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      J-WAFS announces 2023 seed grant recipients

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      Today, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced its ninth round of seed grants to support innovative research projects at MIT. The grants are designed to fund research efforts that tackle challenges related to water and food for human use, with the ultimate goal of creating meaningful impact as the world population continues to grow and the planet undergoes significant climate and environmental changes.Ten new projects led by 15 researchers from seven different departments will be supported this year. The projects address a range of challenges by employing advanced materials, technology innovations, and new approaches to resource management. The new projects aim to remove harmful chemicals from water sources, develop monitoring and other systems to help manage various aquaculture industries, optimize water purification materials, and more.“The seed grant program is J-WAFS’ flagship grant initiative,” says J-WAFS executive director Renee J. Robins. “The funding is intended to spur groundbreaking MIT research addressing complex issues that are challenging our water and food systems. The 10 projects selected this year show great promise, and we look forward to the progress and accomplishments these talented researchers will make,” she adds.The 2023 J-WAFS seed grant researchers and their projects are:Sara Beery, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), is building the first completely automated system to estimate the size of salmon populations in the Pacific Northwest (PNW).Salmon are a keystone species in the PNW, feeding human populations for the last 7,500 years at least. However, overfishing, habitat loss, and climate change threaten extinction of salmon populations across the region. Accurate salmon counts during their seasonal migration to their natal river to spawn are essential for fisheries’ regulation and management but are limited by human capacity. Fish population monitoring is a widespread challenge in the United States and worldwide. Beery and her team are working to build a system that will provide a detailed picture of the state of salmon populations in unprecedented, spatial, and temporal resolution by combining sonar sensors and computer vision and machine learning (CVML) techniques. The sonar will capture individual fish as they swim upstream and CVML will train accurate algorithms to interpret the sonar video for detecting, tracking, and counting fish automatically while adapting to changing river conditions and fish densities.Another aquaculture project is being led by Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering, and Robert Vincent, the assistant director at MIT’s Sea Grant Program. They are working with Otto Cordero, an associate professor in the Department of Civil and Environmental Engineering, to control harmful bacteria blooms in aquaculture algae feed production.

      Aquaculture in the United States represents a $1.5 billion industry annually and helps support 1.7 million jobs, yet many American hatcheries are not able to keep up with demand. One barrier to aquaculture production is the high degree of variability in survival rates, most likely caused by a poorly controlled microbiome that leads to bacterial infections and sub-optimal feed efficiency. Triantafyllou, Vincent, and Cordero plan to monitor the microbiome composition of a shellfish hatchery in order to identify possible causing agents of mortality, as well as beneficial microbes. They hope to pair microbe data with detail phenotypic information about the animal population to generate rapid diagnostic tests and explore the potential for microbiome therapies to protect larvae and prevent future outbreaks. The researchers plan to transfer their findings and technology to the local and regional aquaculture community to ensure healthy aquaculture production that will support the expansion of the U.S. aquaculture industry.

