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

暴露于恶劣的空气质量是全球过早死亡的首要环境风险因素。到目前为止，对健康危害最大的空气污染物是颗粒物，直径小于2.5微米的颗粒物（PM2.5）的影响最大。在拥有大量居民/排放源的特大城市，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，因为目前的证据表明低于这一数值的健康风险较低。然而，超过90%的世界人口生活在超过这一数值的地区，伦敦、北京和德里2016年的数值分别高出约1. 5倍、8倍和15倍。二次有机气溶胶（SOA）可以构成城市地区PM2.5的重要组成部分，随着许多国家采取行动减少氨和氮氧化物的排放，这可能会增加。目前的分析方法无法提供足够的化学形态，以定期分配SOA的贡献源，限制了机会，制定更有针对性的PM减排策略。高复杂性的方法已经彻底改变了生物医学，但在环境界的吸收速度较慢。在这个项目中，我们将拥抱大气的复杂性，使我们的SOA在城市大气中的来源和转换的理解发生了一步变化。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。我们将开发新的工具，使高通量筛选和定量的SOA示踪剂在大气气溶胶样品。我们将开发一个SOA示踪物质的质谱数据库，该数据库使用约克大学设计的新型气溶胶流反应器，并补充了来自6个世界领先的模拟室的样品。这里的关键是识别独特的源特定示踪剂分子，其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的直接联系。MS使用电喷雾电离，这是世界各地分析实验室中最常用的方法之一。这种方法非常适合于许多SOA示踪剂分子，然而电离效率强烈依赖于化学结构。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仪器响应作为分子“指纹”的函数。我们将联合收割机这些工具进行最全面的量化SOA示踪剂在环境气溶胶和使用机器学习方法来确定影响SOA浓度的因素，并估计SOA的生物源和人为源的相对强度PM2.5。我们的项目将提供这种方法的第一个演示;在以前的研究中缺乏足够的化学形态和低时间分辨率到目前为止限制了我们提出的分析。该项目的时间安排使我们能够利用NERC空气污染和人类健康计划的最新投资，提供PM2.5样本档案和来自英国，中国和印度的领导小组收集的大量空气质量数据。为了传达我们的研究结果，我们将制作城市特定的政策报告，突出每个城市的主要结论，供政府和监管机构使用。这将通过在伦敦环境、食品和农村事务部的空气质量政策小组进行为期两个月的知识转移安置来帮助。本项目将以伦敦、北京和德里为测试案例，提供控制城市SOA数量的关键因素的证据。然而，该方法可以应用于地球仪的城市，以制定以减少SOA为目标的减排政策。

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.