Organic aerosols (OA) are an important component of the atmosphere with regards to resolving the impact aerosols have on both climate and air quality. To predict how OA will behave in the atmosphere requires knowledge of their physicochemical properties. A key property for predicting what fraction of a compound will partition into the aerosol phase and what fraction will partition into the gas phase is the saturation vapour pressure (Psat) of the compound. It has been estimated that the number of organic compounds in the atmosphere is in excess of 100,000; therefore it is not feasible to measure the Psat of each compound experimentally. Instead group contribution methods (GCMs) are used to predict Psat. Many GCMs were originally designed for use in chemical engineering and were developed for use with monofunctional compounds and hydrocarbons. This means that they often lack parameters to account for various steric effects and intramolecular interactions that can occur in multifunctional compounds and the impact these interactions have on Psat. As the vast majority of OA consist of multifunctional compounds this leads to GCMs performing poorly when predicting Psat for OA. As well as not properly accounting for intramolecular interactions between functional groups, some functional groups are underrepresented in the data sets that are used to fit GCMs or can be missing entirely. If a functionality is poorly represented this can lead to a GCM overfitting to a limited amount of data, and if a functionality is not represented at all, the effects of that functionality can be misrepresented or ignored entirely. In order to more accurately predict P sat of OA more experimental data is needed, especially for multifunctional compounds that contain functionalities that are poorly represented in GCM fitting data sets. In this project experimental Psat are measured for a range of nitroaromatic compounds and a range of benzaldehydes using Knudsen Effusion Mass Spectrometry (KEMS). These measured values are then compared to each other and chemical explanations are given for the observed trends. The experimental Psat are then compared to predicted Psat using GCMs and potential causes for the observed differences are discussed. Following this multivariate regression techniques are used to calculate feature importance for several GCMs to determine which functionalities give rise to the largest sources of error when predicting Psat.
|Date of Award||31 Dec 2021|
- The University of Manchester
|Supervisor||Rami Alfarra (Supervisor) & David Topping (Supervisor)|
- Atmospheric science
- Organic aerosol