نبذة مختصرة : The deterioration of air quality in recent years is one of the main problems that most cities already cause lung diseases, lung cancer to the exposed population and Quito is no exception. Through the application of tools such as remote sensing it was possible to estimate one of the main pollutants emitted into the air called particulate material (PM10), monitored by the Quito Metropolitan Atmospheric Monitoring Network (REMMAQ) through the automatic and passive stations located along the city. We used satellite images provided by remote sensors (Landsat 7, Landsat 8 and MODIS) during the period 2003-2017, as well as generating a series of environmental indicators from the multispectral bands using the ArcGis 10.5 software. In addition, predictive models were created from miles, both by generalized linear regression (GLM) and geographically weighted regression (GWR) from quarterly averages. The stations of Carapungo and Guamaní exceeded the WHO maximum limit of 50 µg/m3 due to the predominance of public transport, the construction industry and activities associated with industrial sources. To predict the PM10 a multivariate matrix was used for each sensor and it was determined that the images provided by the “Landsat 8” sensor and the format using a GWR model with n = 218 observations tests the best criterion of Akaike AIC = -12.73, R2 Aj = 0.8339, and its residues meet the validation criteria, thus providing the best fit of PM10. ; El deterioro de la calidad del aire en los últimos años es uno de los principales problemas a los que se enfrentan la mayoría de las ciudades ya que provoca enfermedades pulmonares, cáncer de pulmón a la población expuesta y Quito no es la excepción. Mediante la aplicación de herramientas como la teledetección se pudo estimar uno de los principales contaminantes emitidos al aire denominado material particulado (PM10), monitoreado por la Red Metropolitana de Monitoreo Atmosférico de Quito (REMMAQ) mediante las estaciones automáticas y pasivas ubicadas a lo largo de la ciudad. Se empleó ...
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