Using the complexity of secondary organic aerosols to understand their formation, ageing and transformation in three contrasting megacities

利用二次有机气溶胶的复杂性来了解它们在三个对比鲜明的大城市中的形成、老化和转变

• 批准号: NE/S010467/1
• 负责人: Jacqueline Hamilton
• 金额: $ 65.62万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS010467%2F1
• 关键词: Using; complexity; secondary; organic; aerosols

Exposure to poor air quality is the top environmental risk factor of premature mortality globally. By far the most damaging air pollutant to health is particulate matter, with the greatest effects associated with particles less than 2.5 microns in diameter (PM2.5). In megacities, with large numbers of inhabitants/emissions sources, PM2.5 can often exceed recommended guideline values. The World Health Organization recommend an annual mean concentration of less than 10 micrograms/m3, as current evidence suggests lower health risks below this value. However, over 90 % of the worlds population live in regions where this value is exceeded, with London, Beijing and Delhi having values in 2016 ~ 1.5, 8 and 15 times higher. Secondary organic aerosol (SOA) can make up a significant fraction of PM2.5 in urban areas, and this may increase as many counties act to reduce emissions of ammonia and NOx. Current analytical approaches fail to provide sufficient chemical speciation to routinely apportion the contributing sources of SOA, limiting the opportunities to develop more targeted PM abatement strategies. High complexity approaches have revolutionised biomedicine, however uptake within the environmental community has been slower. In this project, we will embrace the atmosphere's complexity to make a step change in our understanding of the sources and transformation of SOA in urban atmospheres. This will be achieved through the combination of two state of the art research areas; high resolution mass spectrometry (MS) and machine learning. We will develop new tools to allow high throughput screening and quantification of SOA tracers in atmospheric aerosol samples. We will develop a mass spectral database of SOA tracer species built using a novel aerosol flow reactor designed at the University of York and supplemented with samples from 6 world-leading simulation chambers. The key here is to identify unique source specific tracer molecules that allow a direct link between the gas phase organic molecule that is emitted to the atmosphere and it's specific oxidation products that can be measured in ambient particles. The MS uses electrospray ionization, one of the most common approaches used in analytical labs throughout the world. This method is ideally suited to many SOA tracer molecules, however the ionization efficiency is strongly dependent on the chemical structure. We will carry out a systematic evaluation of the ionization efficiencies of a wide range of molecules with different functionalities to build a regression model to predict instrument response as a function of a molecular "fingerprint". We will combine these tools to carry out the most comprehensive quantification of SOA tracers in ambient aerosol and use machine learning methods to determine the factors that impact SOA concentration and estimate the relative strength of biogenic and anthropogenic sources of SOA to PM2.5. Our project will provide the first demonstration of such methods; the lack of sufficient chemical speciation and low time resolution in previous studies has so far restricted our proposed analysis. The timing of this project allows us to exploit recent investment in the NERC Air Pollution and Human Health program, providing access to an archive of PM2.5 samples and a wealth of co-located air quality data collected by leading groups from the UK, China and India. To communicate our results we will produce city specific policy reports, highlighting the main conclusions for each city, for use by government and regulatory agencies. This will be aided by a two month knowledge transfer placement in the Air Quality policy group at the Department for Environment, Food and Rural Affairs in London. This project will provide evidence of the key factors that control the amount of SOA in cities, using London, Beijing and Dehli as test cases. However, the methodology could be applied in cities across the globe to develop abatement policies that would target SOA reduction.

