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In the recent two decades, the Mexico City Metropolitan Area (MCMA) has been plagued by high concentrations of air pollutants, risking the health integrity of its inhabitants. Although some policies have been undertaken, they have been insufficient to deplete high air pollutants. Environmental contingencies are commonly imposed when the ozone concentration overpasses a certain threshold, which is well above the recommended maximum by the WHO. This work used a causal version of a generalized Morlet wavelet to characterize the dynamics of daily ozone concentration in the MCMA. The results indicated that the formation of dangerous ozone concentration levels is a consequence of accumulation and incomplete dissipation effects acting over a wide range of time scales. Ozone contingencies occurred when the wavelet coefficient power is increasing, which was linked to an inti-persistence behavior. It was proposed that the wavelet methodology could be used as a further tool for signaling the potential formation of adverse ozone pollution scenarios., Comment: 11 pages, 7 figures

Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models., Comment: 11 pages, 5 figures

This article presents a detailed analysis of atmospheric pollutants in Puebla City, Mexico, based on data collected between 2016 and 2024. The research focuses on the daily variation of six major pollutants: ozone (O$_3$), particles smaller than 10 microns (PM10), particles smaller than 2.5 microns (PM2.5), sulfur dioxide (SO$_2$), and nitrogen dioxide (NO$_2$). The Mann-Kendall test, Innovate Trend, and Wavelet Transform Analysis were applied to identify significant trends and seasonal patterns. The results indicate an increase in the levels of O$_3$, SO$_2$, and NO$_2$, while the levels of PM10, and PM2.5 have shown a decrease. The study also employs the Prophet Forecasting Model to predict PM2.5 and PM10 concentrations for the year 2022, demonstrating that the model's accuracy increases as the analysis extends over longer periods.

Software Defined Networking (SDN) is a widely deployed technology enabling the agile and flexible management of networks and services. This paradigm represents an appropriate candidate to address the dynamic and secure management of large and complex Authentication, Authorization and Accounting (AAA) infrastructures. In those infrastructures, there are several nodes which must exchange information securely to interconnect different realms. This article describes a novel SDN-based framework with a data model-driven approach following the standard YANG, named SDN-AAA, which can be used to dynamically manage routing and security configuration in AAA scenarios., Comment: 7 pages, 5 figures