Measured and modelled Cloud Condensation Nuclei (CCN) concentration in São Paulo, Brazil: the importance of aerosol size-resolved chemical composition on CCN concentration prediction

Abstract

Abstract. Measurements of cloud condensation nuclei (CCN), aerosol size distribution and non-refractory chemical composition were performed from 16 to 31 October 2012 in the São Paulo Metropolitan Area (SPMA), Brazil. CCN measurements were performed at 0.2%, 0.4%, 0.6%, 0.8% and 1.0% water supersaturation and were subsequently compared with Köhler theory, considering the chemical composition. Real-time chemical composition has been obtained deploying for the first time in SPMA an Aerosol Chemical Ionization Monitor (ACSM). CCN closure analyses were performed considering internal mixture. Average aerosol composition during the studied period yielded 4.81 ± 3.05, 3.26 ± 2.10, 0.30 ± 0.27, 0.52 ± 0.32, 0.37 ± 0.21 and 0.04 ± 0.04 μg m−3 for organics, BC, NH4, SO4, NO3 and Cl, respectively. Particle number concentration was 12 813 ± 5350 cm−3, being a large fraction in the nucleation mode. CCN concentrations were on average 1090 ± 328 cm−3 and 3570 ± 1695 cm−3 at SS = 0.2% and SS = 1.0%, respectively. Results show an increase in aerosol hygroscopicity in the afternoon as a result of aerosol photochemical processing, leading to an enhancement of both organic and inorganic secondary aerosols in the atmosphere, as well as an increase in aerosol average diameter. Considering the bulk composition alone, CCN concentrations were substantially overpredicted (29.6 ± 45.1% at 0.2% supersaturation and 57.3 ± 30.0% at 1.0% supersaturation). Overall, the impact of composition on the calculated NCCN decreases with decreasing supersaturation, partially because using bulk composition introduces less bias for large diameters and lower critical supersaturations. Results suggest that the consideration of only inorganic fraction improves the calculated NCCN. Introducing a size-dependent chemical composition based on filter measurements from previous campaigns has considerably improved simulated values for NCCN (average overprediction error 3.0 ± 33.4% at 0.20% supersaturation and average under prediction error 2.4 ± 20.5% at 1.0% supersaturation). This study provides the first insight on aerosol real-time composition and hygroscopicity on a~site strongly impacted by emissions of a unique vehicular fleet due to the extensive biofuel usage.