Airborne Measurements of Ozone Precursor Fluxes - Proof of Concept

臭氧前体通量的机载测量 - 概念验证

• 批准号: NE/J00779X/1
• 负责人: Charles Hewitt
• 金额: $ 33.78万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ00779X%2F1
• 关键词: Airborne; Measurements; Ozone; Precursor; Fluxes

The vast majority of air pollutants are emitted directly into the atmosphere from activities occurring at the Earth's surface. These activities may be anthropogenic in origin or may be natural (biogenic) processes. Of particular relevance to air quality are the emissions of oxides of nitrogen (NOx) and volatile organic compounds (VOCs). NOx or the sum of nitric oxide, NO and nitrogen dioxide, NO2, is emitted by vehicles, power stations and many other industrial activities during the combustion of fossil fuels. Volatile organic compounds (VOCs) may be emitted into the atmosphere as unburnt or partially burnt fossil fuels, by the evaporation of solvents and other industrial chemicals and may also be emitted by plants as a biogenic process. In combination NOx and VOC combine through photochemical reactions in the atmosphere leading to the formation of two other extremely important pollutants - ozone and particulate matter. Species such as NOx, certain VOCs, Ozone and particulate matter are all regulated by EU Air Quality directives. Whilst the chemical reactions and atmospheric processing of NOx and VOcs is reasonably well understood, and can be modelled with some skill, large uncertainties arise in models from uncertainty associated with the initial rate of emissions. Defra is responsible for a highly detailed emissions inventory for the UK (the National Atmospheric Emissions Inventory NAEI), which is the starting point for most atmospheric simulations of air quality. The NAEI is constructed to give spatially resolved emissions, calculated from national activity datasets and emissions factors which are then spatially disaggregated to 1x1km grids. The framework under which the NAEI operates is itself tightly constrained by regulated procedures for reporting. In recent years it has become clear that measured trends in certain pollutants, for example NO2, have not followed trends predicted by inventories. In parallel, other studies have shown that species such as biogenic isoprene are also not currently well reproduced by the NAEI. Continued exceedences of certain air pollution targets is of significant concern to the responsible Government department Defra, who have identified reducing this uncertainty associated with emissions as a key evidence need. Emissions inventories are essentially paper-based calculations of likely emissions, and it is not straightforward to challenge these with real-world emissions measurements, on large spatial scales. In this project we will apply and demonstrate a novel "top down" experimental method for measuring pollution fluxes at the city-wide or landscape scale. We will fit fast-response sensors for NOx and VOCs on NERC's light research aircraft, a Dornier 228, and fly the instruments over Greater London and over rural East Anglia as low and as slow as possible. This aircraft is already fitted with an instrument to measure atmospheric turbulence, and by combining the data from the turbulence probe with that from the chemical sensors (using data analysis techniques known as eddy covariance and virtual disjunct eddy covariance) we aim to demonstrate that we can make top down flux measurements for NOx and VOCs at these scales. We will then compare the experimentally derived data with that produced by the National Atmospheric Emissions Inventory, as a starting point for understanding where and why difference occur.The technique, if successful, would be of considerable strategic importance to Defra, who are co-funding this project. There are however very important applications outside of the urban domain. Understanding emission fluxes of gases in remote and pristine environments is a key element of understanding the earth system. The techniques to be developed here have potential transfers in to fields such as forest and marine biogeochemical gas exchange.

绝大多数空气污染物直接从地球表面发生的活动直接发射到大气中。这些活动的起源可能是人为的，也可能是自然的（生物）过程。与空气质量特别相关的是氮（NOX）和挥发性有机化合物（VOC）的氧化物的排放。 NOX或一氧化氮的总和NO和二氧化氮NO2，在化石燃料燃烧过程中由车辆，电站和许多其他工业活动发射。通过蒸发溶剂和其他工业化学物质，可以将挥发性有机化合物（VOC）散发到大气中，例如未燃烧或部分燃烧的化石燃料，并且也可以被植物作为生物生物过程发射。结合NOx和VOC通过在大气中的光化学反应结合在一起，导致另外两种极为重要的污染物形成 - 臭氧和颗粒物。 NOX，某些VOC，臭氧和颗粒物等物种都受欧盟空气质量指令的调节。尽管对NOX和VOC的化学反应和大气加工的理解众所周知，并且可以以某种技能进行建模，但由于与初始排放率相关的不确定性，模型中出现了大型不确定性。 Defra负责英国（国家大气排放库存NAEI）的高度详细的排放库存，这是大多数大气模拟空气质量的起点。 NAEI的构建是为了给出空间分辨的排放，该排放是根据国家活动数据集和排放因子计算得出的，然后将其在空间上分类至1x1km网格。 NAEI运行的框架本身受到报告的报告程序的严格限制。近年来，很明显，某些污染物的趋势（例如NO2）尚未遵循库存预测的趋势。同时，其他研究表明，诸如生物异丙烯之类的物种目前也无法很好地再现NAEI。持续超越某些空气污染目标是负责政府部门Defra的重大关注，他们确定将与排放相关的不确定性降低为关键证据。排放库存本质上是对可能排放的基于纸张的计算，在大空间尺度上，通过现实世界排放测量值挑战这些排放并不是一件直接的。在这个项目中，我们将应用并展示一种新颖的“自上而下”实验方法，用于测量全市或景观量表的污染通量。我们将在NERC的轻型研究飞机（Dornier 228）上适合NOX的快速响应传感器，并将乐器飞越大伦敦和乡村的东安格利亚（East Anglia）上，尽可能慢。该飞机已经配备了一种测量大气湍流的仪器，并将来自湍流探针的数据与化学传感器（使用称为涡流协方差的数据分析技术和虚拟的涡流协方差）结合在一起，我们旨在证明我们可以在这些尺度上对NOX和VOCS进行倒入倒数的测量。然后，我们将将实验得出的数据与国家大气排放量库存产生的数据进行比较，这是理解差异在哪里和为什么发生的起点。但是，在城市领域之外有非常重要的应用。了解偏远和原始环境中气体的排放通量是理解地球系统的关键要素。这里要开发的技术具有潜在的转移到森林和海洋生物地球化学气体交换等领域。

项目成果

Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories.

来自伦敦的氮氧化物的空间分辨通量测量表明排放量明显高于清单预测的排放量。

• DOI: 10.1039/c5fd00170f
• 发表时间: 2016
• 期刊: Faraday discussions
• 影响因子: 3.4
• 作者: Vaughan AR

VOC emission rates over London and South East England obtained by airborne eddy covariance.

• DOI: 10.1039/c7fd00002b
• 发表时间: 2017-08
• 期刊: Faraday discussions
• 影响因子: 3.4
• 作者: A. Vaughan;James D. Lee;M. Shaw;P. Misztal;S. Metzger;S. Metzger;M. Vieno;B. Davison;T. Karl;L. Carpenter;A. Lewis;R. Purvis;A. Goldstein;C. Hewitt

Airborne determination of the temporo-spatial distribution of benzene, toluene, nitrogen oxides and ozone in the boundary layer across Greater London, UK

• DOI: 10.5194/acp-15-5083-2015
• 发表时间: 2014-10
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: M. Shaw;James D. Lee;B. Davison;A. Vaughan;R. Purvis;A. Harvey;A. Lewis;C. Hewitt