Vapor Pressure Estimation in Atmospheric Organic Compounds

Vapour pressure is a fundamental physicochemical property dictating the tendency of organic molecules to partition between the gas and condensed phases in the atmosphere. Accurate estimation of saturation vapour pressures underpins models of gas–particle partitioning, influences predictions of secondary organic aerosol formation and governs removal processes such as dry and wet deposition. Traditional approaches have relied upon group contribution schemes that assign empirical increments to molecular fragments, yet these often struggle with multifunctional, highly oxidised species. Quantum-chemistry methods integrate molecular conformation and intra-molecular interactions to predict vapour pressures but can be computationally intensive for large data sets. More recently, data-driven techniques combining comprehensive conformer sampling with density functional theory and machine learning regression have emerged, delivering high-throughput estimates across diverse chemical spaces. Enhanced volatility prediction not only refines our understanding of aerosol dynamics and climate forcing but also supports regulatory frameworks for air quality management and industrial applications that depend on thermodynamic property databases.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Vapor Pressure Estimation in Atmospheric Organic Compounds publication trend

The graph below shows the total number of articles in vapor pressure estimation in atmospheric organic compounds across all publications each year (not limited to Nature Index journals).

Technical terms

Saturation vapour pressure (pSat): The equilibrium pressure exerted by a substance’s vapour at a given temperature when in contact with its liquid phase.

Group contribution method: An empirical estimation technique that sums predefined fragment contributions to predict thermodynamic properties from molecular structure.

Density functional theory (DFT): A quantum-chemical approach to compute electronic structure and derive properties such as free energies and vapour pressures.

Machine learning regression: A class of data-driven algorithms that model relationships between molecular descriptors and target properties for predictive purposes.

Secondary organic aerosol (SOA): Particulate matter formed through atmospheric oxidation of volatile organic compounds, affecting air quality and climate.

References

1. Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules. Scientific Data (2023).
2. Data-driven, explainable machine learning model for predicting volatile organic compounds’ standard vaporization enthalpy. Chemosphere (2024).
3. Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning. Atmospheric Chemistry and Physics (2021).