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Author: search.filters.author.Thongtha, KaboonSubject: search.filters.subject.PM2.5

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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.
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    A PM2.5 Forewarning Algorithm Using k-Nearest Neighbors Machine Learning at Changpuek, Chiang Mai, Thailand
    (2023-08-29)
    Pochai, Nopparat
    ;
    Thongtha, Kaboon
    In Chiang Mai, Thailand, the air pollution issue caused by atmospheric particulate matter with a diameter of less than 2.5 μm, or PM2.5, has been identified as an ongoing crisis. PM2.5 not only has a direct impact on people's health and way of life, but it also has a negative impact on the national economy. Residents in such PM2.5-polluted locations are particularly susceptible to respiratory diseases, skin diseases, inflammatory eye diseases, and cardiovascular problems. As a result, this study is going to analyze PM2.5 data using the k-nearest neighbors machine learning algorithm as a guideline to warn people, particularly in Changpuek, Chiang Mai, Thailand, to handle the PM2.5 characterization problem.