Developing a Predictive Understanding of Soot Formation in Wildfires

对野火中烟灰形成的预测性了解

• 批准号: 2328647
• 负责人: Hope Michelsen
• 金额: $ 38.37万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328647&HistoricalAwards=false
• 关键词: Developing; Predictive; Understanding; Soot; Formation

Soot particles emitted from wildfires have enormous detrimental effects on human health, agriculture, air and water quality, and global and regional climate. The combustion processes involved in soot-particle production strongly influence their composition and reactivity, toxicity, impact on agricultural productivity, crop value, and water quality, processing in the atmosphere, ability to nucleate clouds, atmospheric lifetime and transport, and optical and radiative properties. In addition, large radiative heat transfer from soot particles increases the difficulty of controlling and extinguishing medium- to large-scale wildfires. Increasing droughts from climate change and expansion of the wildland-urban interface further increase the frequency of large and uncontrollable wildfires responsible for heavy atmospheric-soot loading and their impact. Reducing wildfire damage requires effective methods to predict and control fire spread. Radiation from soot is a critical component of wildfire-propagation models, but current models do not accurately model soot-formation chemistry, largely because of a severe lack of understanding of soot-production mechanisms. The goal of this project is to address gaps in the understanding of soot-formation chemistry relevant to wildfires and gain enough knowledge of soot-formation mechanisms via targeted experiments and modeling to develop a realistic sub-model for incorporation into wildfire-propagation models. An advanced fundamental understanding of soot formation could also benefit predictions of soot formation under a wide range of conditions and for applications such as engines, furnaces, boilers, and explosives. This project will also provide training for the next generation of scientists and engineers who will tackle the challenges of climate change and the increasing frequency of large-scale fires at the wildland-urban interface.The objectives of this project are to (1) identify the most likely precursors to soot inception, species that lead to particle surface growth, and mechanisms for particle inception and growth during wildfires and (2) develop a predictive model for soot inception during pyrolysis and combustion of biogenic organic compounds and biomass. Vacuum-ultraviolet photoionization aerosol mass spectrometry will be used to probe the precursors and composition of particles generated during the pyrolysis and combustion of biogenic organic compounds. Particle-size distributions associated with these experiments will be measured using a scanning mobility particle sizer and particle volume fraction, and optical properties will be measured using laser-induced incandescence. The results of these experiments, coupled with theoretical investigations, will be used to develop a chemical kinetic model for soot inception and growth. The most significant expected outcome is the improvement soot-formation and radiative-heat-transfer sub-models in wildfire propagation and emissions predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

野火排放的烟尘颗粒对人类健康、农业、空气和水质以及全球和区域气候产生巨大的有害影响。烟灰颗粒产生中涉及的燃烧过程强烈影响其成分和反应性、毒性、对农业生产力、作物价值和水质的影响、大气中的处理、云成核的能力、大气寿命和传输以及光学和辐射特性。此外，烟尘颗粒的大量辐射传热增加了控制和扑灭中大规模野火的难度。气候变化导致的干旱加剧以及荒地与城市界面的扩大进一步增加了大规模且无法控制的野火的发生频率，造成了严重的大气烟尘负荷及其影响。减少野火损害需要有效的方法来预测和控制火势蔓延。烟灰辐射是野火传播模型的关键组成部分，但目前的模型无法准确模拟烟灰形成化学，这很大程度上是因为严重缺乏对烟灰产生机制的了解。该项目的目标是解决对与野火相关的烟灰形成化学的理解方面的差距，并通过有针对性的实验和建模获得足够的烟灰形成机制知识，以开发一个现实的子模型，以纳入野火传播模型。对烟灰形成的先进基本了解还有助于预测各种条件下的烟灰形成以及发动机、熔炉、锅炉和炸药等应用。该项目还将为下一代科学家和工程师提供培训，以应对气候变化的挑战以及荒地与城市交界处日益频繁的大规模火灾。该项目的目标是（1）确定最有可能的烟灰起始前体、导致颗粒表面生长的物种以及野火期间颗粒起始和生长的机制；(2) 开发生物有机化合物和生物质热解和燃烧过程中烟灰起始的预测模型。真空紫外光电离气溶胶质谱法将用于探测生物有机化合物热解和燃烧过程中产生的颗粒的前体和组成。与这些实验相关的粒度分布将使用扫描迁移率粒度仪和颗粒体积分数进行测量，并且光学特性将使用激光诱导白炽光进行测量。这些实验的结果与理论研究相结合，将用于开发烟灰产生和生长的化学动力学模型。最重要的预期成果是改进野火传播和排放预测中的烟灰形成和辐射传热子模型。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。