In-Situ Experiments in the Deep Sea

Daniel Shin
May 27, 2018

Submitted as coursework for PH240, Stanford University, Fall 2017


Fig. 1: One of the early remotely operated vehicles from MBARI. (Source: Wikimedia Commons)

The complexity of Earth's changing climate occurring at various regions of the world in both short and long-term timespans presents a significant challenge to any related scientific endeavors. More specifically, studying the role of the ocean, the largest region on the earth by far, is crucial in both understanding and preventing further damages around the globe. For investigating phenomena such as ocean acidification, oxygen minimum zones, or dead zones, acquiring and analyzing samples from regions from the world are essential, but have proven to be very difficult. Not only are the traditional methods of collecting and retrieving samples expensive, but they also introduce a time lag between sample collection and analysis. One potentially effective yet economical method of doing so is to deploy underwater robots, such as the one seen in Fig. 1, that can take data in real-time without the need to retrieve samples.


Research centers such as the Monterey Bay Aquarium Research Institute have developed different systems that can be easily deployed. One example is their Environmental Sample Processor (ESP), which can autonomously collect and filter water samples, and provide analysis in real-time. [1] The ESP provides a cost-effective means of monitoring the changing samples, as no ships are needed. Another prominent technology is their ultraviolet spectrophotometer, which uses the ultraviolet absorption spectrum to measure the concentration of chemicals in the water. [2] Specifically, their capability to take long-term (>3 months) measurements in waters without any human intervention is beneficial for accurately understanding the current chemical concentrations within the waters. Lastly, mobile autonomous cytometer uses a light source to analyze its reflection off microbes to identify and characterize cells in the seawater. [3] Much effort has been devoted into developing technology to make mobile cytometers that can be attached to the underwater robots to enable long-term autonomous cytometry in vast regions that were previously not accessible with traditional methods. This mobile, autonomous technology saves not only time and money, but also contribute greatly to understanding marine microbial communities. Studying the microbial population's response to climate change and artificial disturbances helps in assessing the marine ecosystem health. For instance, monitoring one of the most important single-celled photosynthetic microbes--known as phytoplankton- -is beneficial to learning more about the microbial communities. To do this, they have developed an autonomous system that acquires color phytoplankton images with an extremely high resolution of 1.3 microns per pixel, all over large spatial as well as temporal scales (over 20 kilometers per day for over five weeks). [4]


While most of these technologies benefit greatly from their autonomy, such functionality adds difficult challenges. For instance, developing a system that selectively filters out wanted samples is crucial, as an abundance of samples could prove to be too much to process. Another challenge is to equip these deployed robots with sustainable on-board processing powers. If these systems are to possess standalone analysis capability and not merely collect samples they must have significant computing power to process the appropriate algorithms. For the specific case of mobile cytometer, they are also working to develop state-of-the-art image processing techniques to automatically identify present species of microbe cells.

© Daniel Shin. The author warrants that the work is the author's own and that Stanford University provided no input other than typesetting and referencing guidelines. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.


[1] H. A. Bowers et al., Recovery and Identification of Pseudo-Nitzschia (Bacillariophyceae) Frustlules From Natural Samples Acquired Using the Environmental Sample Processor (ESP)," J. Phycol. 52, 135 (2016).

[2] K. S. Johnson and L. J. Coletti, "In situ Ultraviolet Spectrophotometry For High Resolution and Long-Term Monitoring of Nitrate, Bromide and Bisulfide in the Ocean," Deep-Seaa Res. Pt. I 49, 1291 (2002).

[3] P. Lopez, T. C. O'Reilly, and D. Klimov, "Cytometers Set Sail With Sea-Going Mobile Robots," Mar. Technol. Soc. J., 49, 17 (2015).

[4] L. Ziccarelli et al., "A Novel Method of Obtaining Near Real-Time Observations of Phytoplankton From a Mobile Autonomous Platform," IEEE 7761230, 19 Sep 16.