Ozone responses to reduced precursor emissions: A modeling analysis on how attainable goals can improve air quality in the Mexico City Metropolitan Area.

High tropospheric ozone (O3) concentrations prevent the improvement of the air quality in the Mexico City Metropolitan Area (MCMA). Although the problem has improved considerably since the 1990s, a rebound in O3 levels in recent years has raised concerns about the deteriorating air quality. The nonlinear relationship between O3 formation and the emissions of its main precursors, i.e., volatile organic compounds (VOCs) and nitrogen oxides (NOx), is a challenge when measures are enacted for effective mitigation of the O3 problem. This study evaluated the reduction in precursors, VOCs and NOx, using an up-to-date regional air quality model (HERMES-Mex-WRF-CMAQ). For evaluating realizable scenarios, the decline in VOC achieved in Japan after policy implementation was the targeted VOC reduction (40 % from area sources), and the NOx reduction observed in the MCMA during the COVID-19 pandemic was the targeted NOx reduction (40 % from mobile sources). The analysis evaluated the O3 responses to changes in a single precursor and a combination of both during a period of high O3 concentrations (April 2019). The results showed that 40 % reduction in VOC emissions would decrease the O3 8-h maximum concentrations by 16 %. However, 40 % reduction in NOx emissions would increase O3 by >15 %. The simultaneous reduction of both precursors did not significantly affect O3 levels. The diagnosis of ozone sensitivity using the H2O2/HNO3 ratios reinforced the simulation findings, indicating that VOC emissions limited ozone formation in most MCMA areas. As the simulated scenarios were based on factual case studies, our research offers insights into the realistic aims of MCMA policies to reduce O3 levels.

Forecasting of COVID19 per regions using ARIMA models and polynomial functions.

COVID-2019 is a global threat, for this reason around the world, researches have been focused on topics such as to detect it, prevent it, cure it, and predict it. Different analyses propose models to predict the evolution of this epidemic. These analyses propose models for specific geographical areas, specific countries, or create a global model. The models give us the possibility to predict the virus behavior, it could be used to make future response plans. This work presents an analysis of COVID-19 spread that shows a different angle for the whole world, through 6 geographic regions (continents). We propose to create a relationship between the countries, which are in the same geographical area to predict the advance of the virus. The countries in the same geographic region have variables with similar values (quantifiable and non-quantifiable), which affect the spread of the virus. We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others.