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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Alvarez Mendoza, CI (Autor) Outra; Ana Teodoro (Autor) FCUP; Torres, N (Autor) Outra; Vivanco, V (Autor) Outra

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: ENVIRONMENTS

Vol. 6

Página Final: 85

ISSN: 2076-3298

Indexação

ISI Web of Knowledge - 37 Citações

Scopus - 43 Citações

Outras Informações

ID Authenticus: P-00Q-Y13

DOI: 10.3390/environments6070085

Abstract (EN): 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 mu m 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 (R-2 = 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15