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Distribution and chemical composition of atmospheric aerosols over the Gulf of Thailand during the southwest monsoon
(2026-05-01)
Kayee, Jariya
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Sompongchaiyakul, Penjai
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Das, Reshmi
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Wang, Xianfeng
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Chinfak, Narainrit
Marine aerosols were collected over the Gulf of Thailand (GOT) during the 2018 southwest monsoon on board the M.V. SEAFDEC2 while sailing. Water-soluble inorganic ions (i.e., Na+, NH<inf>4</inf>+, K+, Mg2+, Ca2+, Cl−, NO<inf>3</inf>−, and SO<inf>4</inf>2−) and elemental concentrations (i.e., Al, As, Ba, Ca, Cd, Cu, Cr, Fe, Mg, Mn, Na, Ni, Pb, Sr, V, and Zn) were determined to investigate their distribution patterns in order to evaluate the influence of particle sources on the chemical properties of atmospheric aerosols. During the southwest monsoon, the wind predominantly blows over the Indian Ocean to the GOT. Correspondingly, our results indicated that coarse particles are dominated by sea salts. Cl− and Na+ are the major ions, accounting for ∼64.8% (range: 51.2–84.3%) of total ions in the coarse mode particles. The strong correlation between Cl− and Na+ ( r = 0.999) along with the Cl/Na ratio (1.5, range: 0.66–2.00) suggest sea salt origin. Enrichment factor (EF) values indicate that elements are originated from oceanic, crustal, and anthropogenic sources. The oceanic source (Sr, Ca, Mg and Na) explains 95% (range: 81–99%) of total elements in the aerosols. Elements such as Al, Fe, Mn and Ba explain 4.6% of the total elements. These elements associate with the crustal source. Only a minimal amount (0.63%, range: 0.02–4.5%) of the total elements is originated from anthropogenic activities over or nearby the GOT as shown by loading of As, Cd, Cu, Cr, Ni, Pb, V and Zn.
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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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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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PM 2.5 Prediction & Air Quality Classification UsinMachine Learning
(2024-06-01)
Soontornpipit, Pichitpong
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Lekawat, Lertsak
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Tritham, Chatchai
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Tritham, Chattabhorn
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Pongpaibool, Pornanong
Forecasting plays a vital role in air pollution alerts and the management of air quality. Studies and observations conducted in Thailand indicate a concerning rise in pollution levels, particularly in the concentration of PM2.5. concentrations. Bangkok, in particular, has been flagged for its alarmingly high PM2.5 By projecting the future PM2.5 concentrations in these urban areas, we can obtain valuable short-term predictive information regarding air quality. After conducting experiments using four different machine learning algorithms, it was found that the LSTM (Long Short-Term Memory) model provides the most accurate forecasts based on various statistical evaluation indicators. These indicators include a Root Mean Square Error (RMSE) of 2.74, Mean Absolute Error (MAE) of 1.97, R-squared value of 0.94, and Mean Absolute Percentage Error (MAPE) of 10.53. Then the classified air quality based on PM2.5 from the LSTM model gives the best performance indicators including accuracy = 0.9072, precision = 0.8466, negative predict value = 0.9403, sensitivity = 0.8144, specificity = 0.9381, and F1-score = 0.8169. The results show that the machine learning model can predict PM2.5 concentration, which is suitable for early warning of pollution and information provision for air quality management systems in Bangkok.
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Bangkok school indoor air quality: monitoring and intervention by positive pressure fresh air system
(2024-04-01)
Ongwandee, Maneerat
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Khianthongkul, Kiraphat
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Panyametheekul, Sirima
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Yongprapat, Kamomchai
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Srinaka, Kessara
A PM<inf>2.5</inf> crisis in Thailand has caused the Thai government and public to be increasingly concerned about children’s exposure to PM<inf>2.5</inf> during time in school. This study is a part of a project to create a modeled effective school indoor air quality management for the Bangkok Metropolitan Administration (BMA). We measured air quality and environment in 10 Bangkok school rooms, including CO<inf>2</inf>, CO, O<inf>3</inf>, PM<inf>2.5</inf>, PM<inf>10</inf>, TVOC<inf>PID</inf>, formaldehyde, airborne bacteria and fungi, and gaseous organic contaminants. The indoor-to-outdoor concentration ratios indicated that either outdoor sources or indoor + outdoor sources were the predominant contributors to PM in naturally ventilated classrooms. Meanwhile, PM levels in air-conditioned classrooms strongly depended on class activities. CO<inf>2</inf> measurements showed that the air-conditioned classrooms had a low 0.4 per hour air change rate and total fungal counts also reached 800 CFU m−3. Analysis of gaseous organic compounds showed that the two most abundant were aliphatic and aromatic hydrocarbons, accounting for 60% by mass concentration. Interestingly, 2‐ethyl‐1‐hexanol, a mucous membrane irritant, was detected in all study rooms. In one naturally ventilated classroom, we implemented a positive pressure fresh air system to mitigate in-class PM levels; it kept PM levels below 20 μg m−3 throughout the class day. Students reported a 20–37% increase in satisfaction with the perceived indoor environmental quality and reported reduced rates in all symptoms of the sick building syndrome after implementing the positive pressure system.
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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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Source apportionment of PM2.5 in Thailand’s deep south by principal component analysis and impact of transboundary haze
(2023-08-01)
Chaisongkaew, Phatsarakorn
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Dejchanchaiwong, Racha
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Inerb, Muanfun
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Mahasakpan, Napawan
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Nim, Nobchonnee
Atmospheric particulate matter smaller than 2.5 micron (PM<inf>2.5</inf>) was evaluated at four sites in the lower southern part of Thailand during 2019–2020 to understand the impact of PM<inf>2.5</inf> transport from peatland fires in Indonesia on air quality during the southwest monsoon season. Mass concentration and chemical bound-PM, including carbon composition, e.g., organic carbon (OC) and elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and inorganic elements, were analyzed. The PM<inf>2.5</inf> emission sources were identified by principal components analysis (PCA). The average mass concentrations of PM<inf>2.5</inf> in the normal period, which represents clean background air, from four sites was 3.5–5.1 µg/m3, whereas during the haze period, it rose to 5.4–13.5 µg/m3. During the haze period, both OC and EC were 3.5 times as high as in the normal period. The average total PAHs and BaP-TEQ of PM<inf>2.5</inf> during the haze period were ~ 1.3–1.7 and ~ 1.2–1.9 times higher than those in the normal period. The K concentrations significantly increased during haze periods. SO<inf>4</inf>2− dominated throughout the year. The effects of external sources, especially the transboundary haze from peatland fires, were significantly enhanced, because the background air in the study locations was generally clean. PCA indicated that vehicle emission, local biomass burning, and secondary particles played a key role during normal period, whereas open biomass burning dominated during the haze phenomena. This was consistent with the OC/EC and PAH diagnostic ratios. Backward trajectories confirmed that the sources of PM during the haze period were predominantly peatland fires in Sumatra, Indonesia, due to southwest wind.

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