A summer episode was modeled to address the expected response of ambient air O 3 to hypothetical emission control scenarios in northeastern Mexico, and in particular in the Monterrey Metropolitan Area (MMA). This region is of interest because the MMA holds one of the worst air quality problems in the country and levels of air pollutants in the rest of northeastern Mexico are starting to be a concern. The MM5-SMOKE-CMAQ platform was used to conduct the numerical experiments. Twenty-four control scenarios were evaluated, combining the level of emission controls of O 3 precursors (NOx and volatile organic compounds [VOCs]) from 0% to 50%. For the MMA, VOC-only controls result in the best option to reduce O 3 concentrations, though the benefit is limited to the urban core. This same strategy results in negligible benefits for the rest of northeastern Mexico. NOx controls result in an increase in O 3 concentration within the MMA of up to 20 ppbv and a decrease at downwind locations of up to 11 ppbv, with respect to the basecase scenario. Indicator ratios were also used to probe for NOx-sensitive and VOC-sensitive areas. Locations with an important influence of NOx point sources (i.e., Monclova and Nava/Acuña) are quite sensitive to changes in NOx emissions. Border cities in the Rio Bravo/Grande Valley tend to be marginally NOx-sensitive. Overall, the MMA seems to be dominated by a VOC-sensitive regime, while the rest of the region would tend to have a NOx-sensitive response. The results obtained serve to expand the current knowledge on the chemical regimes that dominate this region (VOC-or NOx-sensitive), and thus could help guide public policies related to emission regional control strategies.Implications: Updated information on the expected response of ambient air O 3 to emission changes in northeastern Mexico is presented. Results suggest that emission control strategies for the Monterrey Metropolitan Area should focus on VOC reductions or combined VOC-NOx reductions, while the Lower Rio Bravo/Grande Valley border has a negligible response to the emission scenarios tested. Changes in NOx emissions from Carbon II, a 1,400-MW coal-fired electric utility, would have a nonnegligible transboundary effect.

Despite the plethora of studies reported during the last decade in relation to educational innovation in teaching and assessment of competencies, a consensus is seemingly lacking on a definition that establishes the scope and boundaries competency assessment. This research gap motivated a systematic review of the literature published on the topics of “educational innovation in teaching” and “assessment of competencies” in upper secondary and higher education during the period from January 2016 to March 2021. The main objective of the study was to define and evaluate educational innovation in teaching and assessment of competencies in upper secondary and higher education following PRISMA guidelines for a systematic literature review (SLR) on a curated corpus of 320 articles. We intended to answer the following questions: (1) What do “educational innovation in teaching” and “assessment of competencies” represent for upper secondary and higher education? (2) How are they evaluated? Lastly, (3) are efforts exerted toward the standardization of transversal competencies? The SLR seeks answers to these questions by examining nine research sub-queries. The result indicated that the greatest effort toward educational innovation in competencies was made at the higher education level and targeted students. Competencies were revised through associations with the Sustainable Development Goals of the 2030 Agenda. In addition, the methodologies used for teaching and evaluation of competencies were reviewed. Finally, the study discussed which technologies were used to develop the proficiencies of students.

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.

Air quality in the Mexican cities of Monterrey, Nuevo Leon, and Mexicali, Baja California, has suffered great detriment in recent years. It is well known that meteorology is one of the main factors affecting the dynamics of pollutants in the atmosphere. Here, the Penn State/NCAR Meteorological Mesoscale Model (MM5) meteorological system was applied to identify meteorological conditions conducive to high-ozone concentrations in such regions. Two summer 2001 ozone episodes for each geographical domain were selected with the aid of a classification and regression tree analysis technique. Model response to changes in its physical parameterization, horizontal grid resolution, and data assimilation schemes were assessed. Once a suitable configuration was selected, performance statistics were computed for model validation. MM5 simulated satisfactorily the meteorology of such episodes, yielding indexes of agreement of 0.4-0.8 for wind speed and 0.67-0.95 for temperature, on average. However, MM5 tended to underestimated temperature and overestimated wind speed. Froude numbers were calculated to analyze the impact of the terrain complexity on wind circulation. It was concluded that in both cities, wind convergence zones might enhance high-ozone concentrations. These results improve our understanding of the atmospheric processes exerting effect on air pollution within these airsheds.

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