Ocean science

Subterms

    More stories

    • 125 Shares169 Views

      in Energy

      Ocean scientists measure sediment plume stirred up by deep-sea-mining vehicle

      by

      What will be the impact to the ocean if humans are to mine the deep sea? It’s a question that’s gaining urgency as interest in marine minerals has grown.

      The ocean’s deep-sea bed is scattered with ancient, potato-sized rocks called “polymetallic nodules” that contain nickel and cobalt — minerals that are in high demand for the manufacturing of batteries, such as for powering electric vehicles and storing renewable energy, and in response to factors such as increasing urbanization. The deep ocean contains vast quantities of mineral-laden nodules, but the impact of mining the ocean floor is both unknown and highly contested.

      Now MIT ocean scientists have shed some light on the topic, with a new study on the cloud of sediment that a collector vehicle would stir up as it picks up nodules from the seafloor.

      The study, appearing today in Science Advances, reports the results of a 2021 research cruise to a region of the Pacific Ocean known as the Clarion Clipperton Zone (CCZ), where polymetallic nodules abound. There, researchers equipped a pre-prototype collector vehicle with instruments to monitor sediment plume disturbances as the vehicle maneuvered across the seafloor, 4,500 meters below the ocean’s surface. Through a sequence of carefully conceived maneuvers. the MIT scientists used the vehicle to monitor its own sediment cloud and measure its properties.

      Their measurements showed that the vehicle created a dense plume of sediment in its wake, which spread under its own weight, in a phenomenon known in fluid dynamics as a “turbidity current.” As it gradually dispersed, the plume remained relatively low, staying within 2 meters of the seafloor, as opposed to immediately lofting higher into the water column as had been postulated.

      “It’s quite a different picture of what these plumes look like, compared to some of the conjecture,” says study co-author Thomas Peacock, professor of mechanical engineering at MIT. “Modeling efforts of deep-sea mining plumes will have to account for these processes that we identified, in order to assess their extent.”

      The study’s co-authors include lead author Carlos Muñoz-Royo, Raphael Ouillon, and Souha El Mousadik of MIT; and Matthew Alford of the Scripps Institution of Oceanography.

      Deep-sea maneuvers

      To collect polymetallic nodules, some mining companies are proposing to deploy tractor-sized vehicles to the bottom of the ocean. The vehicles would vacuum up the nodules along with some sediment along their path. The nodules and sediment would then be separated inside of the vehicle, with the nodules sent up through a riser pipe to a surface vessel, while most of the sediment would be discharged immediately behind the vehicle.

      Peacock and his group have previously studied the dynamics of the sediment plume that associated surface operation vessels may pump back into the ocean. In their current study, they focused on the opposite end of the operation, to measure the sediment cloud created by the collectors themselves.

      In April 2021, the team joined an expedition led by Global Sea Mineral Resources NV (GSR), a Belgian marine engineering contractor that is exploring the CCZ for ways to extract metal-rich nodules. A European-based science team, Mining Impacts 2, also conducted separate studies in parallel. The cruise was the first in over 40 years to test a “pre-prototype” collector vehicle in the CCZ. The machine, called Patania II, stands about 3 meters high, spans 4 meters wide, and is about one-third the size of what a commercial-scale vehicle is expected to be.

      While the contractor tested the vehicle’s nodule-collecting performance, the MIT scientists monitored the sediment cloud created in the vehicle’s wake. They did so using two maneuvers that the vehicle was programmed to take: a “selfie,” and a “drive-by.”

      Both maneuvers began in the same way, with the vehicle setting out in a straight line, all its suction systems turned on. The researchers let the vehicle drive along for 100 meters, collecting any nodules in its path. Then, in the “selfie” maneuver, they directed the vehicle to turn off its suction systems and double back around to drive through the cloud of sediment it had just created. The vehicle’s installed sensors measured the concentration of sediment during this “selfie” maneuver, allowing the scientists to monitor the cloud within minutes of the vehicle stirring it up.

      Play video

      A movie of the Patania II pre-prototype collector vehicle entering, driving through, and leaving the low-lying turbidity current plume as part of a selfie operation. For scale, the instrumentation post attached to the front of the vehicle reaches about 3m above the seabed. The movie is sped up by a factor of 20. Credit: Global Sea Mineral Resources

      For the “drive-by” maneuver, the researchers placed a sensor-laden mooring 50 to 100 meters from the vehicle’s planned tracks. As the vehicle drove along collecting nodules, it created a plume that eventually spread past the mooring after an hour or two. This “drive-by” maneuver enabled the team to monitor the sediment cloud over a longer timescale of several hours, capturing the plume evolution.

