Descripción del proyecto
Un método revolucionario para controlar las emisiones
El cambio climático amenaza con causar consecuencias imprevisibles para el medio ambiente a menos que luchemos contra las emisiones de gases de efecto invernadero (GEI). ¿El reto? Medir con precisión las emisiones mundiales de GEI y descubrir fuentes desconocidas. Esto sigue siendo extremadamente difícil, y los inventarios actuales se basan en cálculos ascendentes imprecisos. Financiado por el Consejo Europeo de Investigación, el equipo del proyecto CoSense4Climate aprovechará la teoría matemática de la detección comprimida. El objetivo es revolucionar la inversión atmosférica. Aprovechando la potencia de la detección comprimida, utilizada habitualmente en el tratamiento de las señales e imágenes, el equipo del proyecto promete una precisión sin igual en la cuantificación y localización de los emisores de GEI, al tiempo que requiere menos datos. En general, pretende establecer una nueva norma en el seguimiento de las emisiones de GEI, lo que ofrece una esperanza nueva en la lucha contra el cambio climático.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
- 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
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
80333 Muenchen
Alemania