Work

Informational and Topological Characterization of CO and O3 Hourly Time Series in the Mexico City Metropolitan Area During the 2019–2023 Period: Insights into the Impact of the COVID-19 Pandemic

Year: 2025

Type: article

Abstract: The main anthropogenic sources of air pollution in big cities are vehicular traffic and industrial activities. The emissions of primary pollutants are produced directly from the combustion of fossil fuels of vehicles and industry, whilst the secondary pollutants, such as tropospheric ozone (O3), are produced from precursors like Carbon monoxide (CO), among others, and meteorological factors such as radiation. In this study, we analyze the time series of CO and O3 concentrations monitored by the RAMA program between 2019 and 2023 in the southwest of the Mexico City Metropolitan Area, encompassing the COVID-19 lockdown period declared from March to September–October 2020. After removing cyclic patterns and normalizing the data, we applied informational and topological methods to investigate variability changes in the concentration time series, particularly in response to the lockdown. Following the onset of lockdown measures in March 2020—which led to a significant reduction in industrial activity and vehicular traffic—the informational quantities NX and Fisher Information Measure (FIM) for CO revealed significant shifts during the lockdown, while these metrics remained stable for O3. Also, the coefficient of variation of the degree CVk, which was defined for the network constructed for each series by the Visibility Graph, showed marked changes for CO but not for O3. The combined informational and topological analysis highlighted distinct underlying structures: CO exhibited localized, intermittent emission patterns leading to greater structural complexity, while O3 displayed smoother, less organized variability. Also, the temporal variation of the FIM and NX provides a means to monitor the evolving statistical behavior of the CO and O3 time series over time. Finally, the Visibility Graph (VG) method shows a behavioral trend similar to that shown by the informational quantifiers, revealing a significant change during the lockdown for CO, although remaining almost stable for O3.

Source: Applied Sciences

DOI: 10.3390/app15168775

Authors: Alejandro Ramirez-Rojas, Paulina Rebeca Cárdenas-Moreno, Israel Reyes-Ramírez, Michele Lovallo, Luciano Telesca

Institutions: Universidad Autónoma Metropolitana, Instituto Politécnico Nacional, Agenzia Regionale per la Protezione dell'Ambiente della Basilicata, CNR-IMIOT – Consiglio Nazionale delle Ricerche - Istituto di Metodologie Integrate per l’Osservazione della Terra, National Research Council

Language: English

Topic: COVID-19 impact on air quality

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $2,896