Pipeline in Alaska (Photo: Moritz Langer)
Pipeline in Alaska (Photo: Moritz Langer)

German team shows how the Arctic faces problems from legacy industrial contamination, pollutants when permafrost thaws

A previously underestimated risk lurks in the frozen soil of the Arctic. When the ground thaws and becomes unstable in response to climate change, it can lead to the collapse of industrial infrastructure, and in turn to the increased release of pollutants. Moreover, contaminations already present will be able to more easily spread throughout ecosystems. A team led by Moritz Langer and Guido Grosse from the Alfred Wegener Institute (AWI) in Potsdam, Germany, investigated the potential scale of this problem. According to their findings, there are at least 13,000 to 20,000 contaminated sites in the Arctic that could pose a serious risk in the future. Accordingly, long-term strategies for handling this volatile legacy are urgently called for, as the experts explained.

Many of us picture the Arctic as largely untouched wilderness. But that has long since ceased to be true for all of the continents. It is also home to oilfields and pipelines, mines, and various other industrial activities. The corresponding facilities were built on a foundation once considered to be particularly stable and reliable: permafrost. This unique type of soil, which can be found in large expanses of the Northern Hemisphere, only thaws at the surface in summer. The remainder, extending up to hundreds of meters down, remains frozen year-round.

Accordingly, permafrost has not only been viewed as a solid platform for buildings and infrastructure. “Traditionally, it’s also been considered a natural barrier that prevents the spread of pollutants,” explains Moritz Langer from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). “Consequently, industrial waste from defunct or active facilities was often simply left on-site, instead of investing the considerable effort and expense needed to remove it.” As a result of the industrial expansion during the cold war, over the decades this led to micro-dumps full of toxic sludge from oil and gas exploration, stockpiles of mining debris, abandoned military installations, and lakes in which pollutants were intentionally poured. “In many cases, the assumption was that the permafrost would reliably and permanently seal off these toxic substances, which meant there was no need for costly disposal efforts,” says Guido Grosse, who heads the AWI’s Permafrost Research Section. “Today, this industrial legacy still lies buried in the permafrost or on its surface. The substances involved range from toxic diesel fuel to heavy metals and even radioactive waste.”

But as climate change progresses, this “sleeping giant” could soon become an acute threat: since the permafrost regions are warming between twice as fast and four times as fast as the rest of the world, the frozen soil is increasingly thawing. When this happens, it changes the hydrology of the region in question, and the permafrost no longer provides an effective barrier. As a result, contaminants that have accumulated in the Arctic over decades can be released, spreading across larger regions. Oil pipeline crosses the tundra (Photo: Guido Grosse)

In addition, thawing permafrost becomes more and more unstable, which can lead to further contamination. When the ground collapses, it can damage pipelines, chemical stockpiles, and depots. Just how real this risk already is can be seen in a major incident from May 2020 near the industrial city Norilsk in northern Siberia: a destabilized storage tank released 17,000 metric tons of diesel, which polluted the surrounding rivers, lakes, and tundra. According to Langer: “Incidents like this could easily become more frequent in the future.”

To more accurately assess such risks, he and an international team of experts from Germany, the Netherlands, and Norway took a closer look at industrial activities in the High North. To do so, they first analyzed freely available data from the portal OpenStreetMap and from the Atlas of Population, Society, and Economy in the Arctic. According to these sources, the Arctic permafrost regions contain ca. 4,500 industrial sites that either store or use potentially hazardous substances.

“But this alone didn’t tell us what types of facilities they were, or how badly they could potentially pollute the environment,” says Langer. More detailed information on contaminated sites is currently only available for North America, where roughly 40 percent of the global permafrost lies. The data from Canada and Alaska showed that using the location and type of facility, it should be possible to accurately estimate where hazardous substances were most likely to be found.

For Alaska, the Contaminated Sites Program also offers insights into the respective types of contaminants. For example, roughly half of the contaminations listed can be attributed to fuels like diesel, kerosene, and petrol. Mercury, lead, and arsenic are also in the top 20 documented environmental pollutants. And the problem isn’t limited to the legacy of past decades: although the number of newly registered contaminated sites in the northernmost state of the USA declined from ca. 90 in 1992 to 38 in 2019, the number of affected sites continues to rise.

There are no comparable databases for Siberia’s extensive permafrost regions. “As such, our only option there was to analyze reports on environmental problems that were published in the Russian media or other freely accessible sources between 2000 and 2020,” says Langer. “But the somewhat sparse information available indicates that industrial facilities and contaminated sites are also closely linked in Russia’s permafrost regions.”

