1. Home
  2. Vol 8, No 5 (2024)
  3. Guayjarernpanishk

Air Pollution Forecasting in a Regional Context for Sustainable Management

Pannarat Guayjarernpanishk, Nipaporn Chutiman, Narumol Piwpuan, Butsakorn Kong-ied, Monchaya Chiangpradit

Abstract


The aim of this research was to develop and apply a statistical model that can be used to forecast long-term daily maximum particulate matter with a diameter of less than 2.5 microns (PM2.5) concentrations. In order to predict the daily maximum PM2.5 concentrations in the northeastern region of Thailand, the extreme value theory was analyzed, and an appropriate distribution model was identified by employing the Generalized Pareto distribution (GPD). The data of daily maximum PM2.5 concentrations during the years 2021–2023 obtained from six stations was used. These stations are located in Khon Kaen, Loei, Nakhon Ratchasima, Nong Khai, Nakhon Phanom, and Ubon Ratchathani provinces. The results of this study reveal that the GPD is appropriate based on the results of Kolmogorov-Smirnov Statistics Test. Estimating the return levels during the following return periods: 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years showed that the area in the upper northeastern region, particularly Loei and Nakhon Phanom, has daily maximum PM2.5 concentrations above 500 micrograms per cubic meter. These results can also be used as information to support decision-making when conducting response planning in high-risk areas, which can be helpful for efficient resource planning and prevention actions.

 

Doi: 10.28991/ESJ-2024-08-05-024

Full Text: PDF


Keywords


Extreme Value Theory; Peak Over Threshold; Generalized Pareto Distribution; Air Pollution; PM2.5.

References


Yin, S., Wang, X., Zhang, X., Guo, M., Miura, M., & Xiao, Y. (2019). Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016. Environmental Pollution, 254, 112949. doi:10.1016/j.envpol.2019.07.117.

Amnuaylojaroen, T., Inkom, J., Janta, R., & Surapipith, V. (2020). Long range transport of Southeast Asian PM2.5 pollution to northern thailand during high biomass burning episodes. Sustainability (Switzerland), 12(23), 1–14. doi:10.3390/su122310049.

Amnuaylojaroen, T. (2022). Prediction of PM2.5in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model. Advances in Meteorology, 2022, 9. doi:10.1155/2022/3190484.

Amnuaylojaroen, T., Barth, M. C., Emmons, L. K., Carmichael, G. R., Kreasuwun, J., Prasitwattanaseree, S., & Chantara, S. (2014). Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia. Atmospheric Chemistry and Physics, 14(23), 12983–13012. doi:10.5194/acp-14-12983-2014.

Lee, H. H., Iraqui, O., Gu, Y., Yim, S. H. L., Chulakadabba, A., Tonks, A. Y. M., Yang, Z., & Wang, C. (2018). Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia. Atmospheric Chemistry and Physics, 18(9), 6141–6156. doi:10.5194/acp-18-6141-2018.

Lee, H. H., Iraqui, O., & Wang, C. (2019). The Impact of Future Fuel Consumption on Regional Air Quality in Southeast Asia. Scientific Reports, 9(1), 2648. doi:10.1038/s41598-019-39131-3.

Zafra, C., Ángel, Y., & Torres, E. (2017). ARIMA analysis of the effect of land surface coverage on PM10 concentrations in a high-altitude megacity. Atmospheric Pollution Research, 8(4), 660–668. doi:10.1016/j.apr.2017.01.002.

Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., Tan, H., Lin, D., & Wang, J. (2018). Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model. Ecological Indicators, 95, 702–710. doi:10.1016/j.ecolind.2018.08.032.

Zhao, L., Li, Z., & Qu, L. (2022). Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon, 8(12), 12239. doi:10.1016/j.heliyon.2022.e12239.

Bhatti, U. A., Yan, Y., Zhou, M., Ali, S., Hussain, A., Qingsong, H., Yu, Z., & Yuan, L. (2021). Time Series Analysis and Forecasting of Air Pollution Particulate Matter (PM2.5): An SARIMA and Factor Analysis Approach. IEEE Access, 9, 41019–41031. doi:10.1109/ACCESS.2021.3060744.

