Predicting hygroscopic growth using single particle chemical composition estimates

Single particle mass spectral data, collected in Paris, France, have been used to predict hygroscopic growth at the single particle level. The mass fractions of black carbon, organic aerosol, ammonium, nitrate, and sulphate present in each particle were estimated using a combination of single particle mass spectrometer and bulk aerosol chemical composition measurements. The Zdanovskii-Stokes-Robinson (ZSR) approach was then applied to predict hygroscopic growth factors based on these mass fraction estimates. Smaller particles with high black carbon mass fractions and low inorganic ion mass fractions exhibited the lowest predicted growth factors, while larger particles with high inorganic ion mass fractions exhibited the highest growth factors. Growth factors were calculated for subsaturated relative humidity (90%) to enable comparison with hygroscopic tandem differential mobility analyzer measurements. Mean predicted and measured hygroscopic growth factors for 110, 165, and 265 nm particles were found to agree within 6%. Single particle-based ZSR hygroscopicity estimates offer an advantage over bulk aerosol composition-based hygroscopicity estimates by providing additional chemical mixing state information. External mixing can be determined for particles of a given diameter through examination of the predicted hygroscopic growth factor distributions. Using this approach, 110 nm and 265 nm particles were found to be predominantly internally mixed; however, external mixing of 165 nm particles was observed periodically when thinly coated and thickly coated black carbon particles were simultaneously detected. Single particle-resolved chemical information will be useful for modeling efforts aimed at constraining cloud condensation nuclei activity and hygroscopic growth.