Summary
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.
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.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101089203 |
| Start date: | 01-07-2023 |
| End date: | 30-06-2028 |
| Total budget - Public funding: | 1 999 848,00 Euro - 1 999 848,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2022-COGUpdate Date
31-07-2023
Images
No images available.
Geographical location(s)
