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<p>This study characterizes the seasonal variations and cross-boundary aerosol transport in central Thailand using a combination of low-cost PM<sub>2.5</sub> sensors, sunphotometer observations, and Himawari-9 satellite aerosol products. PM<sub>2.5</sub> data were collected by low-cost AEROBOX air quality sensors (LCASs) at four sites in central Thailand (Bangkok, Nakhon Pathom, Sra Kaeo, and Chachoengsao) between September 2022 and June 2024. A performance evaluation conducted against a reference-grade ADR-1500 instrument showed strong agreement (R<sup>2</sup> = 0.96), confirming the suitability of LCASs for regional PM<sub>2.5</sub> monitoring. The results show that PM<sub>2.5</sub> concentrations were consistently higher during the winter season and lower during the rainy season at all sites. The highest mean PM<sub>2.5</sub> levels were observed at the high-density population Nakhon Pathom site (59.0 ± 22.9&#xa0;µg/m<sup>3</sup>), exceeding Thailand’s National Ambient Air Quality Standard (NAAQS) of 37.5&#xa0;µg/m<sup>3</sup>, while lower levels were observed at the rural sites of Sra Kaeo (36.8 ± 13.8&#xa0;µg/m<sup>3</sup>) and Chachoengsao (36.1 ± 11.1&#xa0;µg/m<sup>3</sup>). A case study conducted from 14 to 18 January 2023 demonstrates the combined use of Cimel aerosol optical depth (AOD), Himawari-9 aerosol optical thickness (AOT), ERA5 wind fields, and HYSPLIT backward trajectories to identify aerosol sources and transport pathways affecting central Thailand. During this episode, elevated PM<sub>2.5</sub> and AOD were associated with biomass-burning emissions transported from eastern Thailand and Cambodia under southeasterly flow, which mixed with local pollutants under stable atmospheric conditions. This study highlights the effectiveness of combining low-cost sensors with satellite observations and trajectory analyses for improving PM<sub>2.5</sub> characterization in regions with limited monitoring networks.</p> Graphical Abstract <p></p>

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Characterizing PM2.5 Variability and Cross-Boundary Aerosol Transport in Central Thailand Using Low-Cost Sensors and Himawari-9 Data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Worapan Kanchanachat,
  • Somjet Pattarapanitchai,
  • Sheng-Hsiang Wang,
  • Kulwarun Warunsin,
  • Sumaman Buntoung

摘要

This study characterizes the seasonal variations and cross-boundary aerosol transport in central Thailand using a combination of low-cost PM2.5 sensors, sunphotometer observations, and Himawari-9 satellite aerosol products. PM2.5 data were collected by low-cost AEROBOX air quality sensors (LCASs) at four sites in central Thailand (Bangkok, Nakhon Pathom, Sra Kaeo, and Chachoengsao) between September 2022 and June 2024. A performance evaluation conducted against a reference-grade ADR-1500 instrument showed strong agreement (R2 = 0.96), confirming the suitability of LCASs for regional PM2.5 monitoring. The results show that PM2.5 concentrations were consistently higher during the winter season and lower during the rainy season at all sites. The highest mean PM2.5 levels were observed at the high-density population Nakhon Pathom site (59.0 ± 22.9 µg/m3), exceeding Thailand’s National Ambient Air Quality Standard (NAAQS) of 37.5 µg/m3, while lower levels were observed at the rural sites of Sra Kaeo (36.8 ± 13.8 µg/m3) and Chachoengsao (36.1 ± 11.1 µg/m3). A case study conducted from 14 to 18 January 2023 demonstrates the combined use of Cimel aerosol optical depth (AOD), Himawari-9 aerosol optical thickness (AOT), ERA5 wind fields, and HYSPLIT backward trajectories to identify aerosol sources and transport pathways affecting central Thailand. During this episode, elevated PM2.5 and AOD were associated with biomass-burning emissions transported from eastern Thailand and Cambodia under southeasterly flow, which mixed with local pollutants under stable atmospheric conditions. This study highlights the effectiveness of combining low-cost sensors with satellite observations and trajectory analyses for improving PM2.5 characterization in regions with limited monitoring networks.

Graphical Abstract