CHALLENGER DEEP: Lin Ying-Tsong was invited by Caladan Oceanic founder Victor Vescovo to join him on a 10-hour long trip in the company’s submersible

By Lin Chia-nan

Taiwanese-American Lin Ying-Tsong (林穎聰) last month became the first person from Asia and the 12th in human history to dive into the deepest part on Earth, the Challenger Deep in the Mariana Trench.

Lin, 45, an expert in deep sea acoustics with the Woods Hole Oceanographic Institution (WHOI) in Massachusetts, joined US adventurer and Caladan Oceanic founder Victor Vescovo, 54, on June 22 in a descent to the central pool of the Challenger Deep, the deepest point of the trench, which lies at a depth of more than 10,900m.

The pair made the descent in a submersible named Limiting Factor, a US$37 million two-seater commissioned by Vescovo from Triton Submarines.

Inside the submersible’s capsule the pressure was kept at one standard atmospheric pressure, while the temperature dropped from about 28 ° C to 20 ° C during the dive and climb, he said, adding that the dive and the return climb took a total of 10 hours.

There are three windows in the capsule, and when looking out from the window beneath their feet, he felt as if he was doing a “seawalk,” Lin said.

Photo courtesy of Caladan Oceanic

The ocean bottom appeared to be an otherworldly desert of deadly gray, where only limited species, such as amphipods, can survive the extreme conditions, he said.

When ascending to the surface, he saw, from the depth of nearly 300m, sunlight gradually extend into a carpet of radiance that brought all colors back, he said, describing the scene as a “sunrise in ocean.”

Maintaining the balance of oxygen and carbon dioxide in the capsule was a matter of life and death, so Vescovo, who was piloting the craft, had to regularly check that oxygen was evenly released, Lin said.

The submersible was equipped with an oxygen reserve sufficient to last two passengers for four days.

Lin’s descent was part of the Caladan Oceanic’s Ring of Fire expedition that began last month, which also included dives by former NASA astronaut Kathy Sullivan — the first US woman to complete a spacewalk — and Kelly Walsh — the son of Don Walsh, the first person to descend to the Challenger Deep, in 1960.

Asked how he got involved in the team, Lin said he received an e-mail invitation from Vescovo, whom he had not met before, in late May after he returned from a cruise to measure underwater acoustics for an offshore wind farm project.

While Vescovo reportedly has opened some dive slots for paying customers, Lin said his descent was sponsored by Vescovo.

“I must have done something noble in my former life, such as saving millions of lives, to have the opportunity to join the team,” he said, still sounding excited by his adventure.

“Because he is an expert in deep ocean acoustics, measurement, and tracking, Dr Lin’s involvement in the expedition was important in advancing further exploration and understanding of how sound waves propagate in the deepest parts of the ocean,” Caladan Oceanic said in a press release.

“I was also happy to make the descent with the first person from Taiwan in a first for that country — and continent — since I strongly support the special US-Taiwan relationship,” Vescovo, a former US Navy commander, said in the statement.

Using the submersible and the company’s support ship the Pressure Drop, Lin conducted a series of acoustics experiments in the week of his dive, including surveying ambient sound and acoustic signals with a hydrophone recorder provided by the US National Oceanic and Atmospheric Administration.

The surveys aim to advance understanding about how sounds propagate and refract in different ocean layers and how the derived coefficients can be applied to estimate the geological components of seabed, Lin said.

He said he had been impressed by how quiet the ocean could be.

The deep sea’s ambient sound only measured 55 decibels when there was no container ship passing by, as the COVID-19 pandemic has reduced ship traffic, he said.

Lin also admired the teamwork of the expedition team, which has concrete scientific goals, including mapping the sea floors and collecting biological and geological samples at the bottom of the Challenger Deep.

“I feel home here [the expedition] like at WHOI, because we are all team players, no individuals, no pointing fingers,” Lin wrote on Facebook on June 27.

