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Simulating Non-Equilibrium Dynamics of Atmospheric Multicomponent Clusters

Periodic Reporting for period 4 - DAMOCLES (Simulating Non-Equilibrium Dynamics of Atmospheric Multicomponent Clusters)

Periodo di rendicontazione: 2020-12-01 al 2021-05-31

The aim of DAMOCLES is to achieve a comprehensive understanding of atmospheric nanocluster and ice crystal formation based on fundamental physico-chemical principles
Formation of aerosol particles and ice crystals in the atmosphere affects weather, climate and air quality. These formation processes are poorly understood and are one of the major uncertainties in predicting climate change.
Extracting accurate molecular level information from state of the art experiments is vital for sound model developments. Chemical ionization mass spectrometers can, unlike any other instruments, detect the elemental composition of many of the smallest clusters at low atmospheric concentrations. However, the charging process and energetic collisions inside the instrument change the composition of the clusters. Our objective is build an accurate model for the fate of clusters inside mass spectrometers, which will vastly improve the amount and quality of information that can be drawn from mass spectrometric measurements in atmospheric science and elsewhere.
We will use a wide palette of theoretical and computer simulation methods to build particle formation models with genuine predictive capacity. We also aim to produce reliable model for ice crystal formation. This will lead to improved predictions of aerosol concentrations and size distributions, leading to improved air quality forecasting, more accurate estimates of aerosol indirect climate forcing and other aerosol-cloud-climate interactions.
We have developed a statistical model for cluster fragmentation in the mass spectrometric instrument that is able to reproduce experimental extent of fragmentation without any semi-empirical fitting parameters.

We have discovered new classes of molecular species that efficiently form cluster with sulphuric acid, and also excluded the potential of an array of molecules to participate in the first stages of atmospheric particle formation.

We have designed an effective sampling scheme for finding the most stable configurations for atmospherically relevant clusters consisting of given molecules.

We have validated a modelling approach fr studying the effectiveness of different working fluids in detecting small cluster with different composition using condensation particle counters
The cluster fragmentation model, backed up by experimental work is a breakthrough in analyzing and designing mass spectrometric experiments.
The proof of concept molecular level modelling of the operation of condensation particle counters goes significantly beyond the state of the art.
First steps of applying artificial intelligence approaches in molecular level atmospheric particle formation studies pave the way for future research.