Monitoring air quality using remote sensing based on a Google Earth Engine application in countries with limited air quality data and control policies: A case study in Ecuador.

Currently, remote sensing applications are more diverse and numerous than when remote sensing was introduced as an environmental monitoring technology. New satellite options have appeared recently to monitor agricultural operations, environmental change, geological activity, and other Earth system processes in tandem with new data science approaches, including machine and deep learning cloud-based computing. Air emissions monitoring has recently emerged as an important application of remote sensing, particularly after introducing the Tropospheric Monitoring Instrument (TROPOMI) sensor aboard satellite Sentinel-5P. This high spatio-temporal resolution sensor was launched in 2017. The sensor collects daily aerosol, carbon monoxide (CO), formaldehyde, nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and methane (CH₄) concentrations providing global coverage. Due to the sensor’s characteristics, Sentinel-5P images constitute an alternative to urban air quality without needing other ground-based devices or methodologies. Moreover, with the development of cloud computing applications such as Google Earth Engine (GEE), which allows faster and more efficient access to remote sensing resources than traditional desktop environments, objective evaluations of environmental change can be done more effectively today than in the past. This study presents a novel methodology to build Sentinel-5p-based air quality control applications using GEE. We present an application that focuses on the Ecuadorian mainland. The application allows users to observe the CO, NO2, and O3 concentration at the province level as an interactive colour map during user-determined periods. Thus, users can compare air pollution concentrations in particular areas of interest at different times. We validated the remote sensing-based air quality measurements using Quito’s Air Quality Monitoring Network (REEMAQ) data. Results showed stronger correlations between ground and remote remotely sensed measurements for NO₂ (R² =0.61 and RMSE= 2.669 for the training data; R²= 0.58 and RMSE=2.627 for validation data) than for any other pollutants. The product is available at the link https://cesarivanalvarezmendoza.users.earthengine.app/view/sentinel5p. Diverse municipalities can replicate the application in developing countries with insufficient air quality monitoring resources. In addition, intuitive tools, such as those developed in this study, could help promote air quality policies to improve urban citizens’ living standards.