Probability Distribution Analysis of Daily PM2.5 Concentration at Five Stations in Upper Northern Thailand

Severe seasonal particulate matter pollution poses persistent environmental and public health challenges across upper northern Thailand. This study investigates the statistical distribution of daily average PM2.5 concentrations across five key monitoring stations (Chiang Rai, Mae Hong Son, Chiang Mai, Nan, and Lampang) using an extensive multi-year dataset from June 2023 to May 2026 (1096 daily observations per station). Candidate probability models—specifically Log-Normal, Gamma, and Weibull distributions—were fitted using maximum likelihood estimation. Their goodness of fit was rigorously evaluated through the Kolmogorov–Smirnov test, Anderson–Darling test, Akaike Information Criterion, and Bayesian Information Criterion. The empirical findings reveal that the Log-Normal distribution consistently outperformed the alternative models, yielding the lowest test statistics and information criteria values across all stations. Consequently, the Log-Normal model is established as the most robust framework for characterizing right-skewed particulate matter concentrations and capturing upper-tail extremes associated with regional biomass burning episodes. Furthermore, threshold exceedance evaluations relative to the Thai national 24-h standard (37.5 µg/m3) highlight high regional vulnerability, with Nan recording the highest exceedance frequency at 25.12%, followed by Lampang (21.70%) and Chiang Rai (20.87%). These validated probabilistic models provide a reliable statistical foundation for assessing long-term air quality risks, evaluating regulatory thresholds, and supporting targeted mitigation policies in regions susceptible to transboundary haze.