A Multi-Criteria Geostatistical Framework Integrating Centrality Measures and Satellite Validation for PM2.5 Monitoring Network Expansion in Upper Northern Thailand
7 affiliations
- 1Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
- 2Advanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai 50200, Thailand
- 3Data Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
- 4Centre of Excellence in Mathematics, Ministry of Higher Education, Science, Research and Innovation (MHESI), Bangkok 10400, Thailand
- 5Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
- 6College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
- 7International College of Digital Innovation, Chiang Mai University, Chiang Mai 50200, Thailand
Abstract
Upper Northern Thailand is a critical region reflecting the severity of seasonal PM2.5 pollution and its transboundary spread, yet accurate spatial monitoring remains difficult due to complex mountainous terrain and a sparse network of physical monitoring stations. To resolve this observational limitation under a fixed sensor budget, this study develops a multi-criteria spatial optimization framework using weighted-sum scalarization to identify optimal locations for network expansion. We integrate set-dependent geostatistical Average Kriging Variance (AKV) reduction, meteorological correlation graphs, and cross-source satellite discrepancy into a unified combinatorial objective. Feature selection reveals that coupling rainfall, relative humidity, and temperature yields the best predictive performance, achieving an R2 of 0.8785 and minimizing RMSE to 33.91μg/m3 under Universal Kriging (UK). While Ordinary Kriging (OK) and Universal Kriging produce consistent spatial patterns, UK systematically secures superior variance reduction by accounting for coordinate-based spatial trends. Comparing optimization algorithms demonstrates distinct search dynamics: Genetic Algorithms (GAs) converge faster for smaller network expansion sizes, whereas Simulated Annealing (SA) becomes more scalable and efficient when adding 20 or more stations. Incorporating graph centrality measures (betweenness centrality and clustering coefficient) actively promotes spatial dispersion and prevents candidate stations from clustering redundantly in high-uncertainty areas. Furthermore, retrospective ground-station masking experiments demonstrate that the framework reliably targets genuine observational blind spots, reducing out-of-sample prediction RMSE by up to 40.98% upon network expansion. The proposed framework provides a practical, data-driven decision-support screening tool for environmental agencies seeking cost-effective air quality infrastructure expansion in data-sparse regions.
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BibTeX
@article{himakalasa2026multicriteria,
title = {{A Multi-Criteria Geostatistical Framework Integrating Centrality Measures and Satellite Validation for PM2.5 Monitoring Network Expansion in Upper Northern Thailand}},
author = {Adsadang Himakalasa and Thaned Rojsiraphisal and Tanapat Chalarux and Woratham Khangtragool and Wipawinee Chaiwino and Kanyuta Poochinapan and Payakorn Saksuriya},
journal = {Mathematics},
year = {2026},
month = nov,
doi = {10.3390/math14214042},
url = {https://www.mdpi.com/2227-7390/14/21/4042},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Multi-Criteria Geostatistical Framework Integrating Centrality Measures and Satellite Validation for PM2.5 Monitoring Network Expansion in Upper Northern Thailand
AU - Adsadang Himakalasa
AU - Thaned Rojsiraphisal
AU - Tanapat Chalarux
AU - Woratham Khangtragool
AU - Wipawinee Chaiwino
AU - Kanyuta Poochinapan
AU - Payakorn Saksuriya
PY - 2026
DA - 2026/11/04
JO - Mathematics
DO - 10.3390/math14214042
UR - https://www.mdpi.com/2227-7390/14/21/4042
AB - Upper Northern Thailand is a critical region reflecting the severity of seasonal PM2.5 pollution and its transboundary spread, yet accurate spatial monitoring remains difficult due to complex mountainous terrain and a sparse network of physical monitoring stations. To resolve this observational limitation under a fixed sensor budget, this study develops a multi-criteria spatial optimization framework using weighted-sum scalarization to identify optimal locations for network expansion. We integrate set-dependent geostatistical Average Kriging Variance (AKV) reduction, meteorological correlation graphs, and cross-source satellite discrepancy into a unified combinatorial objective. Feature selection reveals that coupling rainfall, relative humidity, and temperature yields the best predictive performance, achieving an R2 of 0.8785 and minimizing RMSE to 33.91μg/m3 under Universal Kriging (UK). While Ordinary Kriging (OK) and Universal Kriging produce consistent spatial patterns, UK systematically secures superior variance reduction by accounting for coordinate-based spatial trends. Comparing optimization algorithms demonstrates distinct search dynamics: Genetic Algorithms (GAs) converge faster for smaller network expansion sizes, whereas Simulated Annealing (SA) becomes more scalable and efficient when adding 20 or more stations. Incorporating graph centrality measures (betweenness centrality and clustering coefficient) actively promotes spatial dispersion and prevents candidate stations from clustering redundantly in high-uncertainty areas. Furthermore, retrospective ground-station masking experiments demonstrate that the framework reliably targets genuine observational blind spots, reducing out-of-sample prediction RMSE by up to 40.98% upon network expansion. The proposed framework provides a practical, data-driven decision-support screening tool for environmental agencies seeking cost-effective air quality infrastructure expansion in data-sparse regions.
SN - 2227-7390
ER -