distributions. In: Dudin, A., Nazarov, A., and Yakupov, R. (eds). Information Technologies and Mathematical Modelling - Queueing Theory and Applications. ITMM 2015. Communications in Computer and Information Science, Springer, 564:152-162. https://doi.org/10.1007/978-3-319-25861-4_13
Krishnamoorthy, K. (2006). Handbook of Statistical Distributions with Applications. Taylor and Francis Group, NY, 344p. https://doi.org/10.1201/9781420011371
Kvam, P.H. and Vidakovic, B. (2007). Nonparametric Statistics with Applications to Science and Engineering. John Wiley & Sons, 420p. https://doi.org/10.1002/9780470168707
Lu, H.C. (2004). Estimating the emission source reduction of PM10 in central Taiwan. Chemosphere, 54(7):805-814. https://doi.org/10.1016/j.chemosphere.2003.10.012
Malá, I. (2016). On the residual duration of unemployment in the Czech Republic based on finite mixture of lognormal distributions. In APLIMAT 2016 - 15th Conference on Applied Mathematics 2016, p. 791-798.
McLachlan, G.J. and Peel, D. (2000). Finite Mixture Models. John Wiley & Sons, 419p. https://doi.org/10.1002/0471721182
McLachlan, G.J., Lee, S.X., and Rathnayake, S.I. (2019). Finite mixture models. Annual Review of Statistics and Its Application, 2019(6):355-378. https://doi.org/10.1146/annurev-statistics-031017-100325
Mijić, Z., Tasić, M., Rajšić, S., and Novaković, V. (2009). The statistical characters of PM10 in Belgrade area. Atmospheric Research, 92(4):420-426. https://doi.org/10.1016/j.atmosres.2009.01.002
Mishra, G., Ghosh, K., Dwivedi, A.K., Kumar, M., Kumar, S., Chintalapati, S., and Tripathi, S.N. (2021). An application of probability density function for the analysis of PM2.5 concentration during the COVID-19 lockdown period. Science of the Total Environment, 782:146681. https://doi.org/10.1016/j.scitotenv.2021.146681
Pollution Control Department. (2022). Air and Noise Pollution Situation and Management in Thailand, 2022. 66p. Available from: Ministry of Natural Resources and Environment. (In Thai)
Punzo, A., Mazza, A., and Maruotti, A. (2018). Fitting insurance and economic data with outliers: A flexible approach based on finite mixtures of contaminated gamma distributions. Journal of Applied Statistics, 45(14):2563-2584. https://doi.org/10.1080/02664763.2018.1428288
Shi, Y., Matsunaga, T., Yamaguchi, Y., Zhao, A., Li, Z., and Gu, X. (2018). Long-term trends and spatial patterns of PM2.5-induced premature mortality in South and Southeast Asia from 1999 to 2014. Science of the Total Environment, 631-632:1504-1514. https://doi.org/10.1016/j.scitotenv.2018.03.146
Tajuddin, R.R.M., Ismail, N., and Ibrahim, K. (2020). Several two-component mixture distributions for count data. Communications in Statistics: Simulation andComputation, 51(7):3760-3771. https://doi.org/10.1080/03610918.2020.1722834
Taylor, J.A., Jakeman, A.J., and Simpson, R.W. (1986). Modeling distributions of air pollutant concentrations -I. Identification of statistical models. Atmospheric Environment, 20(9):1781-1789. https://doi.org/10.1016/0004-6981(86)90127-7
Wang, X., Chen, R.J., Chen, B.H., and Kan, H.D. (2013). Application of statistical distribution of PM10 concentration in air quality management in 5 representative cities of China. Biomedical and Environmental Sciences, 26(8):638-646. https://doi.org/10.3967/0895-3988.2013.08.002
World Health Organization (WHO). (2021). WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon dioxide. World Health Organization, 273p.