Lin obtained his master’s and doctoral degrees from National Taiwan University’s engineering science and ocean engineering department, and then went to Woods Hole to conduct postdoc research, but stayed on after finishing his research project and is now a tenured associate scientist at the institute, where his work has won him several awards.

He said that he has benefited from decades-long Taiwan-US collaboration in ocean research and now he serves as a bridge to sustain the ties from his vantage point at one of the world’s top ocean research institutions.

Lin said that Taiwan’s government should lend more support to ocean sciences and promote public education about the oceans, while scientists from different backgrounds should strengthen their collaborative efforts.

“Taiwan is a maritime country” should be more than just a slogan, he said.

A strong nation is bolstered by its sea power, which could be enhanced by boosting ocean research capacity, he added.

Source:https://www.taipeitimes.com/News/taiwan/archives/2020/07/10/2003739683

The ‘carbon recycling’ scheme will provide sustainable food for fish and poultry farms which has an up to 75 per cent smaller carbon footprint than other food sources.

Written by: Rosie Frost / Euronews.com

While we are becoming increasingly aware that a meat-based diet has a greater environmental impact than a plant-based one, few know that the biggest environmental impact comes from the food the animals eat. Just last week an article published in the Journal Science claimed that a fifth of soy and beef imported into the EU from Brazil came from illegally deforested land.

In 2017, the WWF found that 75 per cent of global soy and maize production was being grown to feed animals, mostly pigs and poultry. With diets around the world tending towards higher consumption of meat, the production of animal feed poses a growing threat to biodiversity as land is cleared to grow more crops.

Animal feed is usually made from soy, fishmeal or grains – which can lead to these devastating environmental impacts. This new process, created by Deep Branch Biotechnology, takes CO2 from industrial emissions and uses it as an energy source for microbes which generate a single-cell protein specially designed for animal feed.

CEO of the company, Peter Rowe, explained that the technology could help reduce the UK’s reliance on carbon-intensive international supply chains. Its potential use in food for fish and poultry “represents a new way of generating more sustainable animal feeds.”

Making Farming Carbon Neutral

To provide a source of industrial CO2 for the process, Deep Branch has partnered with a power station in the north of England which was recently converted to burn renewable biomass instead of coal.

Drax Power Station in Selby is the UK’s largest power station and supplies 5 percent of the country’s electricity needs. Drax has ambitions to become carbon negative by 2030 and capturing the CO2 it emits for projects like this one is part of this plan. Already the plant has been experimenting with using its captured CO2 to create new plastic products.

They have been awarded £3 million (€ 3.3 million) in funding by the government to test whether the process will work on a larger scale and what the overall carbon emissions would be.

This is partnership represents part of a wider consortium of 10 industry and academic groups called REACT-FIRST which received the £3 million funding. The consortium is “committed to tackling the global climate crisis and the goal of achieving neutral/negative carbon emissions”.

“Currently, most animal feed protein sources are imported from overseas, making the UK dependent on complicated and fragile supply chains,” said Rowe. “REACT-FIRST has been created to focus solely on addressing this problem.”

REACT-FIRST also includes Nottingham Trent University and UK supermarket Sainsbury’s alongside a number of other agriculture technology companies.

From Robotic Fruit Picking to Vertical Farming

The Yorkshire project is one of nine which received part of a £24 million funding package aimed at making the food production system in the UK more efficient. Others included a vertical fruit grower in London, AI technology being used to improve the efficiency of farms and a project testing whether robots can be used to carry out energy-intensive jobs like picking fruit.

“From robotics assisting our farmers in fruit picking, to technology that converts CO2 to clean animal feed, the incredible projects we are backing today represent the future of farming,” said UK Science Minister, Amanda Solloway.

She added that using the “best of British science” the projects chosen by the government would “help accelerate our transition to net-zero food production”.

