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

Abstract

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.

COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas

“…Ogen (2020) has found a strong link between the concentration of NO 2 and fatality caused by COVID-19 in another study of areas in Italy, Spain, France, and Germany. Lee et al (2011), Yap et al (2011, Yap and Hashim (2013), Chitranshi et al (2015); Pal and Mandal (2019a, b), Chowdhury et al (2019), Olmanson et al (2016), Alvarez-Mendoza et al (2019) have warned about the growth of PM concentration in lower atmosphere due to human activities. Most of them had computed the PM using either Landsat or MODIS products.…”

COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity

“…Most of these studies stretched for the meso to synoptic coverage area for vast geographical regions using 1 km × 1 km spatial resolution Moderate Resolution Imaging Spectro-radiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) which retrieved the aerosol characteristics and its optical depth or thickness. But since the spatial resolution of MODIS and VIIRS are coarser than Landsat-8 (30 m) data ( Alvarez-Mendoza et al, 2019 ; Olmanson et al, 2016 ) it is recently being widely used for estimating the aerosol optical depth through apt use of multifarious strategies and models ( Alam et al, 2014 ; Alvarez-Mendoza et al, 2019 ; Bilal et al, 2013 ; Fernández-Pacheco et al, 2018 ; Mozafari et al, 2019 ; Saleh and Hasan, 2014 ).…”

Preface: Special Issue on Air Quality Assessment for Environmental Policy Support: Sources, Emissions, Exposures, and Health Impacts

“…In their study, Alvarez-Mendoza et al [12] presented an approach to estimate the concentration of PM 10 using an empirical land use regression (LUR) model and considered different remote sensing data as the input. The study area was Quito, the capital of Ecuador, and the data were collected between 2013 and 2017.…”