Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand
Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2468 |
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| author | Khuanchanok Chaichana Supanut Chaidee Sayan Panma Nattakorn Sukantamala Neda Peyrone Anchalee Khemphet |
| author_facet | Khuanchanok Chaichana Supanut Chaidee Sayan Panma Nattakorn Sukantamala Neda Peyrone Anchalee Khemphet |
| author_sort | Khuanchanok Chaichana |
| collection | DOAJ |
| description | Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated the spatial and temporal dynamics of PM2.5 in northern Thailand. Specifically, it explored how pollution at one monitoring station influenced concentrations at others and revealed the seasonal structure of PM2.5 transmission using network-based analysis. We developed a Python-based framework to analyze daily PM2.5 data from 2022 to 2023, selecting nine representative stations across eight provinces based on spatial clustering and shape-based criteria. Delaunay triangulation was used to define spatial connections among stations, capturing the region’s irregular geography. Cross-correlation and Granger causality were applied to identify time-lagged relationships between stations for each season. Trophic coherence analysis was used to evaluate the hierarchical structure and seasonal stability of the resulting networks. The analysis revealed seasonal patterns of PM2.5 transmission, with certain stations, particularly in Chiang Mai and Lampang, consistently acting as source nodes. Provinces such as Phayao and Phrae were frequently identified as receptors, especially during the winter and rainy seasons. Trophic coherence varied by season, with the winter network showing the highest coherence, indicating a more hierarchical but less stable structure. The rainy season exhibited the lowest coherence, reflecting greater structural stability. PM2.5 spreads through structured, seasonal pathways in northern Thailand. Network patterns vary significantly across seasons, highlighting the need for adaptive air quality strategies. This framework can help identify influential monitoring stations for early warning and support more targeted, season-specific air quality management strategies in northern Thailand. |
| format | Article |
| id | doaj-art-515786f4b472499c9630247ae99fdeda |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-515786f4b472499c9630247ae99fdeda2025-08-20T03:36:26ZengMDPI AGMathematics2227-73902025-07-011315246810.3390/math13152468Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern ThailandKhuanchanok Chaichana0Supanut Chaidee1Sayan Panma2Nattakorn Sukantamala3Neda Peyrone4Anchalee Khemphet5Advanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, ThailandAdvanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, ThailandAdvanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, ThailandAdvanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, ThailandAdvanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, ThailandNorthern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated the spatial and temporal dynamics of PM2.5 in northern Thailand. Specifically, it explored how pollution at one monitoring station influenced concentrations at others and revealed the seasonal structure of PM2.5 transmission using network-based analysis. We developed a Python-based framework to analyze daily PM2.5 data from 2022 to 2023, selecting nine representative stations across eight provinces based on spatial clustering and shape-based criteria. Delaunay triangulation was used to define spatial connections among stations, capturing the region’s irregular geography. Cross-correlation and Granger causality were applied to identify time-lagged relationships between stations for each season. Trophic coherence analysis was used to evaluate the hierarchical structure and seasonal stability of the resulting networks. The analysis revealed seasonal patterns of PM2.5 transmission, with certain stations, particularly in Chiang Mai and Lampang, consistently acting as source nodes. Provinces such as Phayao and Phrae were frequently identified as receptors, especially during the winter and rainy seasons. Trophic coherence varied by season, with the winter network showing the highest coherence, indicating a more hierarchical but less stable structure. The rainy season exhibited the lowest coherence, reflecting greater structural stability. PM2.5 spreads through structured, seasonal pathways in northern Thailand. Network patterns vary significantly across seasons, highlighting the need for adaptive air quality strategies. This framework can help identify influential monitoring stations for early warning and support more targeted, season-specific air quality management strategies in northern Thailand.https://www.mdpi.com/2227-7390/13/15/2468air pollutionDelaunay triangulationcross-correlationGranger causalitytrophic coherence |
| spellingShingle | Khuanchanok Chaichana Supanut Chaidee Sayan Panma Nattakorn Sukantamala Neda Peyrone Anchalee Khemphet Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand Mathematics air pollution Delaunay triangulation cross-correlation Granger causality trophic coherence |
| title | Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand |
| title_full | Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand |
| title_fullStr | Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand |
| title_full_unstemmed | Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand |
| title_short | Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand |
| title_sort | influence of regional pm2 5 sources on air quality a network based spatiotemporal analysis in northern thailand |
| topic | air pollution Delaunay triangulation cross-correlation Granger causality trophic coherence |
| url | https://www.mdpi.com/2227-7390/13/15/2468 |
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