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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
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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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Analysis of Indoor PM2.5 Contaminants Based on Outdoor Wind Velocity Through Different Infiltration Types
(2025-01-01)
Oo, Ghaim Man
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Kotmool, Komsilp
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Mongkolwongrojn, Mongkol
Nowadays, indoor PM2.5 concentrations have become a significant factor affecting indoor air quality (IAQ) and a major public concern, particularly with the rise of haze in Thailand and globally, as PM2.5 can penetrate human lungs. This research analyzes the dispersion of PM2.5 from outdoors to indoors using a fluid dynamics simulation framework that combines the Eulerian approach for continuous flow, the Lagrangian approach for particulate matter dispersion, and the RNG k-ε turbulent model for airflow. The study is aimed at protecting indoor environments from harmful exposure to PM2.5 from outdoor contaminants and improving IAQ. Primarily, three different infiltration types and shapes are studied to determine the minimum optimal positive room pressure, based on the impact of ambient wind velocity on indoor PM2.5 concentrations from outdoor pollutants. A minimum optimal pressure of 3.6 Pa and 27 air changes per hour (ACH) is sufficient to achieve a PM2.5-free indoor environment for all infiltration models. Furthermore, higher wind speeds can reduce indoor PM2.5 concentrations due to the increased momentum of particles. This research technique is implemented in the practical field study conducted within the laboratory room of the 55-Year Chalermprakiat Building. As a result, the investigated room, with an infiltration area of 0.06 m<sup>2</sup> and a rate of 0.0036 m<sup>3</sup>/s, will be certified as a clean room by achieving an optimal minimum pressure of approximately 0.01 Pa. This research will help achieve cleaner and safer indoor environments for any building by leveraging the optimal minimum pressure of cleanroom technology, provided that the infiltration rate and ambient wind velocity are accurately determined.
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Characterization and proximate analysis of composite biochar briquette from oil palm frond and red clay
(2025-01-01)
Suwankamnoed, Saipin
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Imsuwan, Pattareewan
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Tilokkarn, Worrathon
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Meksiriporn, Bunyarit
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Sutjaritvorakul, Thanawat
The ultimate goal of this research is to create effective and environmentally friendly biochar briquettes from oil palm frond (OPF) waste. Red clay, a plentiful local resource, has been demonstrated to improve the properties of fuel briquettes. In this study, the tested composite briquettes (biochar:red clay:starch by weight) were prepared in the following ratios: 40:55:5 (T2), 50:45:5 (T3), 60:35:5 (T4), and 70:25:5 (T5), while the control sample (T1) consisted of 95:5 (biochar:starch). All of the different ratios were analyzed using proximate analysis. The results showed moisture content ranging from 7.35% to 8.47%, ash content from 29.75% to 49.82%, volatile matter from 15.63% to 17.47%, and fixed carbon from 25.31% to 46.48%. Among them, T4 exhibited the lowest moisture and ash content and the highest calorific value at 5,650 cal/g, making it the optimal candidate for further investigation. A comprehensive analysis of T4 was conducted using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). The techniques revealed the morphological structure and inorganic components that contribute to the energy efficiency of T4. The study also examined air pollutant emissions. T4 produced lower emissions of pollutants such as carbon monoxide (CO), carbon dioxide (CO<inf>2),</inf> total volatile organic compounds (TVOCs), formaldehyde (HCHO), and particulate matter (PM<inf>1.0</inf> and PM<inf>2.5</inf>) compared to the OPF biochar briquette without red clay (T1). Composite biochar briquettes made from OPF and red clay (T4) are safe and suitable for use as a fuel source for household applications like cooking and heating. This research contributes to the development of the green bioeconomy and supports sustainable energy initiatives.
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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
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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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PREDICTION OF AIR POLLUTION FROM POWER GENERATION USING MACHINE LEARNING
(2024-01-31)
Photsathian, Thongchai
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Suttikul, Thitiporn
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Tangsrirat, Worapong
Electrical energy is now widely recognized as an essential part of life for humans, as it powers many daily amenities and devices that people cannot function without. Examples of these include traffic signals, medical equipment in hospitals, electrical appliances used in homes and offices, and public transportation. The process that generates electricity can pollute the air. Even though natural gas used in power plants is derived from fossil fuels, it can nevertheless produce air pollutants involving particulate matter (PM), nitrogen oxides (NO<inf>x</inf>), and carbon monoxide (CO), which affect human health and cause environmental problems. Numerous researchers have devoted significant efforts to developing methods that not only facilitate the monitoring of current air quality but also possess the capability to predict the impacts of this increasing rise. The primary cause of air pollution issues associated with electricity generation is the combustion of fossil fuels. The objective of this study was to create three multiple linear regression models using artificial intelligence (AI) technology and data collected from sensors positioned around the energy generator. The objective was to precisely predict the amount of air pollution that electricity generation would produce. The highly accurate forecasted data proved valuable in determining operational parameters that resulted in minimal air pollution emissions. The predicted values were accurate with the mean squared error (MSE) of 0.008, the mean absolute error (MAE) of 0.071, and the mean absolute percentage error (MAPE) of 0.006 for the turbine energy yield (TEY). For the CO, the MSE was 2.029, the MAE was 0.791, and the MAPE was 0.934. For the NO<inf>x</inf>, the MSE was 69.479, the MAE was 6.148, and the MAPE was 0.096. The results demonstrate that the models developed have a high level of accuracy in identifying operational conditions that result in minimal air pollution emissions, with the exception of NO<inf>x</inf>. The accuracy of the NO<inf>x</inf> model is relatively lower, but it may still be used to estimate the pattern of NO<inf>x</inf> emissions.
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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
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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.
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Low-Cost PM 2.5 Sensor and Cost-Effective Air Purification Study for Household Implementation
(2022-01-01)
Janyasupab, Metini
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Yongwiwat, Jaturun
Particulate matters (PM) 2.5 is one of the most growing concerns during the past few years. Most effectively, air purifiers can reduce harmful PM in any closed spaces. However, economical affordability and health awareness still becomes a limit of device's viability, especially for lower middle-income countries. This study aims to investigate a low-cost PM 2.5 sensor (LCPMS) to quantify a cost-effective household solution of air purification by applying HEP A filter on a cooling fan. In evaluation of various factors involved in venting positions, filter layers, layout configurations, LCPMS can provide quantitative information to determine feasibility of cost-effective solution, reducing air pollution at the efficiency of 91.23% and 96.44% for 1 and 2 filters, respectively, in a range of 43 to 264 μg.m-3 PM2.5 level. Ultimately, the solution was validated on 22-inch conventional fan in 27-m2 room, explicitly confirming a substantial reduction of indoor air pollution by 64.4 % within 20 minutes at a cost less than 5 USD.

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