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.

Of late, air pollution in Asia has increased, particularly in built-up areas due to rapid industrialization and urbanization. The present study sets out to examine the impact that pollution can have on the health of people living in the inner city of Bangkok, Thailand. Consequently, in 2021, fine particulate matter (PM_2.5) and coarse particulate matter (PM₁₀) chemical composition and sources are evaluated at three locations in Bangkok. To identify the possible sources of such particulates, therefore, the principal component analysis (PCA) technique is duly carried out. As determined via PCA, the major sources of air pollution in Bangkok are local emission sources and sea salt. The most significant local sources of PM_2.5 and PM₁₀ in Bangkok include primary combustion, such as vehicle emissions, coal combustion, biomass burning, secondary aerosol formation, industrial emissions, and dust sources. Except for the hazard quotient (HQ) of Ni and Mn of PM_2.5 for adults, the HQ values of As, Cd, Cr, Mn, and Ni of both PM_2.5 and PM₁₀ were below the safe level (HQ = 1) for adults and children. This indicates that exposure to these metals would have non-carcinogenic health effects. Except for the carcinogenic risk (HI) value of Cr of PM_2.5 and PM₁₀, which can cause cancer in adults, at Bangna and Din Daeng, the HI values of Cd, Ni, As, and Pb of PM_2.5 and PM₁₀ are below the limit set by the U.S. Environmental Protection Agency (U.S. EPA). Ni and Mn pose non-carcinogenic risks, whereas Cr poses carcinogenic risks to adults via inhalation, a serious threat to the residents of Bangkok.

This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM_2.5 over Greater Bangkok (GBK). Six ML models namely random forest (RF), adaptive boosting (ADB), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and cat boosting (CB) were used with meteorological factors, fire activity, land use, and socio-economic data as predictor variables. The LGBM outperformed other models achieving ρ = 0.9 (0.95), MBE = 0 (− 0.01), MAE = 5.5 (3.3) μg m⁻³, and RMSE = 8.7 (4.9) μg m⁻³ for hourly (daily) PM_2.5 prediction. LGBM was used for spatiotemporal PM_2.5 estimation, and meteorological normalization was applied to calculate PM_{2.5_emis} (emission-related PM_2.5) and PM_{2.5_met} (meteorology-related PM_2.5). Diurnal variation reveals higher PM_2.5 levels in the morning (08–10 LT) due to increased traffic emissions and thermal inversion and a decrease in PM_2.5 as the day progresses due to decreased emission and inversion dissipation. Monthly variation suggests higher PM_2.5 in winter (December and January) due to emissions and stagnant meteorological conditions. Negative PM_{2.5_met} during November, March, and April values show meteorology improves air quality, while positive values from December to February indicate stagnant winter conditions worsen it. During winter, PM_{2.5_emis} and PM_2.5 showed an increasing trend in 15.6% and 67.8% of the area while decreasing trends fell from 23.2 to 1.9%. In summer, the percentage of areas with an increasing trend rose from 18.7 to 34.6%, and decreasing areas fell from 12.6 to 6.5%. Increase in PM_2.5 despite decreasing emission over a larger area, indicating limited effectiveness of mitigation measures. Winter exhibits greater PM_2.5 variability due to episodic increases from changing meteorological conditions. In Bangkok and nearby areas, higher variability is mainly driven by meteorology, with more consistent emissions in Bangkok compared to rural areas affected by agricultural burning. PM_2.5 and PM_{2.5_emis} showed stronger persistence in winter than in summer, with weaker effects in Bangkok. Hurst exponent averages were 0.75, 0.76, and 0.72 for PM_2.5 and 0.79, 0.8, and 0.73 for PM_{2.5_emis} in dry, winter, and summer seasons, respectively. SHAP analysis suggested relative humidity, planetary boundary layer height, v wind, temperature, u wind, global radiation, and aerosol optical depth as the key variables affecting PM_2.5 with mean absolute SHAP values of 5.29, 4.79, 4.29, 3.68, 2.37, 2.22, and 2.03, respectively. Based on these findings, some policy recommendations have been proposed.