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Found 7 results.

Chemometers for in situ risk assessment of mixtures of pollutants (CHEMO-RISK)

Das Projekt "Chemometers for in situ risk assessment of mixtures of pollutants (CHEMO-RISK)" wird vom Umweltbundesamt gefördert und von Helmholtz-Zentrum für Umweltforschung GmbH durchgeführt. Im Rahmen von CHEMO-RISK soll ein neues Verfahren zur Risikobewertung von Mischungen von Umweltchemikalien erarbeitet werden. Hierzu wird zunächst die Gesamtheit der Chemikalien aus einer Vielzahl von Proben aus der aquatischen Umwelt, marinen Säugetieren und dem Menschen mit Hilfe von passiven Probenehmern weitgehend frei von störenden Probenbegleitstoffen in einen Lösungsmittelextrakt überführt. Dieser Extrakt wird dann (a) mittels chemischer Analytik auf das Vorhandensein und ggf. die Menge an Einzelsubstanzen untersucht und es werden (b) mit Hilfe von zellbasierten Biotestverfahren Mischungseffekte der Gesamtheit an organischen Schadstoffen charakterisiert.

Simulation and Understanding of the Atmospheric Radical Budget for Regions with Large Emissions from Plants (SARLEP)

Das Projekt "Simulation and Understanding of the Atmospheric Radical Budget for Regions with Large Emissions from Plants (SARLEP)" wird vom Umweltbundesamt gefördert und von Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung, Troposphäre (IEK-8) durchgeführt. Atmospheric pollutants emitted by natural and anthropogenic sources influence significantly the quality of life on our planet. Their removal in the atmosphere is controlled by their reactions with photochemically produced hydroxyl radicals. Recent findings from experimental studies and quantum-chemical calculations suggest that an important part of atmospheric radical chemistry, which is directly linked to the self-cleansing ability of our atmosphere, has been overlooked. This causes considerable uncertainty in our understanding of the couplings between the biosphere, atmospheric chemistry and climate. The greatest impact of this lack of understanding has been found for regions with large emissions of organic compounds from plants in remote or rural areas. Within this project, the oxidation of organic compounds will be comprehensively investigated for the most important, biogenic organic compounds. The innovative experimental approach will quantify the radical destruction and production rates in experiments in the unique atmosphere simulation chamber SAPHIR at the host institution. These experiments aim to close the gap between laboratory and field studies. The advantages are: (1) Experiments will be conducted under atmospherically relevant conditions. (2) Radical recycling efficiency will be quantified for the entire chemical system, not just for single reactions. (3) The complexity of the chemical system studied will be increased from single compounds to natural plant emissions. New innovative instrumentation will be developed for accurate and precise measurements of radical species and oxidized organic compounds. These are also of great interest beyond this project. The results of this project will improve our understanding of atmospheric radical chemistry required for accurately predicting the atmospheric radical budget, the formation of harmful secondary pollutants such as ozone, acids and aerosol and the lifetime of greenhouse gases affecting climate change.

OCEAN SENTINEL

Das Projekt "OCEAN SENTINEL" wird vom Umweltbundesamt gefördert und von Centre National de la Recherche Scientifique durchgeführt. Fisheries are operating worldwide over nation's Economic Exclusive Zones (EEZ) as well as over international waters. Information on the location of fishing is rarely known, especially in international waters, yet it is critical since in many oceanic sectors non declared and illegal fisheries are affecting negatively ecosystems through over exploitation and by catch of non-target species Knowledge about the distribution of fishing boats is fundamental for the regulation of fishing activities as well for the conservation of the oceans. I propose a New Concept of ocean surveillance based on new bio-logging technologies fitted on large foraging marine predators. The OCEAN SENTINEL Proof of this Concepts will (1) develop a logger called CENTURION that couple a XGPS platform detecting and locating radar emissions, with a satellite transmission system (Argos) that would send instantaneously the location of vessels to a receiving site, (2) deploy the logger on wide ranging animals used as platforms and (3) make available immediately the information obtained from the CENTURION logger through a website. The OCEAN SENTINEL programme will generate important social benefits by providing information to a wide range of beneficiaries, from governments or regional authorities managing EEZ and natural resources, regional or national fishing authorities, researchers and non-governmental organisation in conservation. The concept will be tested in the Southern Indian Ocean from Crozet and Kerguelen Islands where valuable and extensive fisheries operate in EEZs and over oceanic waters. This concept has the potential to be used in other areas where information on fisheries location is needed. The project will also lead to further discoveries on the relationship between seabirds and fisheries as well on the extent of fisheries in zones where surveillance by conventional methods is not possible.

