Statistical Modeling of PM2.5 Concentrations: Prediction of Extreme Events and Evaluation of Advanced Methods for Air Quality Management

Authors

  • Amaury de Souza

    Institute of Physic, Federal University of Mato Grosso do Sul, Campo Grande 79070- 900, Brazil
  • José Roberto Zenteno Jimenez

    Geophysical Engineering, ESIA-Unidad Ticóman Mayor Gustavo A. Madero. National Polytechnic Institute, México City 07340, México
  • José Francisco de Oliveira- Júnior

    Institute of Atmospheric Sciences (ICAT)Federal University of Alagoas, Maceió 57072-900, Brazil
  • Kelvy Rosalvo Alencar Cardoso

    Institute of Atmospheric Sciences (ICAT)Federal University of Alagoas, Maceió 57072-900, Brazil

DOI:

https://doi.org/10.30564/jasr.v8i3.10878
Received: 21 May 2025 | Revised: 8 July 2025 | Accepted: 16 July 2025 | Published Online: 22 July 2025

Abstract

This study analyzes the statistical behavior of PM2.5 concentrations in Brasília using advanced probabilistic and time series modeling to support air quality management and extreme event forecasting. The methods applied include Generalized Extreme Value (GEV) distributions, Bayesian inference with Log-Normal distribution, ARIMA models, and quasi-Gaussian approaches. Model performance was evaluated through statistical metrics such as RMSE, R², and the Approximation Index, with parameter estimation improved using the Metropolis-Hastings algorithm. Results show that the GEV 1 model provides a better fit for lower PM2.5 concentrations, while GEV 2 performs better at predicting extreme events. The log-logistic and log-normal distributions also demonstrated good fit, capturing asymmetry and long-tail behavior typical of environmental data. The ARIMA model identified seasonal patterns and supported short-term forecasts, though its predictive capacity for extreme values was limited. Bayesian inference allowed robust estimation of parameter uncertainties and revealed the non-negligible likelihood of severe pollution events. The study concludes that model selection should depend on the forecasting objective: GEV for extremes, Log-Normal for general variability, and ARIMA for trends and seasonality. The use of MCMC sampling techniques significantly improved model robustness. These findings provide a comprehensive framework for understanding air pollution dynamics and guiding public policy on air quality in urban environments.

Keywords:

PM2.5; GEV; ARIMA; Bayesian Inference; Metropolis-Hastings

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How to Cite

Souza, A. de, Jimenez, J. R. Z., Júnior, J. F. de O.-., & Cardoso, K. R. A. (2025). Statistical Modeling of PM2.5 Concentrations: Prediction of Extreme Events and Evaluation of Advanced Methods for Air Quality Management. Journal of Atmospheric Science Research, 8(3), 67–92. https://doi.org/10.30564/jasr.v8i3.10878

Issue

Vol. 8 , Iss. 3 (July 2025): In Progress

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Article

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Copyright © 2025 Amaury de Souza, José Roberto Zenteno Jimenez, José Francisco de Oliveira- Júnior, Kelvy Rosalvo Alencar Cardoso

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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