Atmospheric chemistry and aerosols significantly impact air quality and climate forcing by seeding clouds. The formation of airborne aerosols depends on organic oxidation products of sufficiently low volatility, which partition into the particle phase to form particle mass. A molecular-level mechanistic description of this process seems to be out of reach for the time being. After the initiation of oxidation, the chemistry quickly becomes complex, resulting in a wide variety of progressively oxygenated reaction intermediates and products that are impossible to track selectively through any current experimental methodologies. Similarly, the huge number of potential reaction paths renders high-level computational predictions too expensive to derive. At AMC-Lahti, we propose, in cooperation with national and international colleagues, an alternative methodology in which highly sensitive chemical ionisation mass spectrometry (CIMS) detection is utilised for the semi-empirical autoxidation model generation.

During the last few years, we have developed novel autoxidation chemistry schemes for biogenic (Roldin et al., 2019) and anthropogenic (Pichelstorfer et al., 2024) volatile organic compounds. These new codes enable the prediction of organics with extremely low volatility, which can condense on very small clusters in the nm-size range. As a result, we could simulate the growth of newly formed particles in rural and urban areas with high confidence compared to observations. Figure 1 shows computer simulations of flow tube setup for OH oxidation of benzene in the presence of UV light, and Figure 2 presents modelled and measured particle size distributions.