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In the three major urban agglomerations in Mexico (Mexico City, Monterrey, and Guadalajara), a significant change to anthropogenic sources of air pollution happened in March–May 2020, when policies implemented to stop the spread of the COVID-19 virus in Mexico caused the reduction of some anthropogenic sources of air pollution. We study the effect of these significant changes to air pollution sources using satellite-retrieved aerosol optical depth (AOD) and particulate matter (PM10 and PM2.5) concentrations from ground stations. The Chow test was applied to study trend changes in PM concentrations from 1 January to 30 May 2020. The Mann–Whitney non-parametric test was then used to compare average PM concentrations in April and May pre-lockdown, during lockdown in 2020, and post-lockdown in 2021. The assessment was further performed by evaluating the exceedance of national air quality standard maxima. The trend analysis showed that PM10 concentrations were reduced during lockdown in Mexico City and Monterrey, whereas no change was found for PM10 in Guadalajara and PM2.5 in the three cities. Further analysis showed that in Mexico City and Guadalajara, average PM10 and PM2.5 concentrations decreased by 12% in April and May 2020. However, in Monterrey, average PM10 and PM2.5 concentrations increased by 2.76% and 11.07%, respectively, in April 2021 due to a severe drought that caused dry soils and dust around the city. The results of this research can be used to implement policies for reducing anthropogenic sources to improve the air quality in urban areas. [ABSTRACT FROM AUTHOR]

The planetary boundary layer (PBL) height plays a major role in air quality and weather forecast studies. However, it cannot be directly measured as it can only be determined from the measured profiles of atmospheric parameters such as., temperature, moisture, and aerosol backscatter based on different retrieval mechanisms. This paper presents the PBL features such as., stable boundary layer (SBL), convective boundary layer (CBL), and residual layer (RL) detected from the microwave radiometer (MWR), mini Micro Pulse Lidar (MPL), and radiosondes (RS) and an intercomparison was done between the instruments during the period of 16 February – 31 May 2019 in Mexico City. RS were launched thrice a day during this period at 06 LST, 12 LST, and 18 LST respectively. The PBL heights comparison was classified into 3 categories: MWR-RS, mini MPL-RS, and mini MPL-MWR. The daytime CBL heights for the MWR-RS comparison correlate well (r = 0.96 at 12 LST, r = 0.88 at 18 LST) as well as RL height (r = 0.94 at 06 LST). The CBL and RL heights for the mini MPL-RS and mini MPL-MWR comparison also agree very well (0.89 < r < 0.99). Overall, the corresponding SBL height comparisons at 06 LST yielded lower agreements (r < 0.66) with the lowest correlation values for the mini MPL-MWR comparison (r = 0.39), which is likely due to the inability of the mini MPL to detect aerosol backscatter below ~120 m above ground level (agl) as well as the stability of the atmosphere, which prevents uniform mixing of the aerosol particles. [Display omitted] • Intercomparison of microwave radiometer, micro-pulse lidar and radiosondes during 3 months. • Overall good agreement in the detection of the convective and residual boundary layer heights. • Lower agreement for the stable boundary layer height. [ABSTRACT FROM AUTHOR]

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Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM 10 and PM 2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics. [ABSTRACT FROM AUTHOR]