## 5 Conclusions  
This study applied two complementary analytical approaches--the Fisher-Shannon information framework and the Visibility Graph method--to analyze the time series of _CO_ and _O_\({}_{3}\) concentrations in the southwest of the MCMA from 2019 to 2023, including during the COVID-19 lockdown. Our results provide clear evidence of differential responses between primary and secondary pollutants. The informational metrics NX and FIM detected a substantial shift in the statistical behavior of _CO_ during the lockdown period. These changes reflect the abrupt reduction in vehicular activity and industrial emissions and reveal a decrease in complexity and disorder consistent with a more homogeneous and less volatile emission profile during that time. Conversely, the stability of _N_\({}_{X}\) and FIM values for _O_\({}_{3}\) suggests that this secondary pollutant is governed by slower, large-scale chemical and meteorological processes that are less immediately responsive to localized emission reductions. The VG method offered a topological perspective on these findings. The coefficient of variation of the node degree _CV_\({}_{k}\) in the constructed networks showed changes in _CO_ time series complexity similar to those detected by the Fisher-Shannon metrics. For _O_\({}_{3}\), the VG structure remained relatively stable, supporting the conclusion that its variability is less sensitive to short-term anthropogenic shifts and is instead modulated by regional photochemistry, precursor ratios, and seasonal solar radiation patterns. These findings have important practical and methodological implications. They confirm the usefulness of _CO_ as a near-real-time indicator of changes in urban activity while highlighting the complex, nonlinear nature of _O_\({}_{3}\) formation and the need for integrated mitigation strategies that consider precursor dynamics and meteorological conditions. The informational and topological methods applied here can detect subtle changes in pollutant behavior, offering valuable diagnostic insights that complement traditional air quality assessments. Their scalability and adaptability make them suitable for application in other urban contexts, and they hold potential as early-warning tools for identifying anomalous pollution patterns linked to extreme events or policy shifts. The integration of informational theory and complex network analysis provides a powerful framework to explore the dynamics of air pollutant behavior. In contexts like MCMA, where emissions are intense and meteorological constraints severe, such approaches offer a more nuanced and mechanistic understanding of pollutant variability--one that is essential for designing robust, adaptive, and evidence-based air quality policies.  
Conceptualization, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T.; Methodology, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T.; Software, A.R.-R., I.R.-R., M.L. and L.T.; Validation, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T.; Investigation, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T.; Data curation, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T.; Writing--original draft, A.R.-R., P.R.C.-M., I.R.-R., M.L. and L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The instructions to download the dataset are available on the website: [[http://www.aire.cdmx.gob.mx/default.php](http://www.aire.cdmx.gob.mx/default.php)]([http://www.aire.cdmx.gob.mx/default.php](http://www.aire.cdmx.gob.mx/default.php)), accessed on 5 August 2025.  
A.R.R. and P.R.C.-M. thank to Basic Science Department of UAM Azcapotzalco-Mexico. I.R.-R. thanks to COFAA and EDI IPN-Mexico. L.T. and M.L. thank to CNR-Italy. The authors also acknowledge to SECIHTI-Mexico.  
The authors declare no conflicts of interest.