暴露于不良空气质量是全球过早死亡的首要环境风险因素。迄今为止，对健康危害最大的空气污染物是颗粒物，其中直径小于 2.5 微米的颗粒 (PM2.5) 影响最大。在拥有大量居民/排放源的特大城市中，PM2.5 通常会超过建议的指导值。世界卫生组织建议年平均浓度低于 10 微克/立方米，因为当前证据表明低于该值的健康风险较低。然而，世界上90%以上的人口居住在超过这一数值的地区，其中伦敦、北京和德里的数值在2016年分别高出约1.5、8和15倍。二次有机气溶胶 (SOA) 占城市地区 PM2.5 的很大一部分，而且随着许多县采取行动减少氨和氮氧化物的排放，这一比例可能会增加。目前的分析方法无法提供足够的化学形态来常规分配 SOA 的贡献来源，从而限制了制定更有针对性的 PM 减排策略的机会。高复杂性方法已经彻底改变了生物医学，但环境界的采用速度较慢。在这个项目中，我们将拥抱大气的复杂性，以逐步改变我们对城市大气中 SOA 的来源和转变的理解。这将通过两个最先进的研究领域的结合来实现；高分辨率质谱 (MS) 和机器学习。我们将开发新工具，以实现大气气溶胶样品中 SOA 示踪剂的高通量筛选和定量。我们将开发一个 SOA 示踪剂物种的质谱数据库，该数据库使用约克大学设计的新型气溶胶流反应器构建，并补充来自 6 个世界领先的模拟室的样本。这里的关键是识别独特的源特定示踪分子，这些示踪分子允许排放到大气中的气相有机分子与其可以在环境颗粒中测量的特定氧化产物之间直接联系。 MS 使用电喷雾电离，这是世界各地分析实验室最常用的方法之一。该方法非常适合许多 SOA 示踪分子，但电离效率很大程度上取决于化学结构。我们将对具有不同功能的各种分子的电离效率进行系统评估，以建立回归模型来预测作为分子“指纹”函数的仪器响应。我们将结合这些工具对环境气溶胶中的 SOA 示踪剂进行最全面的量化，并使用机器学习方法来确定影响 SOA 浓度的因素，并估计 SOA 的生物来源和人为来源对 PM2.5 的相对强度。我们的项目将首次演示此类方法；迄今为止，先前研究中缺乏足够的化学形态和低时间分辨率限制了我们提出的分析。该项目的时机使我们能够利用最近对 NERC 空气污染和人类健康项目的投资，提供 PM2.5 样本档案以及由英国、中国领先团体收集的大量同地空气质量数据和印度。为了传达我们的结果，我们将制作针对特定城市的政策报告，突出显示每个城市的主要结论，供政府和监管机构使用。这将得益于伦敦环境、食品和农村事务部空气质量政策小组为期两个月的知识转移安置。该项目将使用伦敦、北京和德里作为测试案例，提供控制城市 SOA 数量的关键因素的证据。然而，该方法可以应用于全球各地的城市，以制定以减少 SOA 为目标的减排政策。

项目成果

Secondary organic aerosols from anthropogenic volatile organic compounds contribute substantially to air pollution mortality

• DOI: 10.5194/acp-21-11201-2021
• 发表时间: 2021
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: B. Nault;D. Jo;B. McDonald;P. Campuzano‐Jost;D. Day;Weiwei Hu;J. Schroder;J. Allan;D. Blake;M. Canagaratna;H. Coe;M. Coggon;P. DeCarlo;G. Diskin;R. Dunmore;F. Flocke;A. Fried;J. Gilman;G. Gkatzelis;J. Hamilton;T. Hanisco;P. L. Hayes;D. Henze;A. Hodzic;J. Hopkins;Min Hu;L. G. Huey;B. Jobson;W. Kuster;A. Lewis;Meng Li;J. Liao;M. Nawaz;I. Pollack;J. Peischl;B. Rappenglück;C. Reeves;D. Richter;J. Roberts;T. Ryerson;M. Shao;J. Sommers;J. Walega;C. Warneke;P. Weibring;G. Wolfe;D. Young;Bin Yuan;Qiang Zhang;J. D. de Gouw;J. Jimenez

Combined application of Online FIGAERO-CIMS and Offline LC-Orbitrap MS to Characterize the Chemical Composition of SOA in Smog Chamber Studies

在线 FigAERO-CIMS 和离线 LC-Orbitrap MS 的联合应用在烟雾室研究中表征 SOA 的化学成分

• DOI: 10.5194/amt-2021-420
• 发表时间: 2021
• 作者: Du M

Importance of Oxidants and Temperature in the Formation of Biogenic Organosulfates and Nitrooxy Organosulfates

• DOI: 10.1021/acsearthspacechem.1c00204
• 发表时间: 2021-09-06
• 期刊: ACS EARTH AND SPACE CHEMISTRY
• 影响因子: 3.4
• 作者: Bryant, Daniel J.;Elzein, Atallah;Hamilton, Jacqueline F.
• 通讯作者: Hamilton, Jacqueline F.

Evaluation of isoprene nitrate chemistry in detailed chemical mechanisms

• DOI: 10.5194/acp-22-14783-2022
• 发表时间: 2022-11
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Alfred W. Mayhew;B. H. Lee;J. Thornton;T. Bannan;J. Brean;J. Hopkins;James D. Lee;Beth S. Nelson;C. Percival;A. Rickard;M. Shaw;P. Edwards;J. F. Hamilton

Combined application of online FIGAERO-CIMS and offline LC-Orbitrap mass spectrometry (MS) to characterize the chemical composition of secondary organic aerosol (SOA) in smog chamber studies

联合应用在线 FigAERO-CIMS 和离线 LC-Orbitrap 质谱 (MS) 来表征烟雾室研究中二次有机气溶胶 (SOA) 的化学成分

• DOI: 10.5194/amt-15-4385-2022
• 发表时间: 2022
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: Du M