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

在全球范围内，海洋吸收的二氧化碳比它释放到大气中的更多，并将一部分多余的碳储存在深海中。下沉的颗粒，包括活的浮游生物和非活的碎屑，是碳通量的主要贡献者。现代照相机系统和图像分析技术使人们能够对这些颗粒进行计数、测量和分类，从而为海洋学家提供了一种工具，以高分辨率的空间和时间来估计向深海转移的碳。不幸的是，仅仅知道颗粒的大小还不足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速度。这个项目使用一个连接在粒子捕捉器上的摄像机来检查单个粒子沉入深海时的速度。对于每一种颗粒，确定分类标准，如大小、形状因素、光密度，如果是浮游生物，则确定分类鉴定标准，并与其各自的下沉速度进行比较。这一信息有助于根据对水柱中颗粒的调查计算总体下沉速度，从而根据相机图像对碳通量进行更可靠的估计。该项目支持水下成像系统的技术开发，研究生和本科生教育，以及通过公共宣传计划为中学生及其导师提供科学素养倡议。船舶和自主车辆海洋颗粒清单调查为高时空分辨率的碳通量估算提供了巨大的希望。然而，虽然较大颗粒（如有孔虫壳和海鞘、磷虾和较大桡足类的粪便颗粒）的下沉速度相对较好地受到限制，但较小颗粒尺寸池（50-500微米）的动力学仍然更加难以捉摸。尽管小颗粒的大小和假定的缓慢下沉速度，但它们大量存在于中层和沉积物捕集器材料中。它们在中层的丰度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的残余物，具有高过量密度的颗粒，如成石尘埃颗粒、原生生物排出的小颗粒、原生生物孢子，或者是由于通量饲养者的活动而在深层破碎的结果，等等。该项目通过将单独分辨的光学特征与下沉速度联系起来，解决了一些关于小颗粒池的未回答的问题。使用斯托克斯定律，根据尺寸和下沉速度估计多余密度，然后根据光学测量将其分配给粒子。一个水平安装的摄像机系统记录沉降速度，大小，和附着在海洋通量计划系泊阵列的沉积物陷阱中的颗粒特征。记录的颗粒使用以下方法进行表征：1）经典图像分析，考虑各种形状因素; 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深度学习的图像分类。第二个相同的相机调查在同一站和时间在水柱中的粒子库存，以整合现有的和未受干扰的粒子池通量估计。Niskin瓶样品和颗粒的显微镜检查增强了图像数据的解释。该项目的成果有助于实现基于光学粒子调查的碳通量模型的更高预测能力的总体目标。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.