      Out of steam

      Over multiple vehicle runs, Peacock and his team were able to measure and track the evolution of the sediment plume created by the deep-sea-mining vehicle.

      “We saw that the vehicle would be driving in clear water, seeing the nodules on the seabed,” Peacock says. “And then suddenly there’s this very sharp sediment cloud coming through when the vehicle enters the plume.”

      From the selfie views, the team observed a behavior that was predicted by some of their previous modeling studies: The vehicle stirred up a heavy amount of sediment that was dense enough that, even after some mixing with the surrounding water, it generated a plume that behaved almost as a separate fluid, spreading under its own weight in what’s known as a turbidity current.

      “The turbidity current spreads under its own weight for some time, tens of minutes, but as it does so, it’s depositing sediment on the seabed and eventually running out of steam,” Peacock says. “After that, the ocean currents get stronger than the natural spreading, and the sediment transitions to being carried by the ocean currents.”

      By the time the sediment drifted past the mooring, the researchers estimate that 92 to 98 percent of the sediment either settled back down or remained within 2 meters of the seafloor as a low-lying cloud. There is, however, no guarantee that the sediment always stays there rather than drifting further up in the water column. Recent and future studies by the research team are looking into this question, with the goal of consolidating understanding for deep-sea mining sediment plumes.

      “Our study clarifies the reality of what the initial sediment disturbance looks like when you have a certain type of nodule mining operation,” Peacock says. “The big takeaway is that there are complex processes like turbidity currents that take place when you do this kind of collection. So, any effort to model a deep-sea-mining operation’s impact will have to capture these processes.”

      “Sediment plumes produced by deep-seabed mining are a major concern with regards to environmental impact, as they will spread over potentially large areas beyond the actual site of mining and affect deep-sea life,” says Henko de Stigter, a marine geologist at the Royal Netherlands Institute for Sea Research, who was not involved in the research. “The current paper provides essential insight in the initial development of these plumes.”

      This research was supported, in part, by the National Science Foundation, ARPA-E, the 11th Hour Project, the Benioff Ocean Initiative, and Global Sea Mineral Resources. The funders had no role in any aspects of the research analysis, the research team states. More

    • 138 Shares129 Views

      in Security

      Engineers use artificial intelligence to capture the complexity of breaking waves

      by

      Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex to predict.

      Now, MIT engineers have found a new way to model how waves break. The team used machine learning along with data from wave-tank experiments to tweak equations that have traditionally been used to predict wave behavior. Engineers typically rely on such equations to help them design resilient offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

      The updated model made more accurate predictions of how and when waves break, the researchers found. For instance, the model estimated a wave’s steepness just before breaking, and its energy and frequency after breaking, more accurately than the conventional wave equations.

      Their results, published today in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. Knowing precisely how these waves interact can help hone the design of offshore structures. It can also improve predictions for how the ocean interacts with the atmosphere. Having better estimates of how waves break can help scientists predict, for instance, how much carbon dioxide and other atmospheric gases the ocean can absorb.

      “Wave breaking is what puts air into the ocean,” says study author Themis Sapsis, an associate professor of mechanical and ocean engineering and an affiliate of the Institute for Data, Systems, and Society at MIT. “It may sound like a detail, but if you multiply its effect over the area of the entire ocean, wave breaking starts becoming fundamentally important to climate prediction.”

      The study’s co-authors include lead author and MIT postdoc Debbie Eeltink, Hubert Branger and Christopher Luneau of Aix-Marseille University, Amin Chabchoub of Kyoto University, Jerome Kasparian of the University of Geneva, and T.S. van den Bremer of Delft University of Technology.

      Learning tank

      To predict the dynamics of a breaking wave, scientists typically take one of two approaches: They either attempt to precisely simulate the wave at the scale of individual molecules of water and air, or they run experiments to try and characterize waves with actual measurements. The first approach is computationally expensive and difficult to simulate even over a small area; the second requires a huge amount of time to run enough experiments to yield statistically significant results.

