Evaluation of air quality in Puebla, Mexico: A wavelet transform and predictive modeling approach

Abstract

This article presents a detailed analysis of air pollutant dynamics in Puebla City, Mexico, using data collected between 2016 and 2024. The research examines the daily variation of five main pollutants: ozone (O₃), particulate matter smaller than 10 microns (PM₁₀), particulate matter smaller than 2.5 microns (PM_2.5), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂). To identify significant trends and seasonal patterns, the Mann-Kendall test, innovative trend analysis (ITA), and wavelet transform were applied. The results indicate statistically significant upward trends in O₃, SO₂, and NO₂ concentrations, while PM₁₀ and PM_2.5 levels have exhibited a sustained decrease throughout the study period. The scalogram analysis highlights seasonal energy concentrations of SO₂, potentially linked to industrial activity and meteorological conditions. Additionally, the Prophet forecasting model was used to estimate PM_2.5 and PM₁₀ levels from 2022 to 2024, achieving better performance over longer time horizons. This study is particularly relevant given the urban growth and industrial activity in Puebla, factors that can contribute to the deterioration of air quality and affect the health of the population. The identification of trends and patterns in air pollution is essential for the implementation of mitigation strategies and public policies aimed at improving air quality in the region.