Description du projet
Une approche révolutionnaire de la surveillance des émissions
Le changement climatique menace d’avoir des conséquences environnementales imprévisibles si nous ne nous attaquons pas de front aux émissions de gaz à effet de serre (GES). Le défi? Mesurer avec précision les émissions mondiales de GES et découvrir des sources inconnues. Cette tâche reste extrêmement difficile, et les inventaires actuels reposent sur des calculs ascendants imprécis. Financé par le Conseil européen de la recherche, le projet CoSense4Climate s’appuiera sur la théorie mathématique de l’acquisition comprimée (CS pour «compressed sensing»). L’objectif est de révolutionner l’inversion atmosphérique. En exploitant la puissance de la CS, généralement utilisée dans le traitement des signaux et des images, le projet promet une précision inégalée dans la quantification et la localisation des émetteurs de GES, tout en nécessitant moins de données. Dans l’ensemble, il vise à établir une nouvelle norme en matière de surveillance des émissions de GES, offrant ainsi un nouvel espoir dans la lutte contre le changement climatique.
Objectif
Climate change is a defining issue of our time. Without significant, rapid greenhouse gas (GHG) emission reductions, we will face unpredictable consequences for climate and life. To effectively reduce GHGs, the emissions must be accurately quantified and unknown emitters found. However, measuring global GHG emissions is very challenging, and hence current emission inventories rely mostly on bottom-up calculations, which lack accuracy and the ability to detect unknown sources.
In CoSense4Climate, I will use the powerful mathematical theory of compressed sensing (CS) to revolutionize atmospheric inversion. My goal is to develop a method to locate, quantify, and attribute GHG emitters with unmatched spatial resolution and accuracy. CS has been used with great success in signal and image processing by taking advantage of the fact that most signals contain redundancies. Using CS in combination with domain transformations, I will generate accurate high resolution emission fields and reveal unknown sources, yet require less data than conventional methods. I will develop a CS inversion framework not only for local sensor data, but also for satellite data, which, upon success, will lead to a breakthrough in monitoring urban GHG emissions globally.
I am best suited to reach this goal. I have gathered a unique dataset with my fully automated differential column GHG network MUCCnet, the first of its kind. With my rich experience in applying computational fluid dynamics (CFD), solar-induced fluorescence (SIF), and machine learning (ML) for estimating GHG emissions, I will additionally create a high-resolution CFD-based atmospheric transport model, a satellite SIF-based urban CO2 biogenic flux model, and a ML method for source attribution based on ratios of GHG and air pollutant concentrations.
CoSense4Climate will establish a new standard for GHG emission monitoring, and provide ground-breaking scientific methods to help solve one of today’s most urgent problem: climate change.
Champ scientifique
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- engineering and technologyenvironmental engineeringair pollution engineering
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsfluid dynamicscomputational fluid dynamics
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Mots‑clés
- atmospheric science
- urban greenhouse gas emissions
- compressed sensing
- computational fluid dynamics
- solar-induced fluorescence
- machine learning
- sensor network
- atmospheric measurements
- atmospheric inverse modeling
- atmospheric transport modeling
- biogenic fluxes
- particle dispersion modeling
- ground based remote sensing
- satellite remote sensing
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
80333 Muenchen
Allemagne