      The MIT team instead borrowed pieces from both approaches to develop a more efficient and accurate model using machine learning. The researchers started with a set of equations that is considered the standard description of wave behavior. They aimed to improve the model by “training” the model on data of breaking waves from actual experiments.

      “We had a simple model that doesn’t capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking,” Eeltink explains. “Then we wanted to use machine learning to learn the difference between the two.”

      The researchers obtained wave breaking data by running experiments in a 40-meter-long tank. The tank was fitted at one end with a paddle which the team used to initiate each wave. The team set the paddle to produce a breaking wave in the middle of the tank. Gauges along the length of the tank measured the water’s height as waves propagated down the tank.

      “It takes a lot of time to run these experiments,” Eeltink says. “Between each experiment you have to wait for the water to completely calm down before you launch the next experiment, otherwise they influence each other.”

      Safe harbor

      In all, the team ran about 250 experiments, the data from which they used to train a type of machine-learning algorithm known as a neural network. Specifically, the algorithm is trained to compare the real waves in experiments with the predicted waves in the simple model, and based on any differences between the two, the algorithm tunes the model to fit reality.

      After training the algorithm on their experimental data, the team introduced the model to entirely new data — in this case, measurements from two independent experiments, each run at separate wave tanks with different dimensions. In these tests, they found the updated model made more accurate predictions than the simple, untrained model, for instance making better estimates of a breaking wave’s steepness.

      The new model also captured an essential property of breaking waves known as the “downshift,” in which the frequency of a wave is shifted to a lower value. The speed of a wave depends on its frequency. For ocean waves, lower frequencies move faster than higher frequencies. Therefore, after the downshift, the wave will move faster. The new model predicts the change in frequency, before and after each breaking wave, which could be especially relevant in preparing for coastal storms.

      “When you want to forecast when high waves of a swell would reach a harbor, and you want to leave the harbor before those waves arrive, then if you get the wave frequency wrong, then the speed at which the waves are approaching is wrong,” Eeltink says.

      The team’s updated wave model is in the form of an open-source code that others could potentially use, for instance in climate simulations of the ocean’s potential to absorb carbon dioxide and other atmospheric gases. The code can also be worked into simulated tests of offshore platforms and coastal structures.

      “The number one purpose of this model is to predict what a wave will do,” Sapsis says. “If you don’t model wave breaking right, it would have tremendous implications for how structures behave. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors.”

      This research is supported, in part, by the Swiss National Science Foundation, and by the U.S. Office of Naval Research. More

    • 88 Shares199 Views

      in Environment

      Predator interactions chiefly determine where Prochlorococcus thrive

      by

      Prochlorococcus are the smallest and most abundant photosynthesizing organisms on the planet. A single Prochlorococcus cell is dwarfed by a human red blood cell, yet globally the microbes number in the octillions and are responsible for a large fraction of the world’s oxygen production as they turn sunlight into energy.

      Prochlorococcus can be found in the ocean’s warm surface waters, and their population drops off dramatically in regions closer to the poles. Scientists have assumed that, as with many marine species, Prochlorococcus’ range is set by temperature: The colder the waters, the less likely the microbes are to live there.

      But MIT scientists have found that where the microbe lives is not determined primarily by temperature. While Prochlorococcus populations do drop off in colder waters, it’s a relationship with a shared predator, and not temperature, that sets the microbe’s range. These findings, published today in the Proceedings of the National Academy of Sciences, could help scientists predict how the microbes’ populations will shift with climate change.

      “People assume that if the ocean warms up, Prochlorococcus will move poleward. And that may be true, but not for the reason they’re predicting,” says study co-author Stephanie Dutkiewicz, senior research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So, temperature is a bit of a red herring.”

      Dutkiewicz’s co-authors on the study are lead author and EAPS Research Scientist Christopher Follett, EAPS Professor Mick Follows, François Ribalet and Virginia Armbrust of the University of Washington, and Emily Zakem and David Caron of the University of Southern California at Los Angeles.

      Temperature’s collapse

      While temperature is thought to set the range of Prochloroccus and other phytoplankton in the ocean, Follett, Dutkiewicz, and their colleagues noticed a curious dissonance in data.

      The team examined observations from several research cruises that sailed through the northeast Pacific Ocean in 2003, 2016, and 2017. Each vessel traversed different latitudes, sampling waters continuously and measuring concentrations of various species of bacteria and phytoplankton, including Prochlorococcus. 

