Descrizione del progetto
Un approccio rivoluzionario al monitoraggio delle emissioni
I cambiamenti climatici minaccino di avere conseguenze ambientali imprevedibili se non affrontiamo di petto le emissioni di gas a effetto serra. La sfida? Misurare accuratamente le emissioni globali di gas serra e scoprire le fonti sconosciute. Si tratta di un compito ancora molto difficile e gli inventari attuali si basano su imprecisi calcoli dal basso verso l’alto. Il progetto CoSense4Climate, finanziato dal Consiglio europeo della ricerca, sfrutterà la teoria matematica del Compressed Sensing. L’obiettivo è rivoluzionare l’inversione atmosferica. Sfruttando la potenza del Compressed Sensing, tipicamente utilizzato nell’elaborazione dei segnali e delle immagini, il progetto promette un’accuratezza senza pari nella quantificazione e nella localizzazione delle emissioni di gas serra, pur richiedendo meno dati. Nel complesso, il progetto mira a stabilire un nuovo standard nel monitoraggio delle emissioni di gas serra, offrendo nuove speranze nella lotta ai cambiamenti climatici.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
- 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
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
80333 Muenchen
Germania