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The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the water-leaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis.

One of the greatest challenges of the last decades in the fight against climate change has been to achieve net-zero emissions by mid-century. According to the US EPA (2016), in 2014, global anthropogenic emissions of carbon dioxide (CO2) accounted for ~64% of the greenhouse effect. Carbon dioxide capture and storage (CCS) plays an irreplaceable part as a mitigation technology that avoids CO2 emissions at their source and bridges the transition into a non-carbon-based energy future. The International Energy Agency (IEA) estimates that the need to store CO2 will grow from 40 Mt/y at present to more than 5000 Mt/y by 2050. Additionally, in the IEA’s Sustainable Development Scenario, which aims for global net-zero CO2 emissions from the energy sector by 2070, CCS needs to become a global industry supporting emissions reductions across the overall energy system. CCS technologies essentially consist of capturing and compressing the CO2 at the source and then transport it towards deep suitable rock formations where it is injected to be permanently stored. The key to successful and permanent CO2 storage is the proper analysis and characterization of the reservoir and seal formation. Among the types of reservoir suitable for CO2 storage are unmined coal beds, depleted oil and gas fields, EOR/EGR, saline aquifers, man-made caverns, and basaltic formations (IPCC, 2005). The storage capacity of any of these reservoirs is the subsurface commodity whose quantities and properties are assessed when existing data is provided. Capacity estimations bring their own level of uncertainty and complexity according to the scale at which they are addressed and the nature of the geological conditions of the reservoir. This degree of uncertainty should be accounted for in every estimation (Bradshaw et al., 2007) Resource classification systems (RCS) are frameworks that establish the principles and boundaries for each level of capacity assessment. By making use of these frameworks, it is possible to properly allocate the stage of development of a resource (United Nations, 2020). For every level of assessment, the principles of the estimation change and so do the scale and purpose. As the analysis moves forward, a prospective site develops and exhaustive information is acquired, initial estimations are adjusted, and uncertainty is likely to reduce. Additionally, different economic, technical, regulatory, environmental and societal factors are integrated into the assessment to bring the estimations under present conditions. For instance, if the storage capacity is to be matched with a CO2 source, detailed simulations and analyses regarding injectivity, supply rate, potential routes and economic distances must be performed to achieve a realistic estimation. However, an assessment where the main goal is to merely quantify the space available to store CO2 in a reservoir, does not consider the aforementioned limitations and will carry higher risk and uncertainty in its estimation (Bradshaw et al., 2007). Even though resource classification systems provide a solid foundation for CCS projects, they do not provide the input parameters and analyses needed to reach every level of assessment. This is why storage capacity estimation methodologies go hand in hand with RCS given that the former can give information related to the parameters and constraints considered in the estimation. No standard process has been proposed that can be followed from the starting level of a CO2 storage capacity assessment until a fully developed carbon storage resource; that is, a CO2 storage site ready to become fully operational. This paper aims to develop a methodology where the fundamental steps needed to go through every level of the resource classification systems are standardized. This methodology intends to serve as a general baseline that, regardless of the geological settings and techno-socio-economic conditions, can be adopted for any CCS assessment. The proposed methodology is built by reviewing the available capacity estimation methods for every level of assessment and identifying social, technical and economic aspects that come into play as the resource is being developed. Considering that capacity estimation methodologies can vary their approach even for the same level of assessment, the rationales behind them are expected to be determined. Such rationales can be related to in-place policy restrictions, geographical economic behavior, or the nature of the parameters contemplated. Additionally, PSS, an in-house developed tool that can assess CO2 storage reservoirs at different levels, will be proposed within the methodology. This tool is a bottom-up geotechnical and economic forecasting simulator that can generate source-sink matching for CCS projects, where technical, economic, and geological uncertainties are handled through a Monte Carlo approach for limited foresight (Welkenhuysen et al., 2016). Acknowledgements This research