Summary
Aerosol particles are ubiquitous constituents in the ambient atmosphere. Ultrafine particles (< 10 nm) show adverse effects on our public health, as inhalation leads to an elevated risk of lung- and cardiovascular diseases. Aerosol particles also influence the global climate, by scattering sunlight away from Earth's surface and acting as seeds for cloud droplet formation. Combined, these effects lead to an overall cooling of the Earth, directly counteracting the warming effect of greenhouse gases. According to the IPCC, aerosol particles pose the largest uncertainty in global climate forecast. This uncertainty is caused by the lack of understanding of the early growth behaviour of small (< 3 nm) particles. The largest source of aerosol particles (50-90%) is from nucleation of vapours in the air leading to a burst of freshly nucleated particles (FNPs) of 1-2 nm in size. However, even the basic fundamental properties of these FNPs remain unknown and cannot be studied using currently available experimental techniques. I propose a unique approach to target the properties of FNPs by applying a versatile suite of computational methods, ranging from quantum chemical calculations to application of conceptually new machine learning models. The scientific objectives are: 1) To determine the chemical composition and stability of FNPs. 2) To understand how FNPs evolve over time via exchange of vapours with the environment. 3) To investigate how FNPs transform as a consequence of chemical reactions occurring at the surface or inside the particles. The research will provide unprecedented insight into the molecular level properties of FNPs. This project will directly supply input parameters (chemical composition, thermodynamics and kinetics) for atmospheric models, which are crucial in order to constrain the large uncertainty in climate predictions caused by small aerosol particles.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101040353 |
| Start date: | 01-04-2022 |
| End date: | 31-03-2027 |
| Total budget - Public funding: | 1 462 491,00 Euro - 1 462 491,00 Euro |
Cordis data
Original description
Aerosol particles are ubiquitous constituents in the ambient atmosphere. Ultrafine particles (< 10 nm) show adverse effects on our public health, as inhalation leads to an elevated risk of lung- and cardiovascular diseases. Aerosol particles also influence the global climate, by scattering sunlight away from Earth's surface and acting as seeds for cloud droplet formation. Combined, these effects lead to an overall cooling of the Earth, directly counteracting the warming effect of greenhouse gases. According to the IPCC, aerosol particles pose the largest uncertainty in global climate forecast. This uncertainty is caused by the lack of understanding of the early growth behaviour of small (< 3 nm) particles. The largest source of aerosol particles (50-90%) is from nucleation of vapours in the air leading to a burst of freshly nucleated particles (FNPs) of 1-2 nm in size. However, even the basic fundamental properties of these FNPs remain unknown and cannot be studied using currently available experimental techniques. I propose a unique approach to target the properties of FNPs by applying a versatile suite of computational methods, ranging from quantum chemical calculations to application of conceptually new machine learning models. The scientific objectives are: 1) To determine the chemical composition and stability of FNPs. 2) To understand how FNPs evolve over time via exchange of vapours with the environment. 3) To investigate how FNPs transform as a consequence of chemical reactions occurring at the surface or inside the particles. The research will provide unprecedented insight into the molecular level properties of FNPs. This project will directly supply input parameters (chemical composition, thermodynamics and kinetics) for atmospheric models, which are crucial in order to constrain the large uncertainty in climate predictions caused by small aerosol particles.Status
SIGNEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
Images
No images available.
Geographical location(s)
