Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS

Assessing developmental and transcriptional effects of PM2.5 on zebrafish embryos

Investigating fine particulate matter (PM2.5) toxicity is crucial for health risk assessment and pollution control. This study explores the developmental toxicity of two PM2.5 sources: standard reference material 2786 (NIST, USA) and PM2.5 from Chakri Naruebodindra Medical Institute (CNMI, Thailand) located in the Bangkok Metropolitan area. Zebrafish embryos exposed to these samples exhibited embryonic mortality, with 50% lethal concentration (LC50) values of 1476 µg/mL for standard PM2.5 and 512 µg/mL for CNMI PM2.5. Morphological analysis revealed malformations, including pericardial and yolk sac edema, and blood clotting in both groups. Gene expression analysis highlighted source-specific effects. Standard PM2.5 downregulated sod1 and cat while upregulating gstp2. Inflammatory genes tnf-α and il-1b were upregulated, and nfkbi-αa was downregulated. Apoptosis-related genes bax, bcl-2, and casp3a were downregulated. CNMI PM2.5 consistently downregulated all examined genes. These findings underscore PM2.5 source variability's significance in biological system impact assessment, providing insights into pollutant-gene expression interactions. The study emphasizes the need for source-specific risk assessment and interventions to address PM2.5 exposure's health impacts effectively.

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.

Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options

Many of the current atmospheric environmental problems facing Thailand are linked to air pollution that is largely derived from biomass burning. Different parts of Thailand have distinctive sources of biomass emissions that affect air quality. The main contributors to atmospheric particulate matter (PM), especially the PM_2.5 fraction in Thailand, were highlighted in a recent study of PM derived from biomass burning. This review is divided into six sections. Section one is an introduction to biomass burning in Thailand. Section two covers issues related to biomass burning for each of the four main regions in Thailand, including Northern, Northeastern, Central, and Southern Thailand. In northern Thailand, forest fires and the burning of crop residues have contributed to air quality in the past decade. The northeast region is mainly affected by the burning of agricultural residues. However, the main contributor to PM in the Bangkok Metropolitan Region is motor vehicles and crop burning. In Southern Thailand, the impact of agoindustries, biomass combustion, and possible agricultural residue burning are the primary sources, and cross-border pollution is also important. The third section concerns the effect of biomass burning on human health. Finally, perspectives, new challenges, and policy recommendations are made concerning improving air quality in Thailand, e.g., forest fuel management and biomass utilization. The overall conclusions point to issues that will have a long-term impact on achieving a blue sky over Thailand through the development of coherent policies and the management of air pollution and sharing this knowledge with a broader audience.

Temporal, compositional, and functional differences in the microbiome of Bangkok subway air environment

With the growing numbers of the urban population, an increasing number of commuters have relied on subway systems for rapid transportation in daily life. Analyzing the temporal distribution of air microbiomes in subway environments is crucial for the assessment and monitoring of air quality in the subway system, especially with regard to public health. This study employed culture-independent metabarcode sequencing to analyze bacterial diversity and variations in bacterial compositions associated with bioaerosols collected from a subway station in Bangkok over a four-month period. The bacteria obtained were found to consist primarily of Proteobacteria, Firmicutes, and Actinobacteria, with variations at the family, genus, and species levels among samples obtained in different months. The vast majority of these bacteria are most likely derived from outside environments and human body sources. Many of the bacteria found in Bangkok subway station were also identified as "core microorganisms" of subway environments around the world, as suggested by the MetaSUB Consortium. The diversity of bacterial communities was shown to be influenced by several air quality variables, especially ambient temperature and the quantity of particulate matters, which showed positive correlations with several bacterial species such as Acinetobacter lwoffii, Staphylococcus spp., and Moraxella osloensis. In addition, metabolic profiles inferred from metabarcode-derived bacterial diversity showed significant variations across different sampling times and sites and can be used as a starting point to further explore the functional roles of specific groups of bacteria in the subway environment. This study thus introduced the information required for surveillance of microbiological impacts and their contributions to the well-being of subway commuters in Bangkok.