is carried out under the LEILAC2 project, which receives funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 884170. The LEILAC2 consortium consists of: Calix Europe SARL, HeidelbergCement AG, Ingenieurbüro Kühlerbau Neustad GmbH (IKN), Centre for Research and Technology Hellas (CERTH), Bundesanstalt für Geowissenschaften und Rohstoffe (BGR), Politecnico di Milano (POLIMI), Geological Survey of Belgium (RBINS-GSB), ENGIE Laborelec, Port of Rotterdam, Calix Limited, CIMPOR-Indústria de Cimentos SA and Lhoist Recherche et Development SA. References Bradshaw, J., Bachu, S., Bonijoly, D., Burruss, R., Holloway, S., Christensen, N. P., & Mathiassen, O. M. (2007). CO2 storage capacity estimation: Issues and development of standards. International Journal of Greenhouse Gas Control, 1(1), 62–68. https://doi.org/10.1016/S1750-5836(07)00027-8 IPCC. (2005). Carbon Dioxide Capture and Storage. https://www.ipcc.ch/report/carbon-dioxide-capture-and-storage/ United Nations. (2020). United Nations Framework Classification for Resources: Update 2019. UN. https://doi.org/10.18356/44105e2b-en US EPA. (2016). Global Greenhouse Gas Emissions Data. US EPA. https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data Welkenhuysen, K., Brüstle, A.-K., Bottig, M., Ramírez, A., Swennen, R., & Piessens, K. (2016). A techno-economic approach for capacity assessment and ranking of potential options for geological storage of CO2 in Austria. Geologica Belgica. http://dx.doi.org/10.20341/gb.2016.012

The battery market for electric vehicles and renewable energy storage is dominated by rechargeable lithium-ion batteries, making lithium supply essential to climate action through decarbonization. In 2019, more than half of the world's lithium was sourced from lithium pegmatites of the Li-Cs-Ta (LCT) family, predominantly from Australia. Current global lithium supply involves long diesel-fueled maritime transport routes, which counteracts lithium's role in climate action. Responsible consumption and production require shorter supply chains from deposit to battery. Reading the mineralogical record of LCT pegmatite deposits can help address the challenge of reducing the climate impact of lithium production, by informing deposit models, mineral exploration, and geometallurgy, therefore promoting local supply. Our research focuses on a belt of LCT pegmatites, which is located along the eastern margin of the late-Caledonian S-type Leinster Batholith, southeast Ireland. The LCT pegmatites are hosted by a major regional shear zone and are part of a tin-lithium province that stretches subparallel to the Iapetus suture from Europe through Nova Scotia to North and South Carolina. We investigated crystal chemical zoning in muscovite, cassiterite, and columbite-tantalite using petrography, scanning electron microscopy, and LA-ICP-MS chemical mapping. The zoning patterns record that pegmatite rare element mineralization resulted from an interplay of magmatic crystallization, metasomatism, and hydrothermal processes. Late-stage metasomatic alteration led to partial resorption of early minerals including the lithium ore-mineral spodumene, followed by dispersion of lithium and other rare elements into country rocks, mostly within dark mica. Dispersion led to formation of geochemical halos around the LCT pegmatites with the potential to use country-rock lithogeochemistry and mica composition as geochemical vectoring tools. Geochemistry of mica in the granite host analyzed by handheld LIBS has been found to exhibit coherent spatial patterns occurring adjacent to and above LCT pegmatites known at depth from drilling. These channels of mineral-specific geochemical information are distinct from soil geochemical patterns and are not influenced by the same secondary, surface processes such as dilution. As outcrop is virtually absent in the study area, regional stream sediment geochemistry data (Geological Survey Ireland) was assessed as an LCT pegmatite exploration tool. After correcting for geologic background using a linear regression approach, catchments containing LCT pegmatites show high residuals for concentrations of both tantalum and tin. The mineralogy of stream sediment samples from a subsample of these catchments was subsequently analyzed to characterize the host minerals of tin and tantalum. Cassiterite and columbite-tantalite were identified, and both show geochemical and textural signatures that correspond to the zoning patterns mentioned above, which indicates that these minerals were derived from the local LCT pegmatites. These results suggest that, when regional geology and tectonic setting are prospective, lithium pegmatite prospectivity can be further assessed for tin-tantalum associations in (often publicly available) regional stream sediment data. Following geospatial analysis, stream sediment samples could be obtained from individual prospective catchments and their mineralogy analyzed. Local-scale geochemical surveys could follow where stream sediments of prospective catchments contain tin and tantalum oxides with chemistries and textures indicative of a lithium pegmatite source.