Comparación en la estimación de material particulado PM10 usando imágenes satelitales LANDSAT 7, LANDSAT 8 Y MODIS en Quito

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 e...

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書誌詳細
第一著者: Torres Saquinga, Nelly Stefanía (author)
その他の著者: Vivanco Pérez, Valeria Lizbeth (author)
フォーマット: bachelorThesis
言語:spa
出版事項: 2018
主題:
オンライン・アクセス:http://dspace.ups.edu.ec/handle/123456789/16071
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その他の書誌記述
要約: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.