Model-Measurement Comparisons for Surfactant-Containing Aerosol Droplets.

Area of Science:

• Atmospheric chemistry
• Physical chemistry
• Aerosol science

Background:

• Surfactants in atmospheric aerosols influence cloud droplet formation by altering surface tension.
• High surface-area-to-volume ratios in small aerosol droplets can deplete bulk surfactant concentration, increasing surface tension.
• Existing partitioning models for aerosol surface tension require experimental validation.

Purpose of the Study:

• To directly compare predictions from kinetic and thermodynamic partitioning models against experimentally measured picoliter aerosol droplet surface tensions.
• To assess model performance across a wide range of droplet sizes (8 orders of magnitude).
• To evaluate the impact of macroscopic data quality on model accuracy for droplet surface tension.

Main Methods:

• Experimental measurement of picoliter droplet surface tensions for 12 surfactant-cosolute systems.
• Comparison of experimental data with predictions from a simple kinetic partitioning model and a thermodynamic monolayer partitioning model.
• Model prediction evaluation across 8 orders of magnitude in droplet radius.

Main Results:

• The quality of macroscopic surface tension data significantly impacts model accuracy for droplet surface tension.
• Largest discrepancies between models occurred at the predicted onset of bulk surfactant depletion.
• Both models demonstrated ability to predict aerosol surface tension across size and composition ranges.

Conclusions:

• Validation of partitioning models against experimental droplet surface tension measurements is essential, especially where bulk depletion is expected.
• High-quality macroscopic surface tension data are crucial for accurate model inputs.
• Validated models can improve cloud parcel models by better representing aerosol surface tension effects on cloud droplet formation.