Aydin, Demet2025-03-232025-03-2320240324-8828https://doi.org/10.37190/epe240403https://hdl.handle.net/11486/4654The increasing population and industrialization are the reasons for environmental and air pollution around the world. Air pollution is a major threat, especially to human health, both biological and economic. Therefore, determining the properties of air pollutants is very important for researchers and practitioners working in this field. In this study, the statistical distributions of some air pollutants are determined using the Gumbel, Weibull, generalized Pareto, log-normal, gamma, Rayleigh, and inverse Weibull distributions. The data was obtained from stations Boyabat and Merkez stations in Sinop province in 2017. The Kolmogorov-Smirnov test was used to determine the underlying distributions of the air pollution data. Then we use the root mean square error and coefficient of determination criteria to determine which distribution better fits the air pollution data. Finally, numerical results have shown that the generalized Pareto distribution demonstrates the best overall modeling performance, followed by log-normal and inverse Weibull distributions.eninfo:eu-repo/semantics/closedAccessProbability-DistributionParticulate MatterPm10 ConcentrationModelArticle504476810.37190/epe2404032-s2.0-85214013784Q4WOS:001380589200003Q4