A miniature Atmospheric Particle Classifier (APC)

微型大气颗粒分类器 (APC)

• 批准号: NE/H002316/1
• 负责人: Paul Kaye
• 金额: $ 20.06万
• 依托单位: University of Hertfordshire
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH002316%2F1
• 关键词: miniature; Atmospheric; Particle; Classifier; APC

The single greatest source of uncertainty in the estimates of climate sensitivity to either natural or man-made changes continues to be clouds (IPCC 2001, 2007). Much of this uncertainty arises from the lack of information relating to the properties of smaller cloud particles (droplets, ice crystals) and aerosol. These particles directly and indirectly affect how much sunlight the clouds reflect back into space (ie: cooling the Earth) and how much infrared or heat radiation from the Earth's is trapped (ie: warming the Earth). Climate scientists therefore need accurate information on the sizes, shape, and abundance of these different types of atmospheric particle so that the effect of cloud properties on our future climate can be predicted. Cloud microphysicists have at their disposal several types of in-situ instrument for counting and sizing atmospheric particles down to sub-micrometre sizes, whilst other instruments can capture real images of larger individual particles. Such images are especially valuable as they provide detailed particle shape data, but instrument optical aberrations and depth of field limitations result in image blurring, restricting such imaging techniques to particles greater than ~25um in size. The greatest lack of knowledge, and therefore potentially the greatest source of uncertainty, surrounds smaller particles, such as ice crystals down to a micrometre in size, well below the resolution limits of cloud particle imaging probes. An alternative approach that can provide detailed information on these smaller cloud particles is that of spatial light scattering, in which the unique patterns of light scattered by individual particles passing through a laser beam is recorded and analysed. In the past, the University of Hertfordshire has developed several types of aircraft instrument based on spatial light scattering (so called SID probes) and these have been procured by meteorological research organisations in the USA, UK, and Europe. However, SID probes are large (each requiring a 'PMS' wing-mounted canister) and expensive (>£80k). This limits their deployment to the relatively small numbers of research aircraft that carry PMS canisters (and where competition for such canisters is normally intense). This Proof-of-Concept proposal therefore seeks to address this by developing a small, low-cost (<£3k) and light-weight (<1kg) 'miniature SID' sensor, referred to as the Atmospheric particle Classifier. The APC would exploit recent major technological advances in diode laser and detector array technologies developed for mass consumer markets (such as DVD R/RW players, security systems, etc.) to achieve similar performance to the predecessor SID probes but at a small fraction of the cost, size and weight. The APC would count, size and classify atmospheric particles down to micrometre sizes at rates of several thousand per second, differentiating droplets, solid aerosol, and ice crystals on the basis of shape and determining the extinction coefficient of each particle (an important parameter in understanding cloud radiative properties). The sensor would be small enough to be borne by balloon or UAV, or to be part of a combination probe in a single PMS canister (potentially freeing other PMS mountings). It could potentially be carried by civilian passenger aircraft, thus generating a huge source of cloud data. Beyond this, the APC could also find wider application in general aerosol monitoring (see 'Beneficiaries') in areas of environmental health, pollution monitoring, etc., where a knowledge of the aerosol's constituent particle types is essential. The APC sensor would built and tested at UH, with performance validation and calibration being carried out by the University of Manchester in their cloud simulation chamber. The finished APC would become available for use by all of the UK science community through NERC's Facility for Ground-based Atmospheric Measurement (FGAM).

