Comparison of satellite remote sensing data in the retrieve of PM10 air pollutant over Quito, Ecuador

© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Most of the large cities have an air quality network to measure air pollution including PM10. However, air quality monitoring network has a high cost and it is spatially limited. Quito, capital of Ecuador, is a city with an automatic air quality network (REMMAQ) composed by 9 stations. The REMMAQ works since 2002, measuring PM10 only in 4 regular stations located at different points along the city. This scarce quantity of PM10 measures led us to propose a new strategy to obtain PM10 data in all the city. Several studies have already considered the retrieving of PM10 from remote sensing data in cities with an air quality network. In order to find an optimal model to retrieve PM10 in Quito, this study compare the use of 3 different satellite sensors (Landsat-7 ETM+, Landsat-8 OLI and TERRA/MODIS) between 2013 to 2017. Additional to remote sensing data, we also use field data considering the REMMAQ. In each sensor, we used different variables and environmental indexes to model the best fit equation to quantify PM10 in all the city, finding the significant variables for each type of data and year. The variables considered were the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Stability Index (NSI), surface reflectance Blue Band (B1), surface reflectance Green Band (B2) and surface reflectance Red Band (B3). These variables were considered because most of them are used in different studies combined with meteorological data. All the procedures were implemented in R Studio. The empirical models using remote sensing data/derived products and air quality data can help in retrieving air pollutants in large cities. This work is a valuable contribution for the study of the spatialization of PM10 in order to find new alternatives in the use of remote sensing data to support government decisions.