      The MIT team used the publicly archived cruise data to map out the locations where Prochlorococcus noticeably decreased or collapsed, along with each location’s ocean temperature. Surprisingly, they found that Prochlorococcus’ collapse occurred in regions of widely varying temperatures, ranging from around 13 to 18 degrees Celsius. Curiously, the upper end of this range has been shown in lab experiments to be suitable conditions for Prochlorococcus to grow and thrive.

      “Temperature itself was not able to explain where we saw these drop-offs,” Follett says.

      Follett was also working out an alternate idea related to Prochlorococcus and nutrient supply. As a byproduct of its photosynthesis, the microbe produces carbohydrate — an essential nutrient for heterotrophic bacteria, which are single-celled organisms that do not photosynthesize but live off the organic matter produced by phytoplankton.

      “Somewhere along the way, I wondered, what would happen if this food source Prochlorococcus was producing increased? What if we took that knob and spun it?” Follett says.

      In other words, how would the balance of Prochlorococcus and bacteria shift if the bacteria’s food increased as a result of, say, an increase in other carbohydrate-producing phytoplankton? The team also wondered: If the bacteria in question were about the same size as Prochlorococcus, the two would likely share a common grazer, or predator. How would the grazer’s population also shift with a change in carbohydrate supply?

      “Then we went to the whiteboard and started writing down equations and solving them for various cases, and realized that as soon as you reach an environment where other species add carbohydrates to the mix, bacteria and grazers grow up and annihilate Prochlorococcus,” Dutkiewicz says.

      Nutrient shift

      To test this idea, the researchers employed simulations of ocean circulation and marine ecosystem interactions. The team ran the MITgcm, a general circulation model that simulates, in this case, the ocean currents and regions of upwelling waters around the world. They overlaid a biogeochemistry model that simulates how nutrients are redistributed in the ocean. To all of this, they linked a complex ecosystem model that simulates the interactions between many different species of bacteria and phytoplankton, including Prochlorococcus.

      When they ran the simulations without incorporating a representation of bacteria, they found that Prochlorococcus persisted all the way to the poles, contrary to theory and observations. When they added in the equations outlining the relationship between the microbe, bacteria, and a shared predator, Prochlorococcus’ range shifted away from the poles, matching the observations of the original research cruises.

      In particular, the team observed that Prochlorococcus thrived in waters with very low nutrient levels, and where it is the dominant source of food for bacteria. These waters also happen to be warm, and Prochlorococcus and bacteria live in balance, along with their shared predator. But in more nutrient-rich enviroments, such as polar regions, where cold water and nutrients are upwelled from the deep ocean, many more species of phytoplankton can thrive. Bacteria can then feast and grow on more food sources, and in turn feed and grow more of its shared predator. Prochlorococcus, unable to keep up, is quickly decimated. 

      The results show that a relationship with a shared predator, and not temperature, sets Prochlorococcus’ range. Incorporating this mechanism into models will be crucial in predicting how the microbe — and possibly other marine species — will shift with climate change.

      “Prochlorococcus is a big harbinger of changes in the global ocean,” Dutkiewicz says. “If its range expands, that’s a canary — a sign that things have changed in the ocean by a great deal.”

      “There are reasons to believe its range will expand with a warming world,” Follett adds.” But we have to understand the physical mechanisms that set these ranges. And predictions just based on temperature will not be correct.” More

    • 63 Shares169 Views

      in Security

      Saving seaweed with machine learning

      by

      Last year, Charlene Xia ’17, SM ’20 found herself at a crossroads. She was finishing up her master’s degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program.

      “I had two thoughts: I’m either going to get a PhD to work on a project that protects our planet, or I’m going to start a restaurant,” recalls Xia.

      Xia poured over her extensive cookbook collection, researching international cuisines as she anxiously awaited word about her graduate school applications. She even looked into the cost of a food truck permit in the Boston area. Just as she started hatching plans to open a plant-based skewer restaurant, Xia received word that she had been accepted into the mechanical engineering graduate program at MIT.

      Shortly after starting her doctoral studies, Xia’s advisor, Professor David Wallace, approached her with an interesting opportunity. MathWorks, a software company known for developing the MATLAB computing platform, had announced a new seed funding program in MIT’s Department of Mechanical Engineering. The program encouraged collaborative research projects focused on the health of the planet.

      “I saw this as a super-fun opportunity to combine my passion for food, my technical expertise in ocean engineering, and my interest in sustainably helping our planet,” says Xia.

