Search results

Reliable satellite estimates of chlorophyll-a concentration (Chl-a) are needed in coastal waters for applications such as eutrophication monitoring. However, because of the optical complexity of coastal waters, retrieving accurate Chl-a is still challenging. Many algorithms exist and give quite different performance for different optical conditions but there is no clear definition of the limits of applicability of each algorithm and no clear basis for deciding which algorithm to apply to any given image pixel (reflectance spectrum). Poor quality satellite Chl-a data can easily reach end-users. To remedy this and provide a clear decision on when a specific Chl-a algorithm can be used, we propose simple quality control tests, based on MERIS water leaving reflectance (ρw) bands, to determine on a pixel-by-pixel basis if any of three popular and complementary algorithms can be used. The algorithms being tested are: 1. the OC4 blue-green band ratio algorithm which was designed for open ocean waters; 2. the OC5 algorithm which is based on look-up tables and corrects OC4 overestimation in moderately turbid waters and 3. a near infrared-red (NIR-red) band ratio algorithm designed for eutrophic waters. Using a dataset of 348 in situ Chl-a / MERIS matchups, the conditions for reliable performance of each of the selected algorithms are determined. The approach proposed here looks for the best compromise between the minimization of the relative difference between In situ measurements and satellite estimations and the number of pixels processed. Conditions for a reliable application of OC4 and OC5 depend on ρw412/ρw443 and ρw560, used as proxies of coloured dissolved organic matter and suspended particulate matter (SPM), as compared to ρw560/ρw490, used as a proxy for Chl-a. Conditions for reliable application of the NIR-red band ratio algorithm depend on Chl-a and SPM. These conditions are translated into pixel-based quality control (QC) tests with appropriately chosen thresholds. Results show that by removing data which do not pass QC, the performance of the three selected algorithms is significantly improved. After combining these algorithms, 70\% of the dataset could be processed with a median absolute percent difference of 30.5\%. The QC tests and algorithm merging methodology were then tested on four MERIS images of European waters. The OC5 algorithm was found to be suitable for most pixels, except in very turbid and eutrophic waters along the coasts where the NIR-red band ratio algorithm helps to fill the gap. Finally, a test was performed on an OLCI-S3A image. Although some validations of water reflectance are still needed for the OLCI sensors, results show similar behavior to the MERIS applications which suggests that when applied to OLCI data the present methodology will help to accurately estimate Chl-a in coastal waters for the next decade.

Cable bacteria (Deltaproteobacteria, Desulfobulbaceae) are long filamentous sulfur-oxidizing bacteria that generate long-distance electric currents running through the bacterial filaments. This way, they couple the oxidation of sulfide in deeper sediment layers to the reduction of oxygen or nitrate near the sediment-water interface. Cable bacteria are found in a wide range of aquatic sediments, but an accurate procedure to assess their abundance is lacking. We developed a qPCR approach that quantifies cable bacteria in relation to other bacteria within the family Desulfobulbaceae. Primer sets targeting cable bacteria, Desulfobulbaceae and the total bacterial community were applied in qPCR with DNA extracted from marine sediment incubations. Amplicon sequencing of the 16S rRNA gene V4 region confirmed that cable bacteria were accurately enumerated by qPCR, and suggested novel diversity of cable bacteria. The conjoint quantification of current densities and cell densities revealed that individual filaments carry a mean current of ~110 pA and have a cell specific oxygen consumption rate of 69 fmol O2 cell-1 day-1. Overall, the qPCR method enables a better quantitative assessment of cable bacteria abundance, providing new metabolic insights at filament and cell level, and improving our understanding of the microbial ecology of electrogenic sediments.

Au Proche-Orient, des sépultures appartenant à des hommes, des femmes et des individus immatures, ont été retrouvées sous le sol des maisons néolithiques en Anatolie Centrale, au Levant Nord, au Levant Sud, et dans la Djezirah Iraquienne. Au sein de ses sépultures, les individus reposaient individuellement ou à plusieurs (simultanément ou successivement) dans une position évoquant celle d’un fœtus au sein de l’utérus. Les études sur leur contexte domestique et sur leur organisation spatiale sont des principales clefs pour l’interprétation des idées religieuses derrière ces pratiques. Dans le présent article, nous allons présenter quelques aspects des pratiques funéraires au Néolithique proche-oriental qui concernent surtout la gestion de l’espace funéraire à Çatalhöyük.

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