Implementation and Assessment of the Automated MOdel REduction (AMORE v2.0) Algorithm on α-Pinene
KATHERINE STEVENSON, Forwood Wiser, V. Faye McNeill, Columbia University
Abstract Number: 440
Working Group: Aerosol Chemistry
Abstract
Full oxidation mechanisms of volatile organic compounds are too computationally costly to be included in large-scale chemical transport and air quality models. The graph theory-based Automated MOdel REduction (AMORE) version 2.0 algorithm simplifies chemical mechanisms, while retaining yield accuracies, to enable their use in 3D models. AMORE employs several graph theory techniques to estimate yields, sort species, group similar species, and ultimately reduce the size and complexity of oxidation mechanisms. Yields of semi-volatile oxidation products that are significant for secondary organic aerosol (SOA) formation are prioritized. Thus far, the reductions of an experimentally-derived isoprene and GECKO-derived camphene oxidation mechanisms have been conducted with AMORE v2.0 and extensively analyzed (Wiser et al., 2025). To further improve algorithm generalizability and automation, AMORE has been implemented for the reduction of an α-pinene oxidation mechanism. To obtain a comprehensive α-pinene oxidation mechanism, Master Chemical Mechanism v3.3.1’s α-pinene oxidation mechanism (Jenkin et al., 1997; Saunders et al., 2003) was supplemented with the Peroxy Radical Autoxidation Mechanism (Roldin et al., 2019). This ensured that the formation of highly oxygenated molecules from α-pinene, which are crucial for accurately estimating SOA yields, were incorporated. The α-pinene oxidative mechanism provides a valuable opportunity to assess the accuracy and versatility of AMORE v2.0 due to its increased complexity and presence of many peroxyacyl nitrate strongly connected components (SCCs), or cycles. The performance of the AMORE-derived reduced α-pinene oxidation mechanism and modifications to the AMORE v2.0 algorithm, which prevent undesirable species groupings and SCC removals, are evaluated.