Forwood Wiser, Siddhartha Sen, Zhizhao Wang, Julia Lee-Taylor, Kelley C Barsanti, John Orlando, Daniel M Westervelt, Daven K Henze, Arlene M Fiore, Alexander Berman, Reese Carter, V Faye McNeill
{"title":"基于图论的大气化学机制还原算法。","authors":"Forwood Wiser, Siddhartha Sen, Zhizhao Wang, Julia Lee-Taylor, Kelley C Barsanti, John Orlando, Daniel M Westervelt, Daven K Henze, Arlene M Fiore, Alexander Berman, Reese Carter, V Faye McNeill","doi":"10.1093/pnasnexus/pgaf273","DOIUrl":null,"url":null,"abstract":"<p><p>The atmospheric chemistry of volatile organic compounds (VOC) has a major influence on atmospheric pollutants and particle formation. Accurate modeling of this chemistry is essential for air quality models. Complete representations of VOC oxidation chemistry are far too large for spatiotemporal simulations of the atmosphere, necessitating reduced mechanisms. We present Automated MOdel REduction version 2.0, an algorithm for the reduction of any VOC oxidation mechanism to a desired size by removing, merging, and rerouting sections of the graph representation of the mechanism. We demonstrate the algorithm on isoprene (398 species) and camphene (103,694 species) chemistry. We remove up to 95% of isoprene species while improving upon prior reduced isoprene mechanisms by 53-67% using a multispecies metric. We remove 99% camphene species while accurately matching camphene secondary organic aerosol production simulated using the full mechanism. This algorithm will bridge the gap between large and reduced mechanisms, helping to improve air quality models.</p>","PeriodicalId":74468,"journal":{"name":"PNAS nexus","volume":"4 9","pages":"pgaf273"},"PeriodicalIF":3.8000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12403060/pdf/","citationCount":"0","resultStr":"{\"title\":\"A graph theory-based algorithm for the reduction of atmospheric chemical mechanisms.\",\"authors\":\"Forwood Wiser, Siddhartha Sen, Zhizhao Wang, Julia Lee-Taylor, Kelley C Barsanti, John Orlando, Daniel M Westervelt, Daven K Henze, Arlene M Fiore, Alexander Berman, Reese Carter, V Faye McNeill\",\"doi\":\"10.1093/pnasnexus/pgaf273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The atmospheric chemistry of volatile organic compounds (VOC) has a major influence on atmospheric pollutants and particle formation. Accurate modeling of this chemistry is essential for air quality models. Complete representations of VOC oxidation chemistry are far too large for spatiotemporal simulations of the atmosphere, necessitating reduced mechanisms. We present Automated MOdel REduction version 2.0, an algorithm for the reduction of any VOC oxidation mechanism to a desired size by removing, merging, and rerouting sections of the graph representation of the mechanism. We demonstrate the algorithm on isoprene (398 species) and camphene (103,694 species) chemistry. We remove up to 95% of isoprene species while improving upon prior reduced isoprene mechanisms by 53-67% using a multispecies metric. We remove 99% camphene species while accurately matching camphene secondary organic aerosol production simulated using the full mechanism. This algorithm will bridge the gap between large and reduced mechanisms, helping to improve air quality models.</p>\",\"PeriodicalId\":74468,\"journal\":{\"name\":\"PNAS nexus\",\"volume\":\"4 9\",\"pages\":\"pgaf273\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12403060/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PNAS nexus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/pnasnexus/pgaf273\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PNAS nexus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/pnasnexus/pgaf273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A graph theory-based algorithm for the reduction of atmospheric chemical mechanisms.
The atmospheric chemistry of volatile organic compounds (VOC) has a major influence on atmospheric pollutants and particle formation. Accurate modeling of this chemistry is essential for air quality models. Complete representations of VOC oxidation chemistry are far too large for spatiotemporal simulations of the atmosphere, necessitating reduced mechanisms. We present Automated MOdel REduction version 2.0, an algorithm for the reduction of any VOC oxidation mechanism to a desired size by removing, merging, and rerouting sections of the graph representation of the mechanism. We demonstrate the algorithm on isoprene (398 species) and camphene (103,694 species) chemistry. We remove up to 95% of isoprene species while improving upon prior reduced isoprene mechanisms by 53-67% using a multispecies metric. We remove 99% camphene species while accurately matching camphene secondary organic aerosol production simulated using the full mechanism. This algorithm will bridge the gap between large and reduced mechanisms, helping to improve air quality models.