%R doi:10.3390/atmos13060874
%X Population and industrial growth in Mexico’s Bajío region demand greater electricity consumption. The production of electricity from fuel oil has severe implications on climate change and people’s health due to SO2 emissions. This study describes the simulation of eight different scenarios for SO2 pollutant dispersion. It takes into account distance, geoenvironmental parameters, wind, terrain roughness, and Pasquill–Gifford–Turner atmospheric stability and categories of dispersion based on technical information about SO2 concentration from stacks and from one of the atmospheric monitoring stations in Salamanca city. Its transverse character, its usefulness for modeling, and epidemiological, meteorological, and fluid dynamics studies, as suggested by the models approved by the Environmental Protection Agency (EPA), show a maximum average concentration of 399 µg/m3, at an average distance of 1800 m. The best result comparison in the scenarios was scenery 8. Maximum nocturnal dispersion was shown at a wind speed of 8.4 m/s, and an SO2 concentration of 280 µg/m3 for stack 4, an atypical situation due to the geography of the city. From the validation process, a relative error of 14.7 % was obtained, which indicates the reliability of the applied Gaussian model. Regarding the mathematical solution of the model, this represents a reliable and low-cost tool that can help improve air quality management, the location or relocation of atmospheric monitoring stations, and migration from the use of fossil fuels to environmentally friendly fuels.
%V 13
%D 2022
%L uninimx2491
%N 6
%T Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO2 Atmospheric Emissions in the Salamanca Area, Bajío, Mexico
%J Atmosphere
%A Amanda Enrriqueta Violante Gavira
%A Wadi Elim Sosa González
%A Ramón Pali-Casanova
%A Marcial Alfredo Yam Cervantes
%A Manuel Aguilar Vega
%A Javier Chacha Coto
%A José del Carmen Zavala Loría
%A Luis Alonso Dzul López
%A Eduardo García Villena
%K Gaussian model; dispersion; emissions; meteorological variables; coefficients
%P 874