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).

气候对自然变化或人为变化的气候敏感性估计值中最大的不确定性来源继续是云（IPCC 2001，2007）。这种不确定性的大部分是由于缺乏与较小的云颗粒（液滴，冰晶）和气溶胶的特性有关的信息。这些颗粒直接和间接地影响云层反射到太空（即冷却地球）以及从地球被困的多少红外或热辐射（即：加热地球）。因此，气候科学家需要有关这些不同类型的大气粒子的大小，形状和抽象的准确信息，以便可以预测云特性对我们未来气候的影响。云微物理学家可以使用几种类型的现场仪器，用于计算和尺寸的大气颗粒至子微米尺寸，而其他仪器可以捕获较大单个颗粒的真实图像。这些图像特别有价值，因为它们提供了详细的粒子形状数据，但是仪器的光学畸变和磁场限制的深度导致图像模糊，从而将这种成像技术限制在大于〜25UM的粒子上。最大的知识缺乏，因此可能是最大的不确定性来源，周围的颗粒，例如冰晶尺寸，较大尺寸，远低于云粒子成像问题的分辨率限制。可以提供有关这些较小云颗粒的详细信息的另一种方法是空间光散射，其中记录并分析了通过激光束的单个颗粒散射的独特光模式。过去，赫特福德郡大学（University of Hertfordshire）开发了基于空间光散射（所谓的SID问题）的几种类型的飞机仪器，这些飞机是由美国，英国和欧洲的气象研究组织购买的。但是，SID问题很大（每个都需要“ PMS”翼式罐）和昂贵（> 80k）。这将它们的部署限制在携带PMS罐子的相对数量的研究飞机上（在通常的竞争通常激烈的情况下）。因此，该概念证明的提案试图通过开发一个小的低成本（<£3k）和轻量级（<1kg）的“微型SID”传感器（称为大气粒子分类器）来解决这一问题。 APC将探讨针对大众消费市场（例如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最新主要技术进步，以实现与前身SID问题相似的性能，但要以一小部分成本，大小和体重的价格达到。 APC以每秒几千秒钟的速率计算，大小并将大气颗粒分类至微米尺寸，并根据形状并确定每个粒子的扩展系数（理解云辐射特性的重要参数）。传感器将足够小，可以通过气球或无人机出生，或者成为单个PMS罐中的组合探针的一部分（有可能释放其他PMS安装）。这可能是由平民客运飞机携带的，从而产生了巨大的云数据来源。除此之外，APC还可以在环境健康，污染监测等领域中找到一般的气溶胶监测（请参阅“受益人”），在这种情况下，对气溶胶组成粒子类型的了解至关重要。 APC传感器将在UH建造和测试，曼彻斯特大学在其云模拟室中进行性能验证和校准。完成的APC将通过NERC的设施用于地面大气测量（FGAM），可供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

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

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