      David Des Marais is the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering. His 2023 J-WAFS project seeks to understand plant growth responses to elevated carbon dioxide (CO2) in the atmosphere, in the hopes of identifying breeding strategies that maximize crop yield under future CO2 scenarios.Today’s crop plants experience higher atmospheric CO2 than 20 or 30 years ago. Crops such as wheat, oat, barley, and rice typically increase their growth rate and biomass when grown at experimentally elevated atmospheric CO2. This is known as the so-called “CO2 fertilization effect.” However, not all plant species respond to rising atmospheric CO2 with increased growth, and for the ones that do, increased growth doesn’t necessarily correspond to increased crop yield. Using specially built plant growth chambers that can control the concentration of CO2, Des Marais will explore how CO2 availability impacts the development of tillers (branches) in the grass species Brachypodium. He will study how gene expression controls tiller development, and whether this is affected by the growing environment. The tillering response refers to how many branches a plant produces, which sets a limit on how much grain it can yield. Therefore, optimizing the tillering response to elevated CO2 could greatly increase yield. Des Marais will also look at the complete genome sequence of Brachypodium, wheat, oat, and barley to help identify genes relevant for branch growth.Darcy McRose, an assistant professor in the Department of Civil and Environmental Engineering, is researching whether a combination of plant metabolites and soil bacteria can be used to make mineral-associated phosphorus more bioavailable.The nutrient phosphorus is essential for agricultural plant growth, but when added as a fertilizer, phosphorus sticks to the surface of soil minerals, decreasing bioavailability, limiting plant growth, and accumulating residual phosphorus. Heavily fertilized agricultural soils often harbor large reservoirs of this type of mineral-associated “legacy” phosphorus. Redox transformations are one chemical process that can liberate mineral-associated phosphorus. However, this needs to be carefully controlled, as overly mobile phosphorus can lead to runoff and pollution of natural waters. Ideally, phosphorus would be made bioavailable when plants need it and immobile when they don’t. Many plants make small metabolites called coumarins that might be able to solubilize mineral-adsorbed phosphorus and be activated and inactivated under different conditions. McRose will use laboratory experiments to determine whether a combination of plant metabolites and soil bacteria can be used as a highly efficient and tunable system for phosphorus solubilization. She also aims to develop an imaging platform to investigate exchanges of phosphorus between plants and soil microbes.Many of the 2023 seed grants will support innovative technologies to monitor, quantify, and remediate various kinds of pollutants found in water. Two of the new projects address the problem of per- and polyfluoroalkyl substances (PFAS), human-made chemicals that have recently emerged as a global health threat. Known as “forever chemicals,” PFAS are used in many manufacturing processes. These chemicals are known to cause significant health issues including cancer, and they have become pervasive in soil, dust, air, groundwater, and drinking water. Unfortunately, the physical and chemical properties of PFAS render them difficult to detect and remove.Aristide Gumyusenge, the Merton C. Assistant Professor of Materials Science and Engineering, is using metal-organic frameworks for low-cost sensing and capture of PFAS. Most metal-organic frameworks (MOFs) are synthesized as particles, which complicates their high accuracy sensing performance due to defects such as intergranular boundaries. Thin, film-based electronic devices could enable the use of MOFs for many applications, especially chemical sensing. Gumyusenge’s project aims to design test kits based on two-dimensional conductive MOF films for detecting PFAS in drinking water. In early demonstrations, Gumyusenge and his team showed that these MOF films can sense PFAS at low concentrations. They will continue to iterate using a computation-guided approach to tune sensitivity and selectivity of the kits with the goal of deploying them in real-world scenarios.Carlos Portela, the Brit (1961) and Alex (1949) d’Arbeloff Career Development Professor in the Department of Mechanical Engineering, and Ariel Furst, the Cook Career Development Professor in the Department of Chemical Engineering, are building novel architected materials to act as filters for the removal of PFAS from water. Portela and Furst will design and fabricate nanoscale materials that use activated carbon and porous polymers to create a physical adsorption system. They will engineer the materials to have tunable porosities and morphologies that can maximize interactions between contaminated water and functionalized surfaces, while providing a mechanically robust system.Rohit Karnik is a Tata Professor and interim co-department head of the Department of Mechanical Engineering. He is working on another technology, his based on microbead sensors, to rapidly measure and monitor trace contaminants in water.Water pollution from both biological and chemical contaminants contributes to an estimated 1.36 million deaths annually. Chemical contaminants include pesticides and herbicides, heavy metals like lead, and compounds used in manufacturing. These emerging contaminants can be found throughout the environment, including in water supplies. The Environmental Protection Agency (EPA) in the United States sets recommended water quality standards, but states are responsible for developing their own monitoring criteria and systems, which must be approved by the EPA every three years. However, the availability of data on regulated chemicals and on candidate pollutants is limited by current testing methods that are either insensitive or expensive and laboratory-based, requiring trained scientists and technicians. Karnik’s project proposes a simple, self-contained, portable system for monitoring trace and emerging pollutants in water, making it suitable for field studies. The concept is based on multiplexed microbead-based sensors that use thermal or gravitational actuation to generate a signal. His proposed sandwich assay, a testing format that is appealing for environmental sensing, will enable both single-use and continuous monitoring. The hope is that the bead-based assays will increase the ease and reach of detecting and quantifying trace contaminants in water for both personal and industrial scale applications.Alexander Radosevich, a professor in the Department of Chemistry, and Timothy Swager, the John D. MacArthur Professor of Chemistry, are teaming up to create rapid, cost-effective, and reliable techniques for on-site arsenic detection in water.Arsenic contamination of groundwater is a problem that affects as many as 500 million people worldwide. Arsenic poisoning can lead to a range of severe health problems from cancer to cardiovascular and neurological impacts. Both the EPA and the World Health Organization have established that 10 parts per billion is a practical threshold for arsenic in drinking water, but measuring arsenic in water at such low levels is challenging, especially in resource-limited environments where access to sensitive laboratory equipment may not be readily accessible. Radosevich and Swager plan to develop reaction-based chemical sensors that bind and extract electrons from aqueous arsenic. In this way, they will exploit the inherent reactivity of aqueous arsenic to selectively detect and quantify it. This work will establish the chemical basis for a new method of detecting trace arsenic in drinking water.Rajeev Ram is a professor in the Department of Electrical Engineering and Computer Science. His J-WAFS research will advance a robust technology for monitoring nitrogen-containing pollutants, which threaten over 15,000 bodies of water in the United States alone.Nitrogen in the form of nitrate, nitrite, ammonia, and urea can run off from agricultural fertilizer and lead to harmful algal blooms that jeopardize human health. Unfortunately, monitoring these contaminants in the environment is challenging, as sensors are difficult to maintain and expensive to deploy. Ram and his students will work to establish limits of detection for nitrate, nitrite, ammonia, and urea in environmental, industrial, and agricultural samples using swept-source Raman spectroscopy. Swept-source Raman spectroscopy is a method of detecting the presence of a chemical by using a tunable, single mode laser that illuminates a sample. This method does not require costly, high-power lasers or a spectrometer. Ram will then develop and demonstrate a portable system that is capable of achieving chemical specificity in complex, natural environments. Data generated by such a system should help regulate polluters and guide remediation.Kripa Varanasi, a professor in the Department of Mechanical Engineering, and Angela Belcher, the James Mason Crafts Professor and head of the Department of Biological Engineering, will join forces to develop an affordable water disinfection technology that selectively identifies, adsorbs, and kills “superbugs” in domestic and industrial wastewater.Recent research predicts that antibiotic-resistance bacteria (superbugs) will result in $100 trillion in health care expenses and 10 million deaths annually by 2050. The prevalence of superbugs in our water systems has increased due to corroded pipes, contamination, and climate change. Current drinking water disinfection technologies are designed to kill all types of bacteria before human consumption. However, for certain domestic and industrial applications there is a need to protect the good bacteria required for ecological processes that contribute to soil and plant health. Varanasi and Belcher will combine material, biological, process, and system engineering principles to design a sponge-based water disinfection technology that can identify and destroy harmful bacteria while leaving the good bacteria unharmed. By modifying the sponge surface with specialized nanomaterials, their approach will be able to kill superbugs faster and more efficiently. The sponge filters can be deployed under very low pressure, making them an affordable technology, especially in resource-constrained communities.In addition to the 10 seed grant projects, J-WAFS will also fund a research initiative led by Greg Sixt. Sixt is the research manager for climate and food systems at J-WAFS, and the director of the J-WAFS-led Food and Climate Systems Transformation (FACT) Alliance. His project focuses on the Lake Victoria Basin (LVB) of East Africa. The second-largest freshwater lake in the world, Lake Victoria straddles three countries (Uganda, Tanzania, and Kenya) and has a catchment area that encompasses two more (Rwanda and Burundi). Sixt will collaborate with Michael Hauser of the University of Natural Resources and Life Sciences, Vienna, and Paul Kariuki, of the Lake Victoria Basin Commission.The group will study how to adapt food systems to climate change in the Lake Victoria Basin. The basin is facing a range of climate threats that could significantly impact livelihoods and food systems in the expansive region. For example, extreme weather events like droughts and floods are negatively affecting agricultural production and freshwater resources. Across the LVB, current approaches to land and water management are unsustainable and threaten future food and water security. The Lake Victoria Basin Commission (LVBC), a specialized institution of the East African Community, wants to play a more vital role in coordinating transboundary land and water management to support transitions toward more resilient, sustainable, and equitable food systems. The primary goal of this research will be to support the LVBC’s transboundary land and water management efforts, specifically as they relate to sustainability and climate change adaptation in food systems. The research team will work with key stakeholders in Kenya, Uganda, and Tanzania to identify specific capacity needs to facilitate land and water management transitions. The two-year project will produce actionable recommendations to the LVBC. More

