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Author: search.filters.author.Pochai, NopparatSubject: search.filters.subject.Bangkok

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    A Zero-dimensional Mathematical Model of PM2.5 Measurement due to Daily Vehicle Density in Bangkok
    (2025-05-01)
    Khum-Un, Seree
    ;
    Pochai, Nopparat
    Air pollution, particularly particulate matter smaller than 2.5 microns (PM2.5), has grown to be a serious issue that has an impact on people's health, especially the respiratory system. There are several studies that have found that the level of PM2.5 in the Bangkok region is high, as is how it affects individuals with respiratory illnesses. In this research, a numerical simulation of PM2.5 concentration was performed using a zero-dimensional model of PM2.5 measurement due to the daily vehicle density in Bangkok. It is evident that the wind speed and daily vehicle density have an impact on the simulated PM2.5 concentration in Bangkok. The daily density of vehicles greatly influences PM2.5 emissions. Wind speed was measured in this experiment. The hourly vehicle density in Bangkok, which was represented by calculating functions for wind speed and PM2.5 emission rate, is what produces the computed PM2.5 emission rate. The simulation includes three 24-hour scenarios: low vehicle density with medium wind speed, high vehicle density with low wind speed, and medium vehicle density with high wind speed. All of the models indicated that the PM2.5 level would drop as wind speed increased and vehicle density decreased. The daily vehicle density and wind speed are two factors that affect the PM2.5 level. Focusing, especially on wind speed, will not always lead to PM2.5 reductions. However, daily vehicle density also has a significant role in PM2.5 management. Wind speed and vehicle density influence PM2.5 concentrations, with three scenarios demonstrating that higher wind speed and lower vehicle density reduce PM2.5 levels. While wind speed helps to reduce PM2.5 levels, vehicle density also has a substantial impact on emissions. Managing PM2.5 requires addressing both daily vehicle density and wind speed, as focusing on only wind speed may not always result in reductions.
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    An k-Nearest Neighbors Machine Learning Algorithm for the PM2.5 Early Warning System in Bang Khun Tian, Bangkok, Thailand
    (2024-12-02)
    Thongtha, Kaboon
    ;
    Pochai, Nopparat
    The problem of particulate matter with a diameter of less than 2.5-10 microns, such as PM2.5-PM10 in Bangkok, affects the health of people because there are small particles that can penetrate deep into the alveoli. If there is an early warning system to warn people about the harmful levels of PM2.5 in Bangkok, such as an early warning of 2-3 days, it can help the people have time to prevent themselves. In this research, an early warning system to warn people about the harmful levels of PM2.5 in Bangkok is proposed. The air quality data of the Bang Khun Tian station, Bangkok, for 2 months, from December 1, 2020, to January 31, 2021, were selected because the area is an air-quality-worrying area. A proposed early warning system for the harmful levels of PM2.5 around Bang Khun Tian, Bangkok, was developed using the k-nearest neighbors machine learning algorithm. As the results show, the proposed technique gives an agreeable prediction for the earliest warning by 4 days.