Using supercomputer models, the team calculated the occurrence of contaminated sites for the Arctic as a whole. According to the results, the 4,500 industrial facilities in the permafrost regions have most likely produced between 13,000 and 20,000 contaminated sites. 3,500 to 5,200 of them are located in regions where the permafrost is still stable but will start to thaw before the end of the century. “But without more extensive data, these findings should be considered a rather conservative estimate,” Langer emphasizes. “The true scale of the problem could be even greater.”

Making matters worse, the interest in pursuing commercial activities in the Arctic continues to grow. As a result, more and more industrial facilities are being constructed, which could also release toxic substances into nearby ecosystems. Further, this is happening at a time when removing such environmental hazards is getting harder and harder – after all, doing so often requires vehicles and heavy gear, which can hardly be used on vulnerable tundra soils that are increasingly affected by the thaw.

“In a nutshell, what we’re seeing here is a serious environmental problem that is sure to get worse,” summarises Guido Grosse. What is urgently called for, according to the experts: is more data, and a monitoring system for hazardous substances in connection with industrial activities in the Arctic. “These pollutants can, via rivers and the ocean, ultimately find their way back to people living in the Arctic, or to us.” Other important aspects are intensified efforts to prevent the release of pollutants and undo the damage in those areas that are already contaminated. And lastly, the experts no longer consider it appropriate to leave industrial waste behind in the Arctic without secure disposal options. After all, the permafrost can no longer be relied upon to counter the associated risks.

 

Materials known as metal-organic frameworks (MOFs) have a rigid, cage-like structure that lends itself to a variety of applications, from gas storage to drug delivery. Credits:Image: David Kastner
Materials known as metal-organic frameworks (MOFs) have a rigid, cage-like structure that lends itself to a variety of applications, from gas storage to drug delivery. Credits:Image: David Kastner

MIT scientists use supercomputational modeling to design 'ultrastable' materials

These highly stable metal-organic frameworks could be useful for applications such as capturing greenhouse gases

Materials known as metal-organic frameworks (MOFs) have a rigid, cage-like structure that lends itself to a variety of applications, from gas storage to drug delivery. By changing the building blocks that go into the materials, or the way they are arranged, researchers can design MOFs suited to different uses.

However, not all possible MOF structures are stable enough to be deployed for applications such as catalyzing reactions or storing gases. To help researchers figure out which MOF structures might work best for a given application, MIT researchers have developed a computational approach that allows them to predict which structures will be the most stable.

Using their computational model, the researchers have identified about 10,000 possible MOF structures that they classify as “ultrastable,” making them good candidates for applications such as converting methane gas to methanol.

“When people come up with hypothetical MOF materials, they don’t necessarily know beforehand how stable that material is,” says Heather Kulik, an MIT associate professor of chemistry and chemical engineering, and the senior author of the study. “We used data and our machine-learning models to come up with building blocks that were expected to have high stability, and when we recombined those in ways that were considerably more diverse, our dataset was enriched with materials with higher stability than any previous set of hypothetical materials people had come up with.”

MIT graduate student Aditya Nandy is the lead author of the paper, which appears today in the journal Matter. Other authors are MIT postdoc Shuwen Yue, graduate students Changhwan Oh and Gianmarco Terrones, and Chenru Duan Ph.D. ’22, and Yongchul G. Chung, an associate professor of chemical and biomolecular engineering at Pusan National University.

Modeling MOFs

Scientists are interested in MOFs because they have a porous structure that makes them well-suited to applications involving gases, such as gas storage, separating similar gases from each other, or converting one gas to another. Recently, scientists have also begun to explore using them to deliver drugs or imaging agents within the body.

The two main components of MOFs are secondary building units — organic molecules that incorporate metal atoms such as zinc or copper — and organic molecules called linkers, which connect the secondary building units. These parts can be combined in many different ways, just like LEGO building blocks, Kulik says.

“Because there are so many different types of LEGO blocks and ways you can assemble them, it gives rise to a combinatorial explosion of different possible metal-organic framework materials,” she says. “You can really control the overall structure of the metal-organic framework by picking and choosing how you assemble different components.”

Currently, the most common way to design MOFs is through trial and error. More recently, researchers have begun to try computational approaches to designing these materials. Most such studies have been based on predictions of how well the material will work for a particular application, but they don’t always take into account the stability of the resulting material.

“A really good MOF material for catalysis or gas storage would have a very open structure, but once you have this open structure, it may be really hard to make sure that that material is also stable under long-term use,” Kulik says.

