Evaluation of modelling systems in high resolution to assess the air pollutant impacts on human health

Nowadays the modelling of systems in high resolution is being used for air quality and other forecasting applications, where a spatial area is related with different interrelated variables that could be displayed on a map. This area is usually represented by global domains (hundred to thousand of square km); when smaller regions need to be represented, a high resolution modelling system can be used, these systems goes from one square km to dozen of square km, health is one of these issues where this kind of resolution can be used. In Europe, Asia, North America, South America and other countries, health problems related with the air pollution and climate change is a concern for individuals and world organizations like the WHO; today studies show the relation between morbidity and mortality rates, air pollution and effects on human health; these modelling systems in high resolution help us to simulate scenarios and propose solutions to this problematic. So the objective of this work is to evaluate the system performance WRF – CMAQ and CALIOPE on high resolution (4 km x 4 km) to determine air pollutant impacts of PM10, PM2.5, Ozone, NO2 and SO2 on population, using BenMAP for assess impact on health. The methodology suggested is the time series analysis of two years of hospital admissions, morbidity and mortality rates and the air quality forecasting of the cities selected, previously modelled in WRF, CMAQ and CALIOPE; after that, the Response Functions (DRF/ERF) to determine the impacts on health and the BenMAP software will be used. It is expecting find the scenarios that could decrease the mortality and morbidity rates in diseases like lung cancer, chronic respiratory obstructive disease, asthma, and the acute respiratory diseases in adults and children under ten years old

Assessment of meteorological models for air pollution transport: analysis between Mexico and Puebla metropolitan areas

This poster presents the results of research in the metropolitan areas in Mexico and Puebla valleys. The objective is assess and conduct a sensitivity analysis of meteorological conditions that could influence air pollutant transport between both valleys. The simulations were performed with CALMET v6.4 and WRF v.3.5, latter performed in the Mare Nostrum III super computer in the BSC-CNS; six days simulations considered statistically by Spearman correlations were selected in March and May months in 2012 year. It was found that WRF presented better results in domains to 9,3 and 1 km in contrast to CALMET, considering wind speed and temperature variables

La percepción de estudiantes del Instituto Tecnológico Superior de Poza Rica (ITSPR) ante factores que intervinieron en su Rendimiento Académico durante la cuarentena del COVID-19

En este trabajo se describen los resultados y análisis de la investigación realizada en una muestra de 758 alumnos de Ingeniería de Sistemas Computacionales (ISC) del Instituto Tecnológico Superior de Poza Rica (ITSPR) que durante su trayecto escolar sufrieron la cuarentena COVID-19 del periodo de marzo de 2020 hasta enero de 2022. El objetivo del trabajo consistió en evaluar factores pedagógicos, estrategias educativas, evaluación, factores psicológicos, la focalización de contenidos y factores fisiológicos, que pudieron incidir en el rendimiento académico de los alumnos de la carrera de ISC del ITSPR; quienes previo al COVID -19, ya incursionaban en la modalidad presencial y manejo de TICs. Los resultados obtenidos surgieron de una encuesta virtual que recopiló la información de los factores mencionados anteriormente; su análisis se desarrolló en Excel y en la plataforma de Office365. Se encontró que el rendimiento académico de los alumnos de ISC presentó un impacto positivo a pesar de los resultados de los factores evaluados. Se concluyó y pone en evidencia el diseño de nuevos paradigmas y programas enfocados al aprendizaje autónomo con estrategias que fortalezcan las modalidades virtuales tanto en docentes como en los alumnos, así como el establecer enfoques centrados en el control emocional, estrés y aspectos de salud. Finalmente resulta imprescindible que los docentes integren experiencias vividas en las aulas virtuales para evaluar los aciertos y desaciertos; y que contribuyan a generar planes de acción en el desarrollo del aprendizaje bajo nuevos paradigmas virtuales

Elemental chemical characterization and morphology of PM10 using SEM/EDS in air quality monitoring station filters in Poza Rica, Veracruz, Mexico

Air quality has been a global problem for several decades and has had several approaches for its mitigation, analysis and quantification. There are several ways and methods to quantify primary and secondary air pollutants. Therefore, the objective of this research was to determine, through SEM-EDS analysis, the elemental chemical characterization and morphology of the compounds present in the tapes of PM10 BAM (Beta Attenuation Mass Monitor) equipment from air quality monitoring campaigns in the city of Poza. Rica, Veracruz in Mexico. The methodology consisted of collecting filter tapes from the air quality monitoring stations used in the evaluation period, sampling and analysis was performed by the SEM/EDS scanning electron microscopy technique. The results allowed identifying in the samples the elemental chemical characterization and morphology of the particles retained in them

Desarrollo de un sistema virtual de enseñanza en enfermería basado en UNITY 3D: QüiVR Instrumental quirúrgico en enseñanza media

Los sistemas de realidad virtual son utilizados en numerosos campos para aplicaciones diversas en capacitación y enseñanza. La educación en el campo de la enfermería no es la excepción a estas tecnologías. En este artículo se presenta el desarrollo de la aplicación llamada QüiVR para el aprendizaje del instrumental quirúrgico en la enseñanza media en México, derivado de las necesidades educativas que surgieron por la pandemia de SARs-COv2. El trabajo se desarrolló considerando la metodología SCRUM, un modelo cliente servidor bajo la web para el control de usuarios, lenguajes de programación C#, Javascript y Microsoft Visual estudio. Se desarrollo la realidad virtual y maquetado mediante Blender y UNITY. Las interfaces web fueron elaboradas mediante CCS3, PHP y el gestor de base de datos de MySQL. Como Framework se eligió el entorno punto NET. El producto obtenido se sometió a pruebas diversas de funcionalidad y rendimiento dados los requerimientos de hardware para el manejo del sistema de capacitación QüiVR

Assessment of meteorological models for air pollution transport: analysis between Mexico and Puebla metropolitan areas

This poster presents the results of research in the metropolitan areas in Mexico and Puebla valleys. The objective is assess and conduct a sensitivity analysis of meteorological conditions that could influence air pollutant transport between both valleys. The simulations were performed with CALMET v6.4 and WRF v.3.5, latter performed in the Mare Nostrum III super computer in the BSC-CNS; six days simulations considered statistically by Spearman correlations were selected in March and May months in 2012 year. It was found that WRF presented better results in domains to 9,3 and 1 km in contrast to CALMET, considering wind speed and temperature variables

Recurrent neural network using LSTM for prediction of atmospheric pollutants in the State of Veracruz, Mexico

This article shows the use of a recurrent neural network LSTM whose objective was to make a 24-hour ozone forecast with data obtained from the INECC (National Institute of Ecology and Climate Change) and the validated database of the National Information System of Air Quality (SINAICA spanish acronym). The problem of air quality in the world is essential due to health problems. Currently, the techniques used to generate forecasts in monitoring stations are not accurate and do not allow generating alerts to avoid exposure to poor air quality. The state of Veracruz in Mexico has 7 air quality monitoring stations, showing that it is feasible to alternatively have a betterquality pollutant concentration forecasting system. The results obtained on the correlations of variables, although weak, positive or negative, allowed us to recognize their usefulness in the development of training in the RNN. The use of neural networks was demonstrated for the forecast of ozone concentration values in the metropolitan area of Poza Rica, using the LSTM using Python, Tensor Flow and Keras, with a good fit to the normalized pattern with an average RMSE of 0.0053 ppm of the days chosen for testing between the period from June 2019 to July 2020. Future work will consider PM10 and PM2.5