Comparative analysis of extreme value distributions for modeling particulate matter air pollution in Trenčín, Slovakia

Long-term exposure to high levels of PM10 and PM2.5 represents a serious health risk and causes significant environmental damage.Therefore, it is necessary to monitor and predict the concentrations of air pollutants to implement both short-term and long-term actions to prevent health-risky situations.This study aims to determine the optimal probability distribution for modeling PM10 and PM2.5 concentrations in the city of Trenčín, Slovakia.The dataset, comprising daily average concentrations of PM10 and PM2.5 measured from January 1, 2017, to December 31, 2024, was fitted with the lognormal, gamma, log-logistic, Weibull, and exponentiated Weibull distributions.The exponentiated Weibull probability distribution has not previously been applied to such a dataset.To identify the best-fitting distribution, five performance indicators were employed: the root mean squared error, the coefficient of determination, the prediction accuracy, the Kolmogorov-Smirnov and Anderson-Darling tests.The Weibull distribution generally exhibited the worst performance.In contrast, the exponentiated Weibull distribution consistently ranked among the top three best distributions.