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).

估计气候对自然或人为变化的敏感度的最大不确定因素仍然是云层(IPCC 2001,2007)。这种不确定性在很大程度上是由于缺乏与较小的云粒(水滴、冰晶)和气溶胶的性质有关的信息。这些粒子直接和间接地影响云层反射回太空的太阳光数量(即：冷却地球)和捕获多少来自地球的红外线或热辐射(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，以便能够预测云的特性对我们未来气候的影响。云微物理学家拥有几种类型的现场仪器，用于对大气颗粒进行计数并确定其大小，使其精确到亚微米大小，而其他仪器可以捕捉到较大单个颗粒的真实图像。这类图像特别有价值，因为它们提供了详细的颗粒形状数据，但仪器的光学像差和景深限制导致图像模糊，将此类成像技术限制在尺寸大于~25um的颗粒上。最缺乏知识，因此可能是最大的不确定性来源，围绕着较小的粒子，如冰晶大小到微米，远远低于云粒子成像探测器的分辨率限制。另一种可以提供有关这些较小云粒的详细信息的方法是空间光散射，即记录和分析单个粒子通过激光光束散射的独特光模式。过去，赫特福德郡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机构采购。然而，SID探头很大(每个探头需要一个安装在机翼上的‘PMS’罐)和昂贵(&gt；GB 80k)。这将它们的部署限制在数量相对较少的携带经前综合症毒气罐的研究飞机上(而且这种毒气罐的竞争通常很激烈)。因此，这项概念验证提案试图通过开发一种小型、低成本(&lt；GB 3k)和轻质(&lt；1 kg)的“微型SID”传感器来解决这一问题，称为大气颗粒分类器。APC将利用为大众消费市场(如DVD R/RW播放机、安全系统等)开发的二极管激光和探测器阵列技术方面的最新重大技术进步。实现与前身SID探头类似的性能，但成本、尺寸和重量只是其一小部分。APC将以每秒数千的速度对大气颗粒进行计数、分级和分类，将大气颗粒细化到微米级，根据形状区分水滴、固体气溶胶和冰晶，并确定每个颗粒的消光系数(这是了解云辐射特性的一个重要参数)。传感器将足够小，可以由气球或无人机承载，也可以作为单个经前综合症容器中的组合探头的一部分(可能会释放其他经前综合症支架)。它可能会被民用客机携带，从而产生巨大的云数据来源。除此之外，APC还可以在环境健康、污染监测等领域的一般气溶胶监测(见“受益者”)中得到更广泛的应用，在这些领域，了解气溶胶的组成颗粒类型是至关重要的。APC传感器将在UH建造和测试，性能验证和校准由曼彻斯特大学在他们的云模拟舱中进行。完成的APC将通过NERC的地面大气测量设施(FGAM)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

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