## 1 Introduction  
The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (see [1]). The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (see [2]). The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (see [3]). The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (see [4]). The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (see [5]). The COVID-19 pandemic has been a major focus on the study of COVID-19 pandemic (  
## 1 Introduction  
Air pollution remains a critical challenge in large urban agglomerations due to its complex causes and wide-ranging effects on human health, ecosystems, and climate systems [1]. Among the major pollutants, tropospheric ozone (\(O_{3}\)) and carbon monoxide (\(CO\)) are particularly relevant in urban areas because of their strong links to anthropogenic emissions, primarily from fossil fuel combustion in the transport and industrial sectors. In cities such as the Mexico City Metropolitan Area (MCMA)--one of the largest urban zones globally with more than 20 million inhabitants--air pollution is driven by high vehicular density (over 5 million vehicles), industrial activities, and unfavorable topography that traps pollutants within a basin-like valley [2; 3; 4]. \(CO\) is a primary pollutant emitted directly from incomplete combustion processes, especially from gasoline-powered engines, industrial machinery, and residential sources such as gas heaters and stoves [5]. In contrast, tropospheric ozone is a secondary pollutant formed by complex photochemical reactions involving precursors like CO, nitrogen oxides (\(NO_{x}\)), volatile organic compounds (\(VOCs\)), and solar radiation [6; 7]. Long-term monitoring data from the Environmental Monitoring Office (DMA-CDMX for its Spanish acronym) [8] indicate that from 2019 to 2023, daily \(CO\) concentrations in the MCMA ranged between 0.6 and 1.2 ppm, with occasional exceedances in traffic-congested zones. Meanwhile, \(O_{3}\) concentrations routinely surpassed the WHO guideline of 70 ppb, particularly in spring months during the afternoon, driven by high solar radiation and stagnant meteorological conditions [2; 3; 9]. These local trends mirror observations in other urban centers. For instance, cities like Beijing [10] and Delhi [11] have reported similar patterns, where \(CO\) emissions are largely attributed to vehicular traffic, while \(O_{3}\) formation depends heavily on the interaction between \(VOCs\) and \(NO_{x}\) under specific photochemical regimes. Notably, in VOC-limited environments, reducing \(NO_{x}\) emissions can paradoxically lead to increased \(O_{3}\) concentrations--an effect observed during the COVID-19 lockdowns in many cities worldwide, including the MCMs. Despite a sharp decrease in \(CO\) and \(NO_{x}\) emissions during this period, \(O_{3}\) levels remained stable or even increased due to altered atmospheric chemistry and reduced NO titration [12; 13; 14]. Such anomalies underscore the need for more advanced analytical tools capable of characterizing the temporal dynamics and inherent complexity of air pollution time series, beyond what traditional statistical methods can capture. Time series of atmospheric pollutant concentrations are, by nature, stochastic processes that may exhibit self-similarity and scale-invariant behaviors across different time scales. In many natural systems, such complex signals often follow a multifractal structure characterized by scaling exponents that vary with scale. Several studies have focused on \(O_{3}\) as a primary pollutant, employing multifractal analysis [15; 16] and wavelet-based techniques [13; 17; 18] to examine temporal dynamics. While these approaches have yielded valuable insights, they present limitations when it comes to fully capturing the evolving complexity and structural patterns in pollutant behavior.