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      Martin Wainwright named director of the Institute for Data, Systems, and Society

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      Martin Wainwright, the Cecil H. Green Professor in MIT’s departments of Electrical Engineering and Computer Science (EECS) and Mathematics, has been named the new director of the Institute for Data, Systems, and Society (IDSS), effective July 1.

      “Martin is a widely recognized leader in statistics and machine learning — both in research and in education. In taking on this leadership role in the college, Martin will work to build up the human and institutional behavior component of IDSS, while strengthening initiatives in both policy and statistics, and collaborations within the institute, across MIT, and beyond,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I look forward to working with him and supporting his efforts in this next chapter for IDSS.”

      “Martin holds a strong belief in the value of theoretical, experimental, and computational approaches to research and in facilitating connections between them. He also places much importance in having practical, as well as academic, impact,” says Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing, department head of EECS, and the MathWorks Professor of Electrical Engineering and Computer Science. “As the new director of IDSS, he will undoubtedly bring these tenets to the role in advancing the mission of IDSS and helping to shape its future.”

      A principal investigator in the Laboratory for Information and Decision Systems and the Statistics and Data Science Center, Wainwright joined the MIT faculty in July 2022 from the University of California at Berkeley, where he held the Howard Friesen Chair with a joint appointment between the departments of Electrical Engineering and Computer Science and Statistics.

