Modeling generalized statistical distributions of PM2.5 concentrations during the COVID-19 pandemic in Jakarta, Indonesia

Abstract

Understanding the probabilistic or statistical behavior of air concentrations is necessary for the effective management of air pollution, such as PM2.5. Failure to consider the appropriateness of the model can lead to making inferences that are not supported by scientific evidence. The main focus of this article is to find the best statistical distribution in fitting PM2.5 concentrations in the periods of February–June 2018 and February–June 2019 (the periods without COVID-19) and in the period of February–June 2020 (the period with COVID-19) in Jakarta, Indonesia. This article considers making an assessment of the performance of both generalized distributions (e.g., generalized gamma, generalized extreme value, and generalized log-logistic [GLL]) and classical distributions (such as lognormal [LN], gamma, Weibull, log-logistic, and Gumbel) in modeling daily concentrations of PM2.5 in the period of February–June 2020, or the period during which the COVID-19 pandemic is present, in Jakarta. For comparison purposes, this study also analyzed PM2.5 concentrations in the periods of February–June 2018 and February–June 2019. The comparative evaluation of the models of each period of data uses graphical analyses and goodness-of-fit statistics. The results of applications indicate that the generalized distributions fit the data better than do the classical distributions. Particularly, compared with the classical distributions, including the LN model, the GLL distribution is the most appropriate model in fitting PM2.5 concentrations in the periods without and during the period with COVID-19 in Jakarta, Indonesia.