Statistical Learning for Earth Observation Data Analysis (SEDAL)

Das Projekt "Statistical Learning for Earth Observation Data Analysis (SEDAL)" wird vom Umweltbundesamt gefördert und von Universidad Valencia durchgeführt. SEDAL is an interdisciplinary project that aims to develop novel statistical learning methods to analyze Earth Observation (EO) satellite data. In the last decade, machine learning models have helped to monitor land, oceans, and atmosphere through the analysis and estimation of climate and biophysical parameters. Current approaches, however, cannot deal efficiently with the particular characteristics of remote sensing data. In the coming few years, this problem will largely increase: several satellite missions, such as the operational EU Copernicus Sentinels, will be launched, and we will face the urgent need to process and understand huge amounts of complex, heterogeneous, multisource, and structured data to monitor the rapid changes already occurring in our Planet. SEDAL aims to develop the next generation of statistical inference methods for EO data analysis. We will develop advanced regression methods to improve efficiency, prediction accuracy and uncertainties, encode physical knowledge about the problem, and attain self-explanatory models learned from empirical data. Even more importantly, we will learn graphical causal models to explain the potentially complex interactions between key observed variables, and discover hidden essential drivers and confounding factors. This project will thus aboard the fundamental problem of moving from correlation to dependence and then to causation through EO data analysis. The theoretical developments will be guided by the challenging problems of estimating biophysical parameters and learning causal relations at both local and global planetary scales. The long-term vision of SEDAL is tied to open new frontiers and foster research towards algorithms capable of discovering knowledge from EO data, a stepping stone before the more ambitious far-end goal of machine reasoning of anthropogenic climate change.

Fundamental understanding of reactive nitrogen in the global upper troposphere (UpTrop)

Das Projekt "Fundamental understanding of reactive nitrogen in the global upper troposphere (UpTrop)" wird vom Umweltbundesamt gefördert und von University Leicester durchgeführt. The upper troposphere (UT), a severely under-researched part of the atmosphere, has profound impacts on global climate, air quality, major atmospheric oxidants, and the protective ozone layer. Key to its influence on the Earth system are reactive nitrogen compounds (collectively NOy). Models, since their inception, have grossly misrepresented observations of UT NOy, hindering application of these models to accurately estimate the impact of humans on climate, the ozone layer, and air quality. The reasons proposed for discrepancies between models and observations are unsatisfactory, as past studies have been hampered by observations that are limited in space and time. Only now are there unprecedented global, high-resolution measurements of the UT from instruments on aircraft and satellites that can be combined with detailed and advanced modelling tools to at last tackle this issue on a global scale. The ground-breaking UpTrop work programme will innovatively combine observations from the recently launched ESA Sentinel-5P mission and a long record of aircraft campaigns (most crucially the 2016-2018 NASA ATom campaign) to create the first truly global dataset of UT NOy abundance, interpreted with the state-of-the-art GEOS-Chem model. This pioneering multiplatform approach, the bedrock of my previous highly cited work, will deliver game-changing objectives: (i) novel insights into the processes controlling UT NOy, (ii) an unequivocal account of the role of the upper troposphere in altering climate and the chemical composition of the atmosphere, and (iii) interpretation of the disruptive impact of improved understanding of UT NOy on widespread application of satellite observations to constrain global air quality. UpTrop is ambitious, with bold objectives that will conceptually change fundamental understanding of UT NOy and address a challenge that has plagued atmospheric chemists for decades. A cascade of new avenues of cross-disciplinary research is inevitable.