      Play video

      From MIT Mechanical Engineering: “Saving Seaweed with Machine Learning”

      Wallace knew Xia would be up to the task of taking an interdisciplinary approach to solve an issue related to the health of the planet. “Charlene is a remarkable student with extraordinary talent and deep thoughtfulness. She is pretty much fearless, embracing challenges in almost any domain with the well-founded belief that, with effort, she will become a master,” says Wallace.

      Alongside Wallace and Associate Professor Stefanie Mueller, Xia proposed a project to predict and prevent the spread of diseases in aquaculture. The team focused on seaweed farms in particular.

      Already popular in East Asian cuisines, seaweed holds tremendous potential as a sustainable food source for the world’s ever-growing population. In addition to its nutritive value, seaweed combats various environmental threats. It helps fight climate change by absorbing excess carbon dioxide in the atmosphere, and can also absorb fertilizer run-off, keeping coasts cleaner.

      As with so much of marine life, seaweed is threatened by the very thing it helps mitigate against: climate change. Climate stressors like warm temperatures or minimal sunlight encourage the growth of harmful bacteria such as ice-ice disease. Within days, entire seaweed farms are decimated by unchecked bacterial growth.

      To solve this problem, Xia turned to the microbiota present in these seaweed farms as a predictive indicator of any threat to the seaweed or livestock. “Our project is to develop a low-cost device that can detect and prevent diseases before they affect seaweed or livestock by monitoring the microbiome of the environment,” says Xia.

      The team pairs old technology with the latest in computing. Using a submersible digital holographic microscope, they take a 2D image. They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

      “Using a machine learning network, you can take a 2D image and reconstruct it almost in real time to get an idea of what the microbiome looks like in a 3D space,” says Xia.

      The software can be run in a small Raspberry Pi that could be attached to the holographic microscope. To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

      In that work, under the guidance of Professor Allan Adams and Professor Joseph Paradiso in the Media Lab, Xia focused on developing small underwater communication devices that can relay data about the ocean back to researchers. Rather than the usual $4,000, these devices were designed to cost less than $100, helping lower the cost barrier for those interested in uncovering the many mysteries of our oceans. The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

      By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

      “It’s almost like having the ‘internet of things’ underwater,” adds Xia. “I’m developing this whole underwater camera system alongside the wireless communication I developed that can give me the data while I’m sitting on dry land.”

      Armed with these data about the microbiome, Xia and her team can detect whether or not a disease is about to strike and jeopardize seaweed or livestock before it is too late.

      While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prompt people to rethink how they consider food production in general.

      “We should think about farming and food production in terms of the entire ecosystem,” she says. “My meta-goal for this project would be to get people to think about food production in a more holistic and natural way.” More

    • 125 Shares199 Views

      in Environment

      What will happen to sediment plumes associated with deep-sea mining?

      by

      In certain parts of the deep ocean, scattered across the seafloor, lie baseball-sized rocks layered with minerals accumulated over millions of years. A region of the central Pacific, called the Clarion Clipperton Fracture Zone (CCFZ), is estimated to contain vast reserves of these rocks, known as “polymetallic nodules,” that are rich in nickel and cobalt  — minerals that are commonly mined on land for the production of lithium-ion batteries in electric vehicles, laptops, and mobile phones.

      As demand for these batteries rises, efforts are moving forward to mine the ocean for these mineral-rich nodules. Such deep-sea-mining schemes propose sending down tractor-sized vehicles to vacuum up nodules and send them to the surface, where a ship would clean them and discharge any unwanted sediment back into the ocean. But the impacts of deep-sea mining — such as the effect of discharged sediment on marine ecosystems and how these impacts compare to traditional land-based mining — are currently unknown.

      Now oceanographers at MIT, the Scripps Institution of Oceanography, and elsewhere have carried out an experiment at sea for the first time to study the turbulent sediment plume that mining vessels would potentially release back into the ocean. Based on their observations, they developed a model that makes realistic predictions of how a sediment plume generated by mining operations would be transported through the ocean.

      The model predicts the size, concentration, and evolution of sediment plumes under various marine and mining conditions. These predictions, the researchers say, can now be used by biologists and environmental regulators to gauge whether and to what extent such plumes would impact surrounding sea life.

