Organic Aerosol Hygroscopicity Model Linking Single Compounds to Ambient Air

A machine-learning framework quantitatively links the water-absorbing properties of individual organic compounds to ambient aerosol hygroscopicity. The 156.5 MB dataset, created by Shravan Deshmukh and last updated in April 2026, organizes species into functional family groups to establish a physically interpretable basis for atmospheric aerosol representation.

Use Cases

• Predict ambient aerosol hygroscopicity (κ) from single-compound measurements using the provided machine learning model.
• Analyze the characteristic hygroscopic properties of organic functional family groups defined within the framework.
• Validate the model's physically interpretable basis for representing the organic fraction of atmospheric aerosol.

Strengths

• 156.5 MB of structured data and model framework.
• Framework is anchored to laboratory measurements for quantitative linkage.
• Model establishes a physically interpretable basis for aerosol representation.

Limitations

• Specific row count, column names, and sample data are unavailable for assessment.
• The dataset's primary focus is on the model framework; raw observational data volume may be limited.
• Geographic and temporal coverage of the underlying measurements is not specified.

Provenance

Source

Shravan Deshmukh via figshare.

Collection Method

Laboratory-anchored, machine-learning framework developed to link measurements.

Freshness

Last updated in April 2026.