Source:

Japanese nine companies (Note 1) have started the Ship Carbon Recycling Working Group (hereinafter referred to as “WG”) formed within Japan’s Carbon Capture & Reuse (CCR) Study Group (Note 2), and held its first meeting. Participating members are EX Research Institute Ltd., Hitachi Zosen Corporation, Japan Marine United Corporation, JFE Steel Corporation, JGC Corporation, Mitsui O.S.K. Lines, Ltd., Nippon Kaiji Kyokai(ClassNK), Nippon Steel Corporation, and Sanoyas Shipbuilding Corporation.

As the effects of climate change become apparent, carbon recycling, a method used to capture and reuse emitted carbon dioxide (CO2), is attracting attention as one of the paths to a decarbonized society.

Formed within the CCR Study Group in August 2019, the WG aims to explore the feasibility of the concept of utilizing methanation technology (Note 3) for zero-emission ship fuels (Note 4). Through its activities, the WG aims to reduce greenhouse gas emissions to zero in sea transportation, which accounts for 99.6% of Japanese imports and exports, and thereby contribute to the formation of a sustainable society.

Specifically, the nine companies listed above plan to assume carbon recycling supply chain of methanation fuel that involves the supply of feedstock CO2, transportation of the feedstock, methanation, and conversion into marine fuel. They will calculate the estimated amount of CO2 emissions in the supply chain, and based on these results, identify technical challenges and develop a roadmap for its realization.

The first stage of activities involves: (1) Separation, capture and liquefaction of CO2 emitted from steelworks (2) Transportation of liquefied CO2 by ship to a hydrogen supply site (3) Generation of synthetic methane from CO2 and hydrogen by methanation reaction, and (4) Liquefaction of the synthetic methane and using it as marine fuel (Figure 1).

In addition to obtaining an approximate value of CO2 emissions in this assumed supply chain, the group will also identify challenges and decide whether to proceed with subsequent next-stage activities along with the content of those activities. The acquired knowledge will also be widely disclosed in and out of the industry.

An MIT-developed technique could aid in tracking the ocean’s health and productivity.

Jennifer Chu | MIT News Office

On land, it’s fairly obvious where one ecological region ends and another begins, for instance at the boundary between a desert and savanna. In the ocean, much of life is microscopic and far more mobile, making it challenging for scientists to map the boundaries between ecologically distinct marine regions.

One way scientists delineate marine communities is through satellite images of chlorophyll, the green pigment produced by phytoplankton. Chlorophyll concentrations can indicate how rich or productive the underlying ecosystem might be in one region versus another. But chlorophyll maps can only give an idea of the total amount of life that might be present in a given region. Two regions with the same concentration of chlorophyll may in fact host very different combinations of plant and animal life.

“It’s like if you were to look at all the regions on land that don’t have a lot of biomass, that would include Antarctica and the Sahara, even though they have completely different ecological assemblages,” says Maike Sonnewald, a former postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences.

Now Sonnewald and her colleagues at MIT have developed an unsupervised machine-learning technique that automatically combs through a highly complicated set of global ocean data to find commonalities between marine locations, based on their ratios and interactions between multiple phytoplankton species. With their technique, the researchers found that the ocean can be split into over 100 types of “provinces” that are distinct in their ecological makeup. Any given location in the ocean would conceivably fit into one of these 100 ecological  provinces.

The researchers then looked for similarities between these 100 provinces, ultimately grouping them into 12 more general categories. From these “megaprovinces,” they were able to see that, while some had the same total amount of life within a region, they had very different community structures, or balances of animal and plant species. Sonnewald says capturing these ecological subtleties is essential to tracking the ocean’s health and productivity.

“Ecosystems are changing with climate change, and the community structure needs to be monitored to understand knock on effects on fisheries and the ocean’s capacity to draw down carbon dioxide,” Sonnewald says. “We can’t fully understand these vital dynamics with conventional methods, that to date don’t include the ecology that’s there. But our method, combined with satellite data and other tools, could offer important progress.”

