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Initial development and laboratory testing of a ChLorophyll Absorption Meter (CLAM)

叶绿素吸收计 (CLAM) 的初步开发和实验室测试

基本信息

批准号：
NE/P003443/1
负责人：
Giorgio Dall'Olmo
金额：
$ 14.69万
依托单位：
Plymouth Marine Laboratory
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FP003443%2F1
关键词：
Initial development laboratory testing ChLorophyll

项目摘要

Invisible microscopic algae in the ocean and freshwaters (phytoplankton) are plants that photosynthesise by absorbing solar radiation. In doing so, they provide half of the oxygen we breathe, they support marine ecosystem and the fisheries upon which we depend on, and absorb large quantities of CO2 from the atmosphere. Thus phytoplankton are a crucial component of oceanic and freshwater ecosystems that sustains human population.To be able to effectively predict future changes in phytoplankton and manage environmental resources, it is therefore fundamental to accurately quantify the current stocks and variability of microscopic algae living in marine and freshwater bodies. Since all phytoplankton are provided with the photosynthetic pigment chlorophyll-a, the concentration of this pigment (chl) is typically used as a proxy for phytoplankton biomass.However, current methods to determine chl either rely on laborious and expensive collection and laboratory analyses of discrete water samples, or on highly-inaccurate in-situ measurements of chlorophyll fluorescence. Although in-situ fluorescence has the advantage of providing high-resolution measurements, it is affected by phytoplankton physiological processes that render in-situ fluorescence-based estimates of chl highly inaccurate, even if calibrated using collocated discrete data. For example, the ratio of fluorescence to chlorophyll-a concentration can vary by up to a factor 6 due to variations in solar irradiance.As a consequence, *accurate* chl estimates are currently extremely scarce preventing effective management of water resources.The overall objective of this project is to develop and test a prototype of an instrument that ultimately will provide high-resolution in-situ estimates of chl in with unprecedented accuracy.Specifically, we will develop an initial laboratory prototype which will undergo multiple tests and improvements to achieve highly stable and highly accurate measurements of chl. We envision that this instrument could become the new standard method for automatically measuring chl in situ and in the laboratory. This instrument could also be deployed in continuous mode on ships and on autonomous platforms such as gliders and profiling floats, thus greatly extending the availability of highly-accurate and high-resolution in-situ measurements of chl.

海洋和淡水中的肉眼不可见的微小藻类（浮游植物）是通过吸收太阳辐射进行光合作用的植物。在此过程中，它们提供了我们呼吸的氧气的一半，它们支持我们赖以生存的海洋生态系统和渔业，并从大气中吸收大量的二氧化碳。浮游植物是海洋和淡水生态系统的重要组成部分，也是人类赖以生存的基础。为了有效预测浮游植物的未来变化和管理环境资源，必须准确量化海洋和淡水水体中微型藻类的现存量和变异性。由于所有的浮游植物都具有光合色素叶绿素a，因此叶绿素a的浓度通常被用作浮游植物生物量的替代指标，然而，目前测定叶绿素a的方法要么依赖于费力且昂贵的离散水样收集和实验室分析，要么依赖于高度不准确的叶绿素荧光原位测量。虽然原位荧光具有提供高分辨率测量的优势，但它受浮游植物生理过程的影响，即使使用同位离散数据进行校准，也会使基于原位荧光的叶绿素估计高度不准确。例如，由于太阳辐照度的变化，荧光与叶绿素-a浓度的比率可以变化高达6倍。* 准确的 * 叶绿素估计值目前极为缺乏，妨碍了对水资源的有效管理。本项目的总体目标是开发和测试一种仪器的原型，最终将提供高分辨率的-具体而言，我们将开发一个初步的实验室原型，该原型将经过多次测试和改进，以实现对叶绿素的高度稳定和高度准确的测量。我们预期此仪器可成为新的标准方法，在现场和实验室自动测量叶绿素。该仪器也可以连续模式部署在船舶和自主平台上，如滑翔机和剖面浮标，从而大大扩展了高精度和高分辨率的叶绿素原位测量的可用性。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals

DOI：
10.1016/j.rse.2016.05.005
发表时间：
2016-09-15
期刊：
REMOTE SENSING OF ENVIRONMENT
影响因子：
13.5
作者：
Brewin, Robert J. W.;Dall'Olmo, Giorgio;Boss, Emmanuel S.
通讯作者：
Boss, Emmanuel S.

Evaluating Optical Proxies of Particulate Organic Carbon across the Surface Atlantic Ocean

DOI：
10.3389/fmars.2017.00367
发表时间：
2017-11
期刊：
Frontiers in Marine Science
影响因子：
3.7
作者：
R. Rasse;G. Dall’Olmo;Jason R. Graff;T. Westberry;Virginie van Dongen-Vogels;M. Behrenfeld
通讯作者：
R. Rasse;G. Dall’Olmo;Jason R. Graff;T. Westberry;Virginie van Dongen-Vogels;M. Behrenfeld

Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient.

根据光谱颗粒束衰减系数准确深度学习估计叶绿素 a 浓度。