Mutual information analysis between NO2 and O3 pollutants measured in Mexico City before and during 2020 Covid-19 pandemic year

Abstract

The estimation of statistical dependence between two variables monitored simultaneously, which can come from a complex system, is an important task because allows identify temporal correlations between the components that are involved in the dynamical evolution of the studied system. In many areas of study, like environmental sciences, there are many problems associated with atmospheric pollution, weather, greenhouse effect or climatic change, among others, that remain open today. In general, the variables of complex systems are often linked through nonlinear relations which involve stochastic processes. In order to assessment correlations between random variables, the suitable measure employed to quantify the dependency is the mutual information (MI). In this work we perform a preliminary analysis of temporal dependence between nitrogen dioxide (NO2) and ozone (O3) monitored in the Mexico City Metropolitan Area (MCMA) during the years 2015, 2018 and 2020 being the last the Covid-19 pandemic year. The interest of this study is because NO2 is emitted in large volumes by cars, trucks and industries and O3 is a product from NO2. The aim is to estimate the global correlation between both gases by means the mutual information in three different conditions.