Published Paper Details:

The Modified Inverse Exponentiated Exponential Poisson distribution, a novel and flexible probability distribution, is used in this article to analyze the air quality in Kathmandu. A real data set was used to evaluate the model's suitability for the air quality data of Kathmandu, Nepal, from 2022 to 2026. To investigate the actual ground-level air quality conditions, we performed the data analysis for particulate matter 2.5 (P2.5). This distribution is constructed by adding an additional shape parameter to the Inverse Exponentiated Exponential Poisson distribution. A number of statistical features of the proposed model are derived and analyzed. Additionally, we have examined the estimation of parameters, model validation, and model comparisons of the proposed model with existing models for P2.5 at Ratnapark station, Kathmandu. The model's parameters are estimated using Maximum Likelihood estimation (MLE), and the graphical validation of the model is checked by plotting the P-P and Q-Q charts. The models' suitability is compared using the Anderson-Darling (An) test, Cramer-Von Mises (CVM) test, and Kolmogorov-Smirnov (KS) test, along with their corresponding p-values, to demonstrate the better fitting of the proposed model. The ARIMA model is used to analyze time series data on PM2.5 air pollution from 2022 to 2026. We also found that the basic exponential smoothing strategy provides a decent forecast model for PM2.5 data from 2022 to 2026.