The main objective of GreenEO is to provide improved satellite-based environmental information to support sustainable nature protection practices across four competing land-use areas: cities, agricultural areas, forests, and ecosystems. Through combined exploitation of new Earth Observation products, digital infrastructure, and atmospheric and land surface models using machine learning and data assimilation, GreenEO will provide environmental information and stakeholder engagement practices to support the implementation of the Zero Pollution (ZP) and Biodiversity aspects of the European Green Deal (EGD). The project will serve as a proof-of-concept to enhance specific services provided by Copernicus and EUMETSAT in support of the EGD implementation. GreenEO aims to support the transition towards more sustainable land use practices in Europe and proposes a transformative governance system, relying on a data value chain enhanced with active stakeholder engagement and co-creation. The value chain proposes the uptake of satellite data, integrated in a modelling development chain and followed by value creation modelling applications. In cities, GreenEO will develop three satellite-based approaches of high-resolution urban-scale air quality maps to engage relevant stakeholders to support the ZP strategies. For agriculture, GreenEO will address impacts on nitrogen emissions, deposition, and biodiversity. It will create satellite-based high-resolution emission inventories, deposition, critical load exceedance maps, and identify areas with high recovery potential. GreenEO will develop a forest fire service that will revolutionize the monitoring of wildfires over Europe. GreenEO will create novel ecosystem monitoring indicators such as city greening indicators, ecosystem area and condition indicators. GreenEO will advance seamless climate-weather and environmental services in Europe, by proactive links to EUMETSAT, WMO, the Copernicus Services, EEA and EU Missions.

Climate warming is driven by increased concentrations of greenhouse gasses (GHGs) e.g., CO2 and CH4, in the atmosphere. Existing observatories are able to capture GHG information for large-scale global assessments, but short-term natural variability and climate-driven changes in atmospheric CO2 and CH4 remain less known. There is also currently a lack of sufficiently precise, autonomous, and cost-efficient GHG sensors for GHG monitoring at sufficient spatial scale, and in hard-to-reach areas. MISO will develop and demonstrate an autonomous in-situ observation platform for use in hard to reach areas (Arctic, wetlands), for detecting and quantifying carbon dioxide and methane gasses, using a combination of stationary and mobile (drone) solutions and requiring minimum on-site intervention when deployed. To achieve this objective, MISO will improve detection limit and accuracy of a NDIR GHG sensor, which will then be used in three observing platforms (a static tower, a static chamber and a UAV-mounted sensor) operated with the help of a central base unit. All elements will be designed for operation in harsh environments and with minimum human intervention. The static observatories will be powered by a unique geothermal device. Communication between the three observatories and a data cloud will use a combination of P2P, G4/G5/LTE, LORAWAN and wifi technologies. The specifications of the platform will be co-developed with stakeholders from academia, monitoring and measurement systems, industry and policy. A clear DCE strategy and focus on short-term impact management and medium and long-term commercialization will target several user groups including industries and representatives of main monitoring systems and infrastructures (e.g., ICOS). This will support innovative governance models and science-based policy design, implementation and monitoring. Sustainability performance and competitiveness in the domains covered by HE Cluster 6 will be enhanced.

The Global Stocktake, and the success of the Paris Agreement, hinges on the information nations provide about their emissions through National Greenhouse Gas Inventories (NGHGIs). Current methodologies laid-out by the IPCC for reporting emissions are generally built around the use of statistical data and emission factors. Although they are designed to be transparent, they can have significant uncertainties owing to incomplete or inaccurate information. The 2019 refinement of the IPCC Guidelines highlights the need for independent verification of NGHGIs especially using atmospheric observations. However, the technical complexity and the hitherto limited resolving power of atmospheric constraints makes it challenging for NGHGI compilers to adopt this type of verification. EYE-CLIMA will address this need for independent verification by developing observation-based methods (using both satellite remote sensing and ground-based observations) to a level of readiness where they can be used to determine emissions at national and sub-national scales and for verification of NGHGIs. The methodology involves using process-based and data-driven models to simulate GHG fluxes, first without atmospheric observations, then these fluxes are then combined with models of atmospheric transport and chemistry to assimilate atmospheric observations, which are used to correct the first flux estimates. Through engagement with stakeholders, i.e., NGHGI compilers, EYE-CLIMA, will develop flux data products for CO2 (LULUCF sector), CH4, N2O, and emissions data of F-gases (SF6, HFC-23, HFC-143a, HFC-125, HFC-134a, HFC-32) and black carbon (BC), which will be tailored to their needs. The fluxes will be attributed to natural versus anthropogenic sources, and for the latter, to source sectors that can be compared with groups of IPCC sectors in NGHGIs. The methodology for the atmospheric inversions and how to use these for verification of NGHGIs will be described in best practice guidelines.

INQUIRE aims to protect citizen health by providing knowledge, tools, and measures to substantially improve indoor air quality (IAQ). We will conduct research and evaluate innovative actions to reduce hazardous chemical and biological determinants in homes, positively impacting the health of residents. INQUIRE will focus particularly on infants and young children (<5 years old) as highly sensitive groups that spend a substantial time in the home environment. INQUIRE will comprehensively advance our understanding of the determinants of IAQ in homes by implementing innovative, low-cost, non-invasive sampling strategies (sensors, indoor/outdoor passive sampling, urine biomonitoring) to characterize determinants of household IAQ and their importance to human exposure. To capture the breath of IAQ determinants across Europe, the study will monitor for one month over 200 homes distributed across 8 countries, covering a gradient of conditions in each country. Tiered high-resolution chemical and biological screening techniques and wide-scope holistic characterisation of hazards will provide a comprehensive assessment of the determinants of IAQ. Multifaceted data analysis techniques (including machine learning, exposure modelling, geospatial analysis), will link chemical, biological and toxicity profiles with drivers of IAQ to identify sources and prioritize pollutants. Source identification will feed directly into the testing of both novel technologies and readily deployable strategies to improve IAQ, resulting in evidence-based recommendations and a draft of policy strategy for developing IAQ standards. INQUIREs Open Science approach and generated FAIR data on hazardous determinants, their effects, risk factors and sources will endorse continuous exploitation of results. Open dissemination of generated knowledge will raise citizen awareness while exploitation by industry and policy makers will endorse a transition towards homes with zero pollution. The project INQUIRE is part of the European cluster on indoor air quality and health (name and acronym to be decided)

The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner. To reach this objective, we propose creating the FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing, and dissemination, to unleash the potential of environmental, biodiversity and climate data through dedicated European data spaces. Within this project, TRL 7 will be attained, together with the necessary governance aspects to assure continued maintenance of the FAIRiCUBE HUB beyond the project lifespan. This projects goal is to leverage the power of Machine Learning (ML) operating on multi-thematic datacubes for a broader range of governance and research institutions from diverse fields, who at present cannot easily access and utilize these potent resources. Selected use cases will illustrate how data-driven projects can benefit from cube formats, infrastructure, and computational benefits. They will guide us in creating a user-friendly FAIRiCUBE HUB, which is tightly integrated to the common European data spaces, providing relevant stakeholders an overview of both data and processing modules readily available to be applied to these data sources. Tools enabling users not intimately familiar with the worlds of EO and ML to scope the requirements and costs of their desired analyses will be implemented, easing uptake of these resources by a broader community. The FAIR sharing of results with the community will be fostered by providing easy to use tools and workflows directly in the FAIRiCUBE HUB.