In a 2021 study, Kulik reported a new model that she created by mining a few thousand papers on MOFs to find data on the temperature at which a given MOF would break down and whether particular MOFs can withstand the conditions needed to remove solvents used to synthesize them. She trained the computer model to predict those two features — known as thermal stability and activation stability — based on the molecules’ structure. 

In the new study, Kulik and her students used that model to identify about 500 MOFs with very high stability. Then, they broke those MOFs down into their most common building blocks — 120 secondary building units and 16 linkers.

By recombining these building blocks using about 750 different types of architectures, including many that are not usually included in such models, the researchers generated about 50,000 new MOF structures.

“One of the things that were unique about our set was that we looked at a lot more diverse crystal symmetries than had ever been looked at before, but [we did so] using these building blocks that had only come from experimentally synthesized highly stable MOFs,” Kulik says.

Ultra stability

The researchers then used their computational models to predict how stable each of these 50,000 structures would be and identified about 10,000 that they deemed ultrastable, both for thermal stability and activation stability.

They also screened the structures for their “deliverable capacity” — a measure of a material’s ability to store and release gases. For this analysis, the researchers used methane gas, because capturing methane could be useful for removing it from the atmosphere or converting it to methanol. They found that the 10,000 ultrastable materials they identified had good deliverable capacities for methane and they were also mechanically stable, as measured by their predicted elastic modulus.

“Designing a MOF requires consideration of many types of stability, but our models enable a near-zero-cost prediction of thermal and activation stability,” Nandy says. “By also understanding the mechanical stability of these materials, we provide a new way to identify promising materials.”

The researchers also identified certain building blocks that tend to produce more stable materials. One of the secondary building units with the best stability was a molecule that contains gadolinium, a rare-earth metal. Another was a cobalt-containing porphyrin — a large organic molecule made of four interconnected rings.

Students in Kulik’s lab are now working on synthesizing some of these MOF structures and testing them in the lab for their stability and potential catalytic ability and gas separation ability. The researchers have also made their database of ultrastable materials available for researchers interested in testing them for their scientific applications.

Credit: OUYANG Lin
Credit: OUYANG Lin

Chinese prof Jingjia uses AI to predict ocean waves

Artificial intelligence methods may become a new development direction for ocean wave prediction.

The ability to model and predict the size of ocean waves is important for the fishing industry from both logistic and economic perspectives. Essentially, the bigger the waves, the more expensive the fish. Existing ocean wave models use numerical methods to solve the equations of wind wave action and ocean wave energy to obtain future predictions of ocean waves. However, although good results can be achieved, such models require large amounts of computing resources and are time-consuming and costly. But is there an alternative method that could make wave predictions more quickly whilst at the same time ensuring roughly the same level of accuracy?c

Professor Luo Jingjia and researchers from the Climate and Applied Frontier Research Institute (ICAR) of Nanjing University of Information Science & Technology (NUIST) attempted to solve this problem based on their recent preliminary work on using artificial intelligence (AI) methods to predict ocean waves.

“By comparing several methods, our two-stage ConvLSTM model demonstrates good accuracy in predicting ocean waves,” says Professor Luo. “We also conducted real-time experiments and found that the effect of using the winds predicted by the model as a substitute was also good.”

“Next, we plan to integrate our AI model into the NUIST climate forecast system model by using the winds predicted by it as the input of the AI model to predict ocean waves, which will be more economical and time-saving than operating the ocean wave model,” adds Professor Luo.

Ioannis Kakadiaris is Hugh Roy and Lillie Cranz Cullen Distinguished University Professor of Computer Science and director of UH's Computational Biomedicine Lab.
Ioannis Kakadiaris is Hugh Roy and Lillie Cranz Cullen Distinguished University Professor of Computer Science and director of UH's Computational Biomedicine Lab.

UH prof Kakadiaris wins grant to combat food insecurity through AI

Digital platform aims to help food-insecure Texans access nutritious meals

One in eight Texans experiences food insecurity, according to the non-profit agency Feeding America. That means 1.4 million Texas households are food insecure, with limited or inconsistent access to nutritious food for an active, healthy life. The USDA's most recent survey on the issue reported that Texas is among the top nine U.S. states with a higher prevalence of food insecurity than the national average. food pantry

To address this issue, a University of Houston-led team is developing an artificial intelligence-based platform that can support the food charity ecosystem through data-driven technologies.

"The commitment of our team is to help our fellow neighbors," said Ioannis Kakadiaris, principal investigator and Hugh Roy and Lillie Cranz Cullen Distinguished University Professor of Computer Science at UH's College of Natural Sciences and Mathematics. "This is evident in everything we do and permeates all our work."

