000197344 001__ 197344
000197344 005__ 20230208173547.0
000197344 0247_ $$2CORDIS$$aG:(EU-Grant)692891$$d692891
000197344 0247_ $$2CORDIS$$aG:(EU-Call)ERC-2015-AdG$$dERC-2015-AdG
000197344 0247_ $$2originalID$$acorda__h2020::692891
000197344 035__ $$aG:(EU-Grant)692891
000197344 150__ $$aSimulating Non-Equilibrium Dynamics of Atmospheric Multicomponent Clusters$$y2016-06-01 - 2021-05-31
000197344 371__ $$aHELSINGIN YLIOPISTO$$dFinland$$ehttp://www.helsinki.fi$$vCORDIS
000197344 372__ $$aERC-2015-AdG$$s2016-06-01$$t2021-05-31
000197344 450__ $$aDAMOCLES$$wd$$y2016-06-01 - 2021-05-31
000197344 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000197344 680__ $$aAtmospheric aerosol particles play a key role in regulating the climate, and particulate matter is responsible for most of the 7 million deaths per year attributed to air pollution. Lack of understanding of aerosol processes, especially the formation of ice crystals and secondary particles from condensable trace gases, hampers the development of air quality modelling, and remains one of the major uncertainties in predicting climate. 
The purpose of this project is to achieve a comprehensive understanding of atmospheric nanocluster and ice crystal formation based on fundamental physico-chemical principles. We will use a wide palette of theoretical methods including quantum chemistry, reaction kinetics, continuum solvent models, molecular dynamics, Monte Carlo simulations, Markov chain Monte Carlo methods, computational fluid dynamics, cluster kinetic and thermodynamic models. We will study non-equilibrium effects and kinetic barriers in atmospheric clustering, and use these to build cluster distribution models with genuine predictive capacity.
Chemical ionization mass spectrometers can, unlike any other instruments, detect the elemental composition of many of the smallest clusters at ambient low concentrations. However, the charging process and the environment inside the instrument change the composition of the clusters in hitherto unquantifiable ways. We will solve this problem by building an accurate model for the fate of clusters inside mass spectrometers, which will vastly improve the amount and quality of information that can be extracted from mass spectrometric measurements in atmospheric science and elsewhere.
DAMOCLES will produce reliable and consistent models for secondary aerosol and ice particle formation and growth. 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.
000197344 909CO $$ooai:juser.fz-juelich.de:821991$$pauthority$$pauthority:GRANT
000197344 909CO $$ooai:juser.fz-juelich.de:821991
000197344 970__ $$aoai:dnet:corda__h2020::7653bf5a4af1c89363ec38e2604ff232
000197344 980__ $$aG
000197344 980__ $$aCORDIS
000197344 980__ $$aAUTHORITY