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Knowledge of hygroscopic properties is essential to prediction of the role of aerosol in cloud formation and lung deposition. Our objective was to introduce a new approach to classify and predict the hygroscopic growth factors (Gfs) of specific atmospheric sub-micrometre particle types in a mixed aerosol based on measurements of the ensemble hygroscopic growth factors and particle number size distribution (PNSD). Based on a non-linear regression model between aerosol source contributions from PMF applied to the PNSD data set and the measured Gf values (at 90% relative humidity) of ambient aerosols, the estimated mean Gf values for secondary inorganic, mixed secondary, nucleation, urban background, fresh, and aged traffic-generated particle classes at a diameter of 110 nm were found to be 1.51, 1.34, 1.12, 1.33, 1.09 and 1.10, respectively. It is found possible to impute (fill) missing HTDMA data sets using a Random Forest regression on PNSD and meteorological conditions.
Bibliographical noteFunding Information:
This research was supported by the UK Natural Environment Research Council funding though the AIRPOLL-Beijing project within the APHH programme (NE/N007190/1). We would like to thank the University of Manchester who collected the initial data set as part of the ClearfLo project (NERC funded under Grant NE/H00324X/1) for data sets archived on CEDA.
ASJC Scopus subject areas
- Global and Planetary Change
- Environmental Chemistry
- Atmospheric Science