Agarwal, A., & Sahu, M. (2023). Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions. Environmental Monitoring and Assessment, 195(4), 502. doi:10.1007/s10661-023-11045-8.

Li, X., Feng, Y. J., & Liang, H. Y. (2017). The Impact of Meteorological Factors on PM2.5 Variations in Hong Kong. IOP Conference Series: Earth and Environmental Science, 78, 12003. doi:10.1088/1755-1315/78/1/012003.

Miao, Y., Liu, S., Guo, J., Yan, Y., Huang, S., Zhang, G., Zhang, Y., & Lou, M. (2018). Impacts of meteorological conditions on wintertime PM2.5 pollution in Taiyuan, North China. Environmental Science and Pollution Research, 25(22), 21855–21866. doi:10.1007/s11356-018-2327-1.

Amnuaylojaroen, T. (2022). Prediction of PM2.5in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model. Advances in Meteorology, 2022, 9. doi:10.1155/2022/3190484.

Saiohai, J., Bualert, S., Thongyen, T., Duangmal, K., Choomanee, P., & Szymanski, W. W. (2023). Statistical PM2.5 Prediction in an Urban Area Using Vertical Meteorological Factors. Atmosphere, 14(3), 15. doi:10.3390/atmos14030589.

Jayamala, R., Shanmugapriya, N., Lalitha, K., & Vijayarajan, P. (2023). A deep learning model and optimization algorithm to forecasting environment monitoring of the air pollution. Global Nest Journal, 25(10), 47–55. doi:10.30955/gnj.004759.

Barid, A. J., & Hadiyanto, H. (2024). Hyperparameter optimization for hourly PM2.5 pollutant prediction. Journal of Emerging Science and Engineering, 2(1), e15. doi:10.61435/jese.2024.e15.

Mohammadi, F., Teiri, H., Hajizadeh, Y., Abdolahnejad, A., & Ebrahimi, A. (2024). Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran. Scientific Reports, 14(1), 2109. doi:10.1038/s41598-024-52617-z.

Chutiman, N., Busababodhin, P., P. Senapeng, P., Phoophiwfa, T. & Chiangpradit, M. (2019). Analysis Extreme Weather Event of Thailand with Spatial Modeling Theory of Extreme values: Case study in Mekong, Chi, Mun River Basin. Advances and Applications in Mathematical Sciences, 19(2), 129–138.

Busababodhin, P., Chiangpradit, M., Papukdee, N., Ruechairam, J., Ruanthaisong, K., & Guayjarernpanishk, P. (2021). Extreme Value Modeling of Daily Maximum Temperature with the r-Largest Order Statistics. The Journal of Applied Science, 20(1), 28–38. doi:10.14416/j.appsci.2021.01.003.

Gong, X., Wang, X., Li, Y., Ma, L., Li, M., & Si, H. (2022). Observed Changes in Extreme Temperature and Precipitation Indices on the Qinghai-Tibet Plateau, 1960–2016. Frontiers in Environmental Science, 10, 888937. doi:10.3389/fenvs.2022.888937.

Guayjarernpanishk, P., Chiangpradit, M., Kong-Ied, B., & Chutiman, N. (2023). Climate Forecasting Models for Precise Management Using Extreme Value Theory. Civil Engineering Journal (Iran), 9(7), 1753–1767. doi:10.28991/CEJ-2023-09-07-014.

Contzen, J., Dickhaus, T., & Lohmann, G. (2023). Long-term temporal evolution of extreme temperature in a warming Earth. PLoS ONE, 18(2), 280503. doi:10.1371/journal.pone.0280503.

Liu, H., Yang, F., & Wang, H. (2023). Research on Threshold Selection Method in Wave Extreme Value Analysis. Water (Switzerland), 15(20), 3648. doi:10.3390/w15203648.

Maruyama, F. (2023). Analyzing of the ENSO Index Using Extreme Value Theory. Journal of Geoscience and Environment Protection, 11(06), 96–105. doi:10.4236/gep.2023.116007.