Sonnewald, who is now an associate research scholar at Princeton University and a visitor at the University of Washington, has reported the results today in the journal Science Advances. Her coauthors at MIT are Senior Research Scientist Stephanie Dutkiewitz, Principal Research Engineer Christopher Hill, and Research Scientist Gael Forget.

Rolling out a data ball

The team’s new machine learning technique, which they’ve named SAGE, for the Systematic AGgregated Eco-province method, is designed to take large, complicated datasets, and probabilistically project that data down to a simpler, lower-dimensional dataset.

“It’s like making cookies,” Sonnewald says. “You take this horrifically complicated ball of data and roll it out to reveal its elements.”

In particular, the researchers used a clustering algorithm that Sonnewald says is designed to “crawl along a dataset” and hone in on regions with a large density of points — a sign that these points share something in common.

Sonnewald and her colleagues set this algorithm loose on ocean data from MIT’s Darwin Project, a three-dimensional model of the global ocean that combines a model of the ocean’s climate, including wind, current, and temperature patterns, with an ocean ecology model. That model includes 51 species of phytoplankton and the ways in which each species grows and interacts with each other as well as with the surrounding climate and available nutrients.

If one were to try and look through this very complicated, 51-layered space of data, for every available point in the ocean, to see which points share common traits, Sonnewald says the task would be “humanly intractable.” With the team’s unsupervised machine learning algorithm, such commonalities “begin to crystallize out a bit.”

This first “data cleaning” step in the team’s SAGE method was able to parse the global ocean into about 100 different ecological provinces, each with a distinct balance of species.

The researchers assigned each available location in the ocean model to one of the 100 provinces, and assigned a color to each province. They then generated a map of the global ocean, colorized by province type.

“In the Southern Ocean around Antarctica, there’s burgundy and orange colors that are shaped how we expect them, in these zonal streaks that encircle Antarctica,” Sonnewald says. “Together with other features, this gives us a lot of confidence that our method works and makes sense, at least in the model.”

Ecologies unified

The team then looked for ways to further simplify the more than 100 provinces they identified, to see whether they could pick out commonalities even among these ecologically distinct regions.

“We started thinking about things like, how are groups of people distinguished from each other? How do we see how connected to each other we are? And we used this type of intuition to see if we could quantify how ecologically similar different provinces are,” Sonnewald says.

To do this, the team applied techniques from graph theory to represent all 100 provinces in a single graph, according to biomass — a measure that’s analogous to the amount of chlorophyll produced in a region. They chose to group the 100 provinces into 12 general categories, or “megaprovinces.” When they compared these megaprovinces, they found that those that had a similar biomass were composed of very different biological species.

“For instance, provinces D and K have almost the same amount of biomass, but when we look deeper, K has diatoms and hardly any prokaryotes, while D has hardly any diatoms, and a lot of prokaryotes. But from a satellite, they could look the same,” Sonnewald says. “So our method could start the process of adding the ecological information to bulk chlorophyll measures, and ultimately aid observations.”

The team has developed an online widget that researchers can use to find other similarities among the 100 provinces. In their paper, Sonnewald’s colleagues chose to group the provinces into 12 categories. But others may want to divide the provinces into more groups, and drill down into the data to see what traits are shared among these groups.

Sonnewald is sharing the tool with oceanographers who want to identify precisely where regions of a particular ecological makeup are located, so they could, for example, send ships to sample in those regions, and not in others where the balance of species might be slightly different.

“Instead of guiding sampling with tools based on bulk chlorophyll, and guessing where the interesting ecology could be found with this method, you can surgically go in and say, ‘this is what the model says you might find here,’” Sonnewald says. “Knowing what species assemblages are where, for things like ocean science and global fisheries, is really powerful.”

This research was funded, in part, by NASA and the Jet Propulsion Laboratory.

Source: http://news.mit.edu/2020/machine-learning-map-ocean-0529