The gas-particle partitioning of semi-volatile organic compounds on urban nanoparticles

Jia, Weiqing (2022). The gas-particle partitioning of semi-volatile organic compounds on urban nanoparticles. University of Birmingham. Ph.D.

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Abstract

A considerable fraction of organic compounds from the vehicle exhaust are semi-volatile. The gas-particle partitioning of these semi-volatile organic compounds (SVOCs) on urban nanoparticles is crucial for their properties, lifetime, and health effects. This work utilizes multiple modelling and theoretical methods for investigating the gas-particle partitioning of SVOCs on urban vehicle-emitted nanoparticles: An aerosol box model (Chapter 3); A conceptual monolayer sorption model of soot nanoparticles (Chapter 4); Several theoretical thermodynamic equilibrium partitioning approaches (Chapter 5); A kinetic model of sorption on soot nanoparticles (Chapter 6).
The volatility spectrum of urban SVOCs is represented by n-alkanes, the greater part of urban SVOCs near source, as surrogates of the complex mixture. The effects of the saturated vapour pressure on gas-particle partitioning of n-alkane are tested using the aerosol box model. The capacity of particle phase n-alkane on the fractal-like vehicle-emitted soot nanoparticles is evaluated by the monolayer sorption model. The equilibrium partitioning behaviour and gasparticle partition coefficients for n-alkanes are investigated by applying the equilibrium partitioning approaches. The microphysics of mass transfer between gas phase and particle phase, and the equilibration timescale are examined by employing the kinetic model. The results from the models are challenged with the measurement data at an urban background site, Regent’s University, and an urban traffic site, Marylebone Road in London during the FASTER project. By synthesising the model results, explanations for the anomalously high particle phase concentration for high-volatile n-alkane C16-C19 from measurement are addressed.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):