在估计气候对自然或人为变化的敏感性时，最大的不确定性来源仍然是云（IPCC 2001, 2007）。这种不确定性很大程度上是由于缺乏与较小的云粒子（液滴、冰晶）和气溶胶的特性有关的信息。这些粒子直接或间接地影响着云层反射回太空的阳光的多少（即使地球变冷），以及地球的红外线或热辐射被捕获的多少（即使地球变暖）。因此，气候科学家需要关于这些不同类型的大气颗粒的大小、形状和丰度的准确信息，以便预测云特性对未来气候的影响。云微物理学家有几种现场仪器可供他们使用，这些仪器可以对大气颗粒进行计数，并将其缩小到亚微米大小，而其他仪器可以捕捉到更大的单个颗粒的真实图像。这样的图像特别有价值，因为它们提供了详细的颗粒形状数据，但仪器光学像差和景深的限制导致图像模糊，限制了这种成像技术的颗粒大于~25um的尺寸。最大的知识缺乏，因此也可能是最大的不确定性来源，围绕着更小的颗粒，比如小到一微米大小的冰晶，远低于云粒子成像探测器的分辨率限制。另一种可以提供这些较小的云粒子详细信息的方法是空间光散射，在这种方法中，单个粒子通过激光束散射的光的独特模式被记录和分析。在过去，赫特福德大学已经开发了几种基于空间光散射的飞机仪器（所谓的SID探测器），这些仪器已经被美国、英国和欧洲的气象研究组织采购。然而，SID探针很大（每个都需要一个“PMS”机翼安装罐），而且价格昂贵（大约8万英镑）。这限制了它们的部署，只有相对较少的研究飞机携带PMS气罐（而这些气罐的竞争通常是激烈的）。因此，这个概念验证提案试图通过开发一种小型、低成本（< 3000英镑）和轻质（<1kg）的“微型SID”传感器来解决这个问题，该传感器被称为大气颗粒分类器。APC将利用最近为大众消费市场（如DVD R/RW播放器，安全系统等）开发的二极管激光和探测器阵列技术的主要技术进步，以实现与前一代SID探测器相似的性能，但成本，尺寸和重量都是一小部分。APC将以每秒几千个的速度对大气颗粒进行计数、大小和分类，精确到微米大小，根据形状区分液滴、固体气溶胶和冰晶，并确定每个颗粒的消光系数（了解云辐射特性的一个重要参数）。该传感器将足够小，可以由气球或无人机携带，或者作为单个PMS罐中组合探头的一部分（可能解放其他PMS安装）。它有可能被民用客机搭载，从而产生巨大的云数据来源。除此之外，APC还可以在环境卫生、污染监测等领域的一般气溶胶监测（见“受益者”）中找到更广泛的应用，在这些领域，气溶胶组成颗粒类型的知识是必不可少的。APC传感器将在UH建造和测试，性能验证和校准将由曼彻斯特大学在他们的云模拟室进行。完成的APC将通过NERC的地面大气测量设施（FGAM）提供给所有英国科学界使用。

项目成果

Cluster analysis of WIBS single-particle bioaerosol data

• DOI: 10.5194/amt-6-337-2013
• 发表时间: 2012-09
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: N. Robinson;J. Allan;J. A. Huffman;P. Kaye;Virginia Foot;M. Gallagher

Measurements and comparison of primary biological aerosol above and below a tropical forest canopy using a dual channel fluorescence spectrometer

• DOI: 10.5194/acp-10-4453-2010
• 发表时间: 2010-01-01
• 期刊: ATMOSPHERIC CHEMISTRY AND PHYSICS
• 影响因子: 6.3
• 作者: Gabey, A. M.;Gallagher, M. W.;Stanley, W. R.
• 通讯作者: Stanley, W. R.

Modelling light scattering by absorbing smooth and slightly rough facetted particles

通过吸收光滑和稍微粗糙的多面粒子来模拟光散射

• DOI: 10.1016/j.jqsrt.2015.02.004
• 发表时间: 2015
• 期刊: Journal of Quantitative Spectroscopy and Radiative Transfer
• 影响因子: 2.3
• 作者: Hesse E

Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

• DOI: 10.5194/amt-10-695-2017
• 发表时间: 2016-07
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: S. Ruske;D. Topping;Virginia Foot;P. Kaye;W. Stanley;I. Crawford;Andrew P. Morse;M. Gallagher

Polarized optical scattering signatures from biological materials

• DOI: 10.1016/j.jqsrt.2010.07.001
• 发表时间: 2010-11
• 期刊: Journal of Quantitative Spectroscopy & Radiative Transfer
• 影响因子: 2.3
• 作者: W. Martin;E. Hesse;J. Hough;W. Sparks;C. Cockell;Z. Ulanowski;T. Germer;P. Kaye