Nanoparticles (NPs), nanoaerosols (NAs), ultrafine particles (UFPs), and PM_0.1 (diameters ≤ 0.1 µm or 100 nm) are used interchangeably in the field of atmospheric studies. This review article summarizes recent research on PM_0.1 in Thailand. The review involved peer-reviewed papers that appeared in the Scopus and the Web of Science databases and included the most recently published articles in the past 10 years (2013–2022). PM_0.1 mainly originate from combustion processes such as in motor vehicles. The highest mass concentration of PM_0.1 occurs during the dry season, in which open fires occur in some regions of Thailand. The northern area of the country has higher PM_0.1 mass concentrations, followed by the central and southern areas. Carbonaceous nanoaerosols are produced during normal periods, and the proportions of organic to elemental carbon and char to soot suggest that these originate from motor vehicles. However, in haze periods, biomass fires can also produce carbon-containing particles. PM_0.1 pollution from local and cross-border countries also needs to be considered. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience.

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM_2.5 levels and excessive PM_2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM_2.5 levels. The software was trained to predict PM_2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM_2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM_2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM_2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas.

In Bangkok, the megacity of Thailand, concentrations of fine particulate matter (PM_2.5) have often exceeded the National Ambient Air Quality standards. During severe smog events over Bangkok, the air quality has exhibited moderate to unhealthy atmospheric conditions, according to the air quality index of the United States. To investigate the formation of secondary organic aerosols (SOA), a field campaign to estimate secondary organic carbon (SOC) in Bangkok using the EC tracer method was conducted in January 2021, when the concentrations of PM_2.5 were high. The monthly period was classified into three pollution groups, including high pollution, high PM, and low pollution events. The study showed that the correlations between PM_2.5 and O₃ were negative during both the daytime and night-time. The OC/EC ratios varied from 4.32 to 5.43, while the moderate OC/EC values implied that fossil fuel combustion was the major carbonaceous aerosol in Bangkok. The EC tracer-estimated SOC and POC showed that SOC contributed between 32.5 and 46.4% to OC, while the highest SOC contribution occurred during the low pollution event. The heightened formation of SOA during the low pollution event was perhaps owing to the levels of oxides of nitrogen (NO_x). Since Bangkok is more likely to have a NO_x-rich photochemical reaction regime, an increase in the NO_x level tended to decrease the SOA yield ([NO_x] was 21.6 ppb, 20.8 ppb, and 17.1 ppb during the high pollution, high PM, and low pollution events, respectively). Together with the high humidity and high light intensity during the low pollution event, the SOA formation was enhanced. Even though the driving factors of SOA formation over Bangkok remain unclear, the results of this study reveal the significance and urgency of local actions to reduce NO_x and O₃ towards more habitable and sustainable urban environments.

Agricultural waste burning is a major source of fine particulate matter (FPM), which remains in long-term suspension in the atmosphere during the aging processes. This research studied changes in the size, morphology, and chemical composition of FPM produced from rice straw and sugarcane leaf burning and the effects of relative humidity (RH) on the aging process. The FPM was measured using a scanning mobility particle sizer and the particle mass was collected for morphological (transmission electron microscopy) and chemical (organic and elemental carbon, and water-soluble ions) analyses during 3, 6, and 9 h of aging. The geometric mean diameter (GMDN) of FPM from biomass burning were 95 ± 4 nm (Aitken mode) and 133 ± 40 nm (accumulation mode). The FPM changed from non-uniform and aggregate shapes in fresh particles into chain aggregates in 9 h-aged particles. The dominant chemical components of FPM were OC2, OC3, NO3–, Na+, and K+ under low RH condition (60%). Under high RH condition (90%), the FPM had a larger GMDN and a significant influence on its morphology, as observed in coated spherical or agglomerated shapes. The results showed that the aging process, RH, and aging duration were important factors affecting the size, shape, and chemical composition of aged particles.

Volatile organic compounds (VOCs) play an important role in atmospheric chemistry due to their high reactivity—reacting photochemically with oxides of nitrogen (NOx) in the presence of solar radiation forming tropospheric ozone (O3). Each VOC species have different effects on ozone formation according to the rates and pathways of their reactions. The objective of this study aims to examine ozone formation from the estimation of ozone formation potential (OFP). The observation of 29 VOCs species was carried out in the urban area near the roads of Bangkok, Thailand. Measurements were carried out during the dry season, from 16th February to 15th March, 2018. The air samples were analyzed using gas chromatography flame ionization detector (GC-FID). The results showed that toluene had the highest VOCs concentration followed by propane, and carbon tetrachloride (CCl4). The average ratio of benzene to toluene (B/T) and toluene to benzene (T/B) indicate that both toluene and benzene emitted from industrial area and vehicular emission. Ratio of m/p-xylene to benzene (m/p-X/B) indicate that BTEX emitted far from the source. The ozone formation potential indicated that toluene was the main VOC contributing to the total ozone formation. High VOCs concentration in monitoring site was influenced by vehicular sources and the sea breeze brought the pollutants back to the land.