Funded by a $750,000 grant from the National Science Foundation, the project aims to help food pantries communicate with other pantries, food donors, and agencies while also helping to provide culturally aware and personalized food to clients.

On the demand side, there must be the identification of the nutritional needs, cultural preferences, and food preparation equipment and supplies of food-insecure households, according to Kakadiaris. If someone does not know what a particular food is or how to prepare it, it will go to waste, and the efforts of the food charity ecosystem will fail, he added. On the supply side, there needs to be streamlined logistics, improved communication, and coordinated efforts between the various stakeholders in the food charity system to optimize the supply chain so that inefficiencies are minimized.

The platform will potentially use food delivery services like DoorDash to transfer the food. In turn, food donors could be rewarded for their charitable donations.

"Donors could receive NFT (non-fungible tokens) that will say how good of a donor they have been in the past month," Kakadiaris said. "I envision having gold, silver, or bronze donors, depending on how much food they have donated over the past month or week."

The research team from UH includes Norma Olvera, professor of education and a USDA E. Kika de la Garza Fellow; Elizabeth Anderson-Fletcher, associate professor of supply chain management in the C. T. Bauer College of Business and Hobby School of Public Affairs; and Susie Gronseth, professor of education. From the University of Texas is Junfeng Jiao, associate professor and director of the Urban Information Lab in the School of Architecture.

"We will offer users and stakeholders healthy and culturally appropriate recipes using this platform," said Olvera.

Jiao adds that they will ensure AI is fair, safe, transparent, and accessible to all parties.

"This is a multi-disciplinary team that brings various expertise to the table," said Anderson-Fletcher. The team is partnering with Alison Reese, executive director of Souper Bowl of Caring. Souper Bowl of Caring, home of the Tackle Hunger Map, is a non-profit that uses its digital platform to fundraise for both small and large food charities across the country.

This UH project is one of sixteen projects awarded nationwide, totaling $11 million through the NSF's Convergence Accelerator program that focuses on advancing regenerative agricultural practices and providing equitable and affordable nutritious food options.

"Food and nutrition security is a new focus for the Convergence Accelerator's portfolio, and we are excited to welcome these teams into our program," said Douglas Maughan, head of the NSF Convergence Accelerator. "We hope to create a group of synergistic efforts that advance regenerative agriculture practices, reduce water usage, provide equitable access to nutritious and affordable food for disadvantaged communities, and spur technology and job creation."

Kakadiaris' team has been funded through Phase 1 of their project. The Convergence Accelerator teams will submit formal Phase 2 proposals for additional support of up to $5 million.

Faint galaxies lurking in the dark

Using the most accurate and detailed cosmological simulations available, an international team has made an exciting prediction that may shed new light on our understanding of the universe: a large population of faint galaxies in our cosmic neighborhood await discovery.

The study focuses on ultra-diffuse galaxies: faint galaxies with masses of up to one billion Suns – about one-thousandth of the mass of the Milky Way – that are spread over an area comparable to the size of our Milky Way. This makes them very faint and difficult to observe, and as a result, they remain poorly understood.

The researchers believe that the Local Group, a small cluster that currently contains approximately 60 known galaxies, including our home galaxies the Milky Way and Andromeda, holds the best prospects for further discoveries. While only two ultra-diffuse galaxies have been found in the Local Group so far, scientists believe that understanding the total number of ultra-diffuse galaxies in the Local Group is crucial to our understanding of the cosmos.

So, how many more lurk in our cosmic backyard? To find out, the international team examined state-of-the-art supercomputer simulations of our cosmic neighborhood. Named after the ancient Greek goddess of the home, the HESTIA simulations are the most accurate and detailed simulations of the Milky Way and its immediate neighborhood in existence. The simulations predict that there may be as many as 12 ultra-diffuse galaxies waiting to be discovered in the Local Group. Based on an analysis of the ultra-diffuse galaxies’ properties in the HESTIA simulations, the team believes several of these galaxies could be directly observable using existing data from surveys such as the Sloan Digital Sky Survey.

The discovery of these new galaxies could have far-reaching implications for our understanding of galaxy formation and evolution. Current models suggest that up to half of the low-mass galaxies in the universe could be extended and diffuse, and most of them will be unobservable with our current technological capabilities. As the number of galaxies in the universe is a strong prediction of various cosmological models, the size of the population of ultra-diffuse galaxies in the Local Group could be used to rule out some of these models.