Canton Enriquez, D., Niembro-Ceceña, J. A., Muñoz Mandujano, M., Alarcon, D., Arcadia Guerrero, J., Gonzalez Garcia, I., Montes Gutierrez, A. A., & Gutierrez-Lopez, A. (2022). Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset. Data in Brief, 40, 107783. doi:10.1016/j.dib.2021.107783.

Chutiman, N., Guayjarernpanishk, P., Chiangpradit, M., Busababodhin, P., Rattanawan, S., Phoophiwfa, T., & Kong-ied, B. (2022). The Risk Area Assessment on Re-emerging Diseases in Elderly People by Using Extreme Value Theory. International Journal of Mathematics and Computer Science, 17(1), 243–253.

de Haan, L., Mercadier, C., & Zhou, C. (2016). Adapting extreme value statistics to financial time series: dealing with bias and serial dependence. Finance and Stochastics, 20(2), 321–354. doi:10.1007/s00780-015-0287-6.

Gkillas, K., & Katsiampa, P. (2018). An application of extreme value theory to cryptocurrencies. Economics Letters, 164, 109–111. doi:10.1016/j.econlet.2018.01.020.

Chavez-Demoulin, V., Embrechts, P., & Hofert, M. (2016). An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates. Journal of Risk and Insurance, 83(3), 735–776. doi:10.1111/jori.12059.

Chavez-Demoulin, V., & Guillou, A. (2018). Extreme quantile estimation for β-mixing time series and applications. Insurance: Mathematics and Economics, 83, 59–74. doi:10.1016/j.insmatheco.2018.09.004.

Einmahl, J. J., Einmahl, J. H. J., & de Haan, L. (2019). Limits to Human Life Span Through Extreme Value Theory. Journal of the American Statistical Association, 114(527), 1075–1080. doi:10.1080/01621459.2018.1537912.

Tamošaitienė, J., Yousefi, V., & Tabasi, H. (2021). Project portfolio construction using extreme value theory. Sustainability (Switzerland), 13(2), 1–13. doi:10.3390/su13020855.

Drees, H., de Haan, L., & Turkman, F. (2018). Extreme value estimation for discretely sampled continuous processes. Extremes, 21(4), 533–550. doi:10.1007/s10687-018-0313-0.

Einmahl, J. H. J., Li, J., & Liu, R. Y. (2015). Bridging centrality and extremity: Refining empirical data depth using extreme value statistics. Annals of Statistics, 43(6), 2738–2765. doi:10.1214/15-AOS1359.

Vignotto, E., & Engelke, S. (2020). Extreme value theory for anomaly detection – the GPD classifier. Extremes, 23(4), 501–520. doi:10.1007/s10687-020-00393-0.

Just, A. C., Wright, R. O., Schwartz, J., Coull, B. A., Baccarelli, A. A., Tellez-Rojo, M. M., Moody, E., Wang, Y., Lyapustin, A., & Kloog, I. (2015). Using High-Resolution Satellite Aerosol Optical Depth to Estimate Daily PM2.5 Geographical Distribution in Mexico City. Environmental Science & Technology, 49(14), 8576–8584. doi:10.1021/acs.est.5b00859.

Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014). Estimating ground-level PM2.5 in China using satellite remote sensing. Environmental Science & Technology, 48(13), 7436–7444. doi:10.1021/es5009399.

Coles, S. and Nadaraja, S. (2001). An Introduction to statistical modeling of extreme values. Springer, London. United Kingdom. doi:10.1007/978-1-4471-3675-0.

Zhang, J. (2002). Powerful Goodness-of-fit Tests Based on the Likelihood Ratio. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(2), 281–294. doi:10.1111/1467-9868.00337.


Full Text: PDF

DOI: 10.28991/ESJ-2024-08-05-024

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Monchaya Chiangpradit, Nipaporn Chutiman, Butsakorn Kong-ied, Narumol Piwpuan, Pannarat Guayjarernpanishk