Abstract. Scientific evidence has demonstrated that deterioration of ambient air quality has increased the number of deaths worldwide by appointing air pollution among the most pressing sustainability concerns. In this context, the continuous monitoring of air quality and the modelling of complex air pollution patterns is critical to protect population and ecosystems health. Availability of air quality observations has terrifically improved in the last decades allowing – nowadays – for extensive spatial and temporal resolved analysis at both global and local scale. Satellite remote sensing is mostly accountable for this data availability and is promising to foster air quality monitoring in support of traditional ground sensors measurements. In view of the above, this study compares observations from the Sentinel-5P mission of the European Copernicus Programme (the most recent Earth Observation platform providing open measurements of atmospheric constituents) with traditional ground measurements to investigate their space and time correlations across the Lombardy region (Northern Italy). The correlation analysis focused on nitrogen dioxide. The use of data collected during the COVID-19 pandemic allowed for a parallel exploration of the lockdown effects on nitrogen dioxide emissions. Results show a marked decrease in nitrogen dioxide concentrations during the lockdown and an overall strong positive correlation between satellite and ground sensors observations. These experiments are preparatory for future activities that will focus on the development of satellite-based air quality local prediction models, aiming at improving the granularity of the ground-based information available for air quality monitoring and exposure modelling.

Abstract. In recent years, geospatial big data has been generated at a very high speed, and the data volume is becoming increasingly massive. In order to realize the full potential of geospatial big data, there has been a strong requirement and push to embrace the value of open science. However, it is still challenging to preserve the privacy and integrity of geospatial data in data sharing and management. Blockchain as d distributed ledger technology has a series of good characteristics, such as decentralization, trust-free, transparency, tamper-free, consensus and security, etc. These characteristics of blockchain are beneficial for facilitating geospatial data sharing and management, and hence promoting the development of open GIS. In this paper, we provide a comprehensive review on the literature that involves how blockchain technology is applied to geospatial data, especially in geospatial data privacy and integrity preservation. First, the background knowledge on geospatial data privacy and blockchain technology are introduced. Then, we reviewed how blockchain technology is applied to geospatial data, followed by the conclusion of the topic. This review is beneficial for understanding how blockchain technology can be applied to geospatial domain by integrating geospatial technologies like GIS and remote sensing.

Abstract. Internet decentralization nowadays represents a critical topic to be addressed. It protects the users’ privacy, promotes data ownership, eliminates single points of failure and data censorship. An element that has an important role in decentralization is blockchain technology. Although blockchain has revolutionised sectors like the financial one with Bitcoin, there are still some fields where it needs to be further developed. One of these is geospatial data sharing and citizen science, where features like decentralization, immutability and transparency are needed. This study focuses on the description of a decentralized application developed specifically for geospatial data-point sharing and validation. As an example, the Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems (SIMILE) is used. This application is developed in the Velas blockchain infrastructure and implements a combination of a Discrete Global Grid System (DGGS) with smart contracts. Two types of smart contracts were created, a cell and a registry smart contract. The cell smart contracts are individual for each DGGS partition and contain the list of observations present in a specific area. The registry smart contracts keep track of all the DGGS cells added to the system. Currently, SIMILE observations are validated by public authorities, which requires time that is not always available. Therefore, a fully working prototype was developed to solve this. Here users can add and manage personal observations and validate the ones belonging to other users. This work demonstrates the feasibility of creating decentralized applications for geographical data validation as a citizen science solution.

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.

Abstract. Nowadays, the amount of open geospatial data delivered e.g. by private and public entities, such as environmental agencies, enables outstanding possibilities to any user interested in investigating real-world phenomena. However, the availability of such information presents several challenges in terms of its practical use. These are mainly connected to the heterogeneity of data sources, formats and processing tools which have to be mastered by the user to obtain the desired results. As a relevant example, air quality monitoring requires the integration of multiple data with different spatial and temporal granularities that are often distributed by more than one provider using not uniform formats and access methods. Besides traditional air pollution ground sensors observations, novel data sources have emerged. Among them, the Sentinel-5P mission of the European Copernicus Programme is one of the most recent Earth Observation platforms providing estimates of air pollutants with daily global coverage. These estimates are promising to foster air quality analysis and monitoring by complementing ground sensors observations. Therefore, the development of data handling and analysis strategies – allowing users for a smooth integration of satellite and ground sensor observations – is key to support future air quality studies. To that end, the present work investigates the use of the Open Data Cube as a single data endpoint to incorporate ground sensors and satellite observations into local air pollution analyses. A preliminary implementation is presented using the Lombardy region (Northern Italy) as a case study.