Mining is one of the principal economic activities in Mexico, which in addition to bringing benefits to the population, causes health and environmental problems. This activity produces a lot of wastes, but the main one is tailings. In Mexico, these wastes are disposed of in the open air, and there is no control over them, so the particles of these wastes are dispersed by wind currents to the surrounding population. In this research, tailings were characterized, finding in them particles smaller than 100 microns; in this way, tailings can enter into the respiratory system and hence can cause diseases. Furthermore, it is important to identify the toxic components. The present work does not have previous research in Mexico, and it shows a qualitative characterization of the tailings from an active mine using different analytical techniques. In addition to the data obtained from the characterization of the tailings, as well as the concentration of the toxic elements found, which were Pb and As, a dispersal model was generated and used to estimate the concentration of particles in the wind generated at the studied area. The air quality model used in this research is AERMOD, where it uses emission factors and available databases provided by Environmental Protection Agency (USEPA); Moreover, the model was coupled with meteorological information from the latest generation WRF model. The modeling results estimated that the dispersion of particles from the tailings dam can contribute up to 10.15 µg/m³ of PM₁₀ to the air quality of the site, which, according to the characterization of the samples obtained, could be dangerous for human health and can be estimated up to a concentration of 0.04 µg/m³ of Pb and 10.90 ng/m³ of As. It is very important to make this kind of research to know the risk which people around this disposal sites are exposed to.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO₂ and SO₂ in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 μg/m³ for NO₂ and 15.71 μg/m³ for SO₂, while background concentrations of 8.80 for NO_x and 0.05 for SO_x (μg/m³) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

The Tula Metropolitan Area in Mexico is characterized by significant industrial activity, including thermoelectric power plants, refineries, cement plants, and mining operations. While the impact of mining on air quality has been less studied compared to other industries, this research aims to estimate the contribution of mining areas to PM₁₀ air pollution in the region. Using the AERMOD dispersion model coupled with the WRF meteorological model, emission areas were identified through GIS analysis, and specific emission factors for mining activities were applied. The results indicate that mining areas can contribute up to 40 µg/m³ of PM₁₀, exceeding both national and international air quality standards. Monitoring data suggests that mining activities account for approximately 30% of the measured PM₁₀ concentrations in the area. Furthermore, spatial analysis using the Urban Marginalization Index (UMI) revealed that areas with high PM₁₀ concentrations often coincide with regions of high social vulnerability, particularly in communities with elevated levels of marginalization. This study concludes that mining operations significantly contribute to air pollution in the Tula Metropolitan Area, highlighting the need for targeted mitigation measures and public policies that address both environmental and social vulnerabilities.

En el presente trabajo se evalúa la sensibilidad de la base de datos meteorológicos generadas por el sistema de modelización meteorológico WRF, extrayendo los valores representativos para un sitio empleando el modelo MMIF. La estación meteorológica seleccionada de referencia (Villa de las Flores) es administrada por el Sistema de Monitoreo Atmosférico de la Ciudad de México, y está ubicada en el municipio de Coacalco. Para determinar la sensibilidad se analizó cualitativamente el comportamiento de los datos en una serie de tiempo a diferentes escalas, mientras que el análisis cuantitativo se efectuó utilizando los siguientes estadísticos: el error cuadrático medio (RMSE), el error absoluto medio normalizado (NMAE) (NMB), el sesgo (BIAS), coeficiente de correlación de Pearson (r) y el índice de ajuste (IOA). Se evaluó la temperatura, la humedad relativa, la velocidad y dirección del viento, dando como resultado que las tres primeras variables tienen un índice bueno de concordancia. No obstante, hubo diferencias cualitativas, pero no significativas en la dirección de viento, se infiere que puede ser producto de la escala utilizada en la modelización en el WRF.