Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements

Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO2 , which is one of the most harmful pollutants, has the potential to cause diseases such as Chronic Obstructive Pulmonary Disease (COPD). Unfortunately, not all countries have local atmospheric pollutant monitoring networks to perform ground measurements (especially Low- and Middle-Income Countries). Although some alternatives, such as satellite technologies, provide a good approximation for tropospheric NO2 , these do not measure concentrations at the ground level. In this work, we aim to provide an alternative to ground sensor measurements. We used a combination of ground meteorological measurements with satellite Sentinel-5P observations to estimate ground NO2 . For this task, we used state-of-the-art Machine Learning models, linear regression models, and feature selection algorithms. From the results obtained, we found that a Multi-layer Perceptron Regressor and Kriging in combination with a Random Forest feature selection algorithm achieved the lowest RMSE (2.89 µg/m3 ). This result, in comparison with the real data standard deviation and the models using only satellite data, represented an RMSE decrease of 55%. Future work will focus on replacing the use of meteorological ground sensors with only satellite-based data

GeoPriv: QGIS geoprivacy plugin

Los datos de los usuarios en plataformas digitales son constantemente blanco de ataques; particularmente los datos geográficos son usados para campañas de phishing geolocalizado, ataques de profiling e incluso en rastrear y atacar físicamente a personas. Ante este problema la comunidad científica ha propuesto distintos mecanismos para la protección de la privacidad de la localización (LPPMs), los cuales han tenido muy poca acogida en la industria debido a su complejidad, su fuerte componente teórico y la falta de herramientas para su fácil aplicación. Esto ocasiona que muy pocas compañías los usen. GeoPriv es un proyecto que abarca el diseño y creación de un plugin de QGIS para la fácil aplicación de LPPMs mediante la integración e implementación de diversos mecanismos de protección de privacidad basados en localización. Además, al ser Open Source y estar en el repositorio de plugins de QGIS, está abierto a la comunidad para que se puedan agregar más LPPMs. Este plugin le brinda a la comunidad una herramienta para la geoprivacidad, para que los datos que se vayan a publicar, exportar, compartir o a plasmar en mapas cuenten con protección a la privacidad. Con este plugin solo basta cargar los datos a procesar y observar la representación geográfica del resultado. Además, cuenta con indicadores para tener un estimado de la similitud entre los datos originales y procesados luego de la aplicación de los LPPMs. La documentación de los mecanismos implementados y tutoriales de uso se encuentran en la página web https://diuke.github.io/, el plugin puede ser descargado en el repositorio oficial de complementos de QGIS en https://plugins.qgis.org/plugins/GeoPriv/ o desde el administrador de complementos de QGIS.User data on digital platforms is a constant target of attacks; particularly the geographic data, which is used for geolocated phishing campaigns, profiling attacks, tracking and on physical attacks on people. To face this problem, the scientific community has proposed different Location Privacy Protection Mechanisms (LPPMs), which have had little reception in the industry due to its complexity, its strong theoretical component and the lack of tools for its easy application. This causes very few companies to use them. Geopriv is a project that involves the design and creation of a QGIS plugin for an easy application of LPPMs through the integration and implementation of various protection mechanisms based on location privacy. In addition, the plugin is open source and in the official QGIS plugin repository, which means that it is open for the community to contribute by adding more LPPMs. This plugin gives the community a reliable tool for geoprivacy, so data that is going to be published, exported, shared or translated into maps has privacy protection. With this plugin, it is enough to load the data to be processed and observe the geographical representation of the result. Additionaly, it has indicators to have an estimation of the similarity between the original data and processed data after the application of the LPPMs. The documentation of the implemented mechanisms and usage tutorials can be found on the website https://diuke.github.io/ and the plugin can be downloaded from the official plugin QGIS repository at https://plugins.qgis.org/plugins/GeoPriv/ or from the QGIS add-ons administrator