A graph theory-based algorithm for the reduction of atmospheric chemical mechanisms.

IF 3.8 Q2 MULTIDISCIPLINARY SCIENCES
PNAS nexus Pub Date : 2025-08-21 eCollection Date: 2025-09-01 DOI:10.1093/pnasnexus/pgaf273
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
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引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

基于图论的大气化学机制还原算法。
挥发性有机化合物(VOC)的大气化学对大气污染物和颗粒物的形成有重要影响。这种化学反应的精确建模对空气质量模型至关重要。挥发性有机化合物氧化化学的完整表示对于大气的时空模拟来说太大了,因此需要简化机制。我们提出了自动化模型缩减2.0版,这是一种通过删除、合并和重新路由机制的图表示部分,将任何VOC氧化机制缩减到所需大小的算法。我们对异戊二烯(398种)和莰烯(103,694种)的化学性质进行了验证。我们去除了高达95%的异戊二烯物种,同时使用多物种指标将先前减少异戊二烯的机制提高了53-67%。我们去除99%的莰烯物种,同时准确匹配使用全机制模拟的莰烯二次有机气溶胶生成。该算法将弥合大型和小型机制之间的差距,有助于改善空气质量模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.80
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