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Open AccessArticle

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

1
Department of Geosciencies, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre 687, Porto 4169-007, Portugal
2
Grupo de Investigación Ambiental en el Desarrollo Sustentable GIADES, Carrera de Ingeniería Ambiental, Universidad Politécnica Salesiana, Quito 170702, Ecuador
3
Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, Porto 4169-007, Portugal
*
Author to whom correspondence should be addressed.
Environments 2019, 6(7), 85; https://doi.org/10.3390/environments6070085
Received: 14 June 2019 / Revised: 16 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available. View Full-Text
Keywords: remote sensing; air quality modeling; air quality monitoring; PM10; LUR remote sensing; air quality modeling; air quality monitoring; PM10; LUR
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Alvarez-Mendoza, C.I.; Teodoro, A.C.; Torres, N.; Vivanco, V. Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador. Environments 2019, 6, 85.

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