Linking optical characteristics of small particles (50 - 500 micrometer) with their sinking velocities in the mesopelagic environment

将小颗粒（50 - 500 微米）的光学特性与其在中层环境中的下沉速度联系起来

• 批准号: 2128438
• 负责人: Alexander Bochdansky
• 金额: $ 51.74万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128438&HistoricalAwards=false
• 关键词: Linking; optical; characteristics; small; particles

Globally, the ocean removes more carbon dioxide than it releases into the atmosphere storing a portion of the excess carbon in the deep sea. Sinking particles, both living plankton and non-living detritus, are major contributors to this flux of carbon. Modern camera systems and image analysis techniques have made it possible to count, measure and classify these particles, thus providing oceanographers with a tool to estimate carbon transfers to the deep ocean at high resolution in space and time. Unfortunately, it is not enough to know the sizes of particles to estimate how fast these particles sink because shape and particle density also influence the sinking velocity. This project examines the velocities of individual particles as they sink into the deep ocean using a camera attached to a particle trap. For each of these particles, classification criteria, such as size, shape factors, optical density, and in the case of plankton, taxonomic identification, is determined and compared to their individual sinking velocities. This information serves to calculate overall sinking velocities from surveys of particles in the water column and thereby produce more reliable estimates of carbon fluxes from camera images. This project supports technology development in underwater imaging systems, graduate and undergraduate student education, and science literacy initiatives for middle-school students and their mentors through public outreach programs.Shipboard and autonomous vehicle surveys of oceanic particle inventories hold great promise for estimating carbon fluxes at high temporal and spatial resolutions. However, while the sinking velocities of larger particles such as foraminifera shells and fecal pellets of salps, krill, and larger copepods are relatively well constrained, the dynamics of the smaller particle size pool (50–500 micrometers) remain more elusive. Despite their size and presumed slow sinking velocities, small particles occur in large numbers in the mesopelagic layer and sediment-trap material. Their abundance in the mesopelagic could be the result of deep mixing, or small particles could be remnants of digested larger particles, particles with a high excess density such as lithogenic dust particles, minipellets egested by protists, protist spores, or the result of fragmentation at depth due to the activity of flux feeders, among other possibilities. This project addresses some unanswered questions about the small particle pool by linking individually-resolved optical features with sinking velocities. Using Stokes’ law, excess density is being estimated from size and sinking velocity and then assigned to particles from optical surveys. A horizontally installed camera system records sinking velocities, sizes, and features of particles in a sediment trap attached to the Oceanic Flux Program mooring array. The recorded particles are being characterized using 1) classic image analysis, taking various shape factors into account; 2) opacity of individual particles; and 3) image classification with supervised and unsupervised deep learning using convolutional neural networks. A second identical camera surveys the particle inventory at the same station and time in the water column to integrate flux estimates over the existing and undisturbed particle pool. Niskin bottle samples and microscopic examination of particles augment the interpretation of image data. The results of this project contribute to the overarching goal of achieving higher predictive power for carbon flux models based on optical particle surveys.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在全球范围内，海洋去除了二氧化碳多于释放到大气中，将多余的碳的一部分存储在深海中。下沉的颗粒（均活着的浮游生物和非生存的碎屑）是这种碳通量的主要因素。现代的摄像头系统和图像分析技术使计算，测量和分类这些粒子成为可能，从而为海洋学者提供了一种工具，可以在时空上高分辨率估算向深海的碳传输。不幸的是，不足以了解颗粒的尺寸以估计这些颗粒下沉的速度，因为形状和颗粒密度也会影响下沉的速度。该项目使用附着在粒子陷阱上的相机进入深海时，检查了它们的速度。对于这些颗粒中的每一个，分类标准（例如大小，形状因子，光密度）以及在浮游生物的情况下，分类学识别并将其与其个体下沉速度进行了比较。该信息可用于计算水柱中颗粒调查的总体下沉速度，从而产生更多可靠的相机图像碳通量估算值。该项目通过公共宣传计划支持水下成像系统，研究生和本科生教育以及针对中学生及其导师的科学素养计划的技术开发。海洋粒子库存的船舶和自动驾驶汽车调查具有巨大的前景，可以在高临时和空间压力下估算碳的碳通量。然而，尽管较大颗粒的下沉速度，例如有孔虫壳和乳液，磷虾和较大的copods的粪便相对较好，但较小的粒径池（50-500微米）的动力学仍然更难以捉摸。尽管它们的大小并表现出缓慢的下沉速度，但小颗粒在中质层和沉积物陷阱材料中大量出现。它们在中质中的抽象可能是深层混合的结果，或者小颗粒可能是消化的较大颗粒，较大密度的颗粒，例如岩性粉尘颗粒，是由生物，原生孢子，生物孢子或由于Flux Feeders的深度而产生的碎片的结果。该项目通过将单独解决的光学特征与下沉速度联系起来，解决了有关小粒子池的一些未解决问题。使用Stokes的定律，通过大小和下沉速度估算过量密度，然后从光学调查中分配给颗粒。水平安装的摄像头系统记录下沉速度，尺寸和颗粒的特征，该沉积物陷阱连接到海洋通量程序系泊阵列。使用1）经典图像分析对记录的粒子进行表征，考虑各种形状因子； 2）单个颗粒的不透明度； 3）使用卷积神经网络进行监督和无监督的深度学习图像分类。第二个相同的摄像头在水柱中的同一站点和时间上调查粒子清单，以整合现有和未受干扰的粒子池的通量估计。 Niskin瓶样品和颗粒的显微镜检查增强了图像数据的解释。该项目的结果有助于基于光学粒子调查来实现碳通量模型更高的预测能力的总体目标。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来评估NSF的法定任务。

项目成果

The aquatic particle number quandary

• DOI: 10.3389/fmars.2022.994515
• 发表时间: 2022-11-07
• 期刊: FRONTIERS IN MARINE SCIENCE
• 影响因子: 3.7
• 作者: Bochdansky，Alexander B. B.;Huang，Huanqing;Conte，Maureen H. H.
• 通讯作者: Conte，Maureen H. H.