      “There is a lot of speculation about [deep-sea-mining’s] environmental impact,” says Thomas Peacock, professor of mechanical engineering at MIT. “Our study is the first of its kind on these midwater plumes, and can be a major contributor to international discussion and the development of regulations over the next two years.”

      The team’s study appears today in Nature Communications: Earth and Environment.

      Peacock’s co-authors at MIT include lead author Carlos Muñoz-Royo, Raphael Ouillon, Chinmay Kulkarni, Patrick Haley, Chris Mirabito, Rohit Supekar, Andrew Rzeznik, Eric Adams, Cindy Wang, and Pierre Lermusiaux, along with collaborators at Scripps, the U.S. Geological Survey, and researchers in Belgium and South Korea.

      Play video

      Out to sea

      Current deep-sea-mining proposals are expected to generate two types of sediment plumes in the ocean: “collector plumes” that vehicles generate on the seafloor as they drive around collecting nodules 4,500 meters below the surface; and possibly “midwater plumes” that are discharged through pipes that descend 1,000 meters or more into the ocean’s aphotic zone, where sunlight rarely penetrates.

      In their new study, Peacock and his colleagues focused on the midwater plume and how the sediment would disperse once discharged from a pipe.

      “The science of the plume dynamics for this scenario is well-founded, and our goal was to clearly establish the dynamic regime for such plumes to properly inform discussions,” says Peacock, who is the director of MIT’s Environmental Dynamics Laboratory.

      To pin down these dynamics, the team went out to sea. In 2018, the researchers boarded the research vessel Sally Ride and set sail 50 kilometers off the coast of Southern California. They brought with them equipment designed to discharge sediment 60 meters below the ocean’s surface.  

      “Using foundational scientific principles from fluid dynamics, we designed the system so that it fully reproduced a commercial-scale plume, without having to go down to 1,000 meters or sail out several days to the middle of the CCFZ,” Peacock says.

      Over one week the team ran a total of six plume experiments, using novel sensors systems such as a Phased Array Doppler Sonar (PADS) and epsilometer developed by Scripps scientists to monitor where the plumes traveled and how they evolved in shape and concentration. The collected data revealed that the sediment, when initially pumped out of a pipe, was a highly turbulent cloud of suspended particles that mixed rapidly with the surrounding ocean water.

      “There was speculation this sediment would form large aggregates in the plume that would settle relatively quickly to the deep ocean,” Peacock says. “But we found the discharge is so turbulent that it breaks the sediment up into its finest constituent pieces, and thereafter it becomes dilute so quickly that the sediment then doesn’t have a chance to stick together.”

      Dilution

      The team had previously developed a model to predict the dynamics of a plume that would be discharged into the ocean. When they fed the experiment’s initial conditions into the model, it produced the same behavior that the team observed at sea, proving the model could accurately predict plume dynamics within the vicinity of the discharge.

      The researchers used these results to provide the correct input for simulations of ocean dynamics to see how far currents would carry the initially released plume.

      “In a commercial operation, the ship is always discharging new sediment. But at the same time the background turbulence of the ocean is always mixing things. So you reach a balance. There’s a natural dilution process that occurs in the ocean that sets the scale of these plumes,” Peacock says. “What is key to determining the extent of the plumes is the strength of the ocean turbulence, the amount of sediment that gets discharged, and the environmental threshold level at which there is impact.”

      Based on their findings, the researchers have developed formulae to calculate the scale of a plume depending on a given environmental threshold. For instance, if regulators determine that a certain concentration of sediments could be detrimental to surrounding sea life, the formula can be used to calculate how far a plume above that concentration would extend, and what volume of ocean water would be impacted over the course of a 20-year nodule mining operation.

      “At the heart of the environmental question surrounding deep-sea mining is the extent of sediment plumes,” Peacock says. “It’s a multiscale problem, from micron-scale sediments, to turbulent flows, to ocean currents over thousands of kilometers. It’s a big jigsaw puzzle, and we are uniquely equipped to work on that problem and provide answers founded in science and data.”

      The team is now working on collector plumes, having recently returned from several weeks at sea to perform the first environmental monitoring of a nodule collector vehicle in the deep ocean in over 40 years.

      This research was supported in part by the MIT Environmental Solutions Initiative, the UC Ship Time Program, the MIT Policy Lab, the 11th Hour Project of the Schmidt Family Foundation, the Benioff Ocean Initiative, and Fundación Bancaria “la Caixa.” More