Geodetic data assimilation: Forecasting Deformation with InSAR (GEO-4D)

Das Projekt "Geodetic data assimilation: Forecasting Deformation with InSAR (GEO-4D)" wird vom Umweltbundesamt gefördert und von Universite Paris VI, Ecole Normale Superieure durchgeführt. Recent space-based geodetic measurements of ground deformation suggest a paradigm shift is required in our understanding of the behaviour of active tectonic faults. The classic view of faults classified in two groups - the locked faults prone to generate earthquakes and the creeping faults releasing stress through continuous aseismic slip - is now obscured by more and more studies shedding light on a wide variety of seismic and aseismic slip events of variable duration and size. What physical mechanism controls whether a tectonic fault will generate a dynamic, catastrophic rupture or gently release energy aseismically? Answering such a fundamental question requires a tool for systematic and global detection of all modes of slip along active faults. The launch of the Sentinel 1 constellation is a game changer as it provides, from now on, systematic Radar mapping of all actively deforming regions in the world with a 6-day return period. Such wealth of data represents an opportunity as well as a challenge we need to meet today. In order to expand the detection and characterization of all slip events to a global scale, I will develop a tool based on machine learning procedures merging the detection capabilities of all data types, including Sentinel 1 data, to build time series of ground motion. The first step is the development of a geodetic data assimilation method with forecasting ability toward the first re-analysis of active fault motion and tectonic phenomena. The second step is a validation of the method on three faults, including the well-instrumented San Andreas (USA) and Longitudinal Valley faults (Taiwan) and the North Anatolian Fault (NAF, Turkey). I will deploy a specifically designed GPS network along the NAF to compare with outputs of our method. The third step is the intensive use of the algorithm on a global scale to detect slip events of all temporal and spatial scales for a better understanding of the slip behaviour along all active continental faults.

Fluorescence-based photosynthesis estimates for vegetation productivity monitoring from space (SENTIFLEX)

Das Projekt "Fluorescence-based photosynthesis estimates for vegetation productivity monitoring from space (SENTIFLEX)" wird vom Umweltbundesamt gefördert und von Universidad Valencia durchgeführt. Global food security will remain a worldwide concern for the next 50 years and beyond. Agricultural production undergoes an increasing pressure by global anthropogenic changes, including rising population, increased protein demands and climatic extremes. Because of the immediate and dynamic nature of these changes, productivity monitoring measures are urgently needed to ensure both the stability and continued increase of the global food supply. Europe has expressed ambitions to keep its fingers on the pulse of its agricultural lands. In response to that, this proposal - named SENTIFLEX - is dedicated to developing a European vegetation productivity monitoring facility based on the synergy of Sentinel-3 (S3) with FLEX satellite fluorescence data. ESA's 8th Earth Explorer FLEX is the first mission specifically designed to globally measure Sun-Induced chlorophyll Fluorescence (SIF) emission from terrestrial vegetation. These two European Earth observation missions offer immense possibilities to increase our knowledge of the basic functioning of the Earth's vegetation, i.e., the photosynthetic activity of plants resulting in carbon fixation. Two complementary approaches are envisioned to realize quantification of photosynthesis through satellite SIF and S3. First, the work seeks to advance the science in establishing and consolidating relationships between canopy-leaving SIF and unbiased estimates of photosynthesis of the plants, thereby disentangling the role of dynamic vegetative and atmospheric variables. Second, consolidated relationships between SIF and photosynthesis will be used to build a FLEX-S3 data processing assimilation scheme through process-based vegetation models that will deliver spatiotemporally highly resolved information on Europe's vegetation productivity. To streamline all these datasets into a prototype vegetation productivity monitoring facility, new data processing concepts will be introduced such as the emulation of radiative transfer models.

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