Application of the Parametric Bootstrap Method for Confidence Interval Estimation and Statistical Analysis of PM2.5 in Bangkok

Abstract

Research in epidemiology and health science indicates that exposure to particles with an aerodynamic diameter of less than 2.5 µm (PM2.5) causes harmful health consequences. Probability density functions (pdf) are utilized to analyze the distribution of pollutant data and study the occurrence of high-concentration occurrences. In this study, PM2.5 concentrations (in μg/m^3 ) were recorded daily from January 2011 to December 2022 at 12 air quality monitoring locations in Bangkok. The study utilized two-parameter distributions such as gamma, inverse Gaussian, lognormal, log-logistic, Weibull, and Pearson type V to identify the most suitable statistical distribution model for PM2.5 in Bangkok. The Anderson-Darling test result indicates that the inverse Gaussian and Pearson type V distributions are the most appropriate probability density functions for the daily average PM2.5 concentration at stations in Bangkok. The projected 98th percentile of daily PM2.5 levels at two locations is higher than the 24-hour threshold for daily PM2.5 concentrations in Thailand, posing significant health risks. Additionally, the two parametric bootstrap methods used to estimate confidence intervals for the median, namely percentile bootstrap and simple bootstrap, indicate that two stations have poor air quality for those with sensitive health conditions.