      Wainwright received his bachelor’s degree in mathematics from the University of Waterloo, Canada, and doctoral degree in electrical engineering and computer science from MIT. He has received a number of awards and recognition, including an Alfred P. Sloan Foundation Fellowship, and best paper awards from the IEEE Signal Processing Society, IEEE Communications Society, and IEEE Information Theory and Communication Societies. He has also been honored with the Medallion Lectureship and Award from the Institute of Mathematical Statistics, and the COPSS Presidents’ Award from the Joint Statistical Societies. He was a section lecturer with the International Congress of Mathematicians in 2014 and received the Blackwell Award from the Institute of Mathematical Statistics in 2017.

      He is the author of “High-dimensional Statistics: A Non-Asymptotic Viewpoint” (Cambridge University Press, 2019), and is coauthor on several books, including on graphical models and on sparse statistical modeling.

      Wainwright succeeds Munther Dahleh, the William A. Coolidge Professor in EECS, who has helmed IDSS since its founding in 2015.

      “I am grateful to Munther and thank him for his leadership of IDSS. As the founding director, he has led the creation of a remarkable new part of MIT,” says Huttenlocher. More

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      Study: Shutting down nuclear power could increase air pollution

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      Nearly 20 percent of today’s electricity in the United States comes from nuclear power. The U.S. has the largest nuclear fleet in the world, with 92 reactors scattered around the country. Many of these power plants have run for more than half a century and are approaching the end of their expected lifetimes.

      Policymakers are debating whether to retire the aging reactors or reinforce their structures to continue producing nuclear energy, which many consider a low-carbon alternative to climate-warming coal, oil, and natural gas.

      Now, MIT researchers say there’s another factor to consider in weighing the future of nuclear power: air quality. In addition to being a low carbon-emitting source, nuclear power is relatively clean in terms of the air pollution it generates. Without nuclear power, how would the pattern of air pollution shift, and who would feel its effects?

      The MIT team took on these questions in a new study appearing today in Nature Energy. They lay out a scenario in which every nuclear power plant in the country has shut down, and consider how other sources such as coal, natural gas, and renewable energy would fill the resulting energy needs throughout an entire year.

      Their analysis reveals that indeed, air pollution would increase, as coal, gas, and oil sources ramp up to compensate for nuclear power’s absence. This in itself may not be surprising, but the team has put numbers to the prediction, estimating that the increase in air pollution would have serious health effects, resulting in an additional 5,200 pollution-related deaths over a single year.

      If, however, more renewable energy sources become available to supply the energy grid, as they are expected to by the year 2030, air pollution would be curtailed, though not entirely. The team found that even under this heartier renewable scenario, there is still a slight increase in air pollution in some parts of the country, resulting in a total of 260 pollution-related deaths over one year.

      When they looked at the populations directly affected by the increased pollution, they found that Black or African American communities — a disproportionate number of whom live near fossil-fuel plants — experienced the greatest exposure.

      “This adds one more layer to the environmental health and social impacts equation when you’re thinking about nuclear shutdowns, where the conversation often focuses on local risks due to accidents and mining or long-term climate impacts,” says lead author Lyssa Freese, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS).

      “In the debate over keeping nuclear power plants open, air quality has not been a focus of that discussion,” adds study author Noelle Selin, a professor in MIT’s Institute for Data, Systems, and Society (IDSS) and EAPS. “What we found was that air pollution from fossil fuel plants is so damaging, that anything that increases it, such as a nuclear shutdown, is going to have substantial impacts, and for some people more than others.”

      The study’s MIT-affiliated co-authors also include Principal Research Scientist Sebastian Eastham and Guillaume Chossière SM ’17, PhD ’20, along with Alan Jenn of the University of California at Davis.

      Future phase-outs

      When nuclear power plants have closed in the past, fossil fuel use increased in response. In 1985, the closure of reactors in Tennessee Valley prompted a spike in coal use, while the 2012 shutdown of a plant in California led to an increase in natural gas. In Germany, where nuclear power has almost completely been phased out, coal-fired power increased initially to fill the gap.

      Noting these trends, the MIT team wondered how the U.S. energy grid would respond if nuclear power were completely phased out.

      “We wanted to think about what future changes were expected in the energy grid,” Freese says. “We knew that coal use was declining, and there was a lot of work already looking at the impact of what that would have on air quality. But no one had looked at air quality and nuclear power, which we also noticed was on the decline.”

      In the new study, the team used an energy grid dispatch model developed by Jenn to assess how the U.S. energy system would respond to a shutdown of nuclear power. The model simulates the production of every power plant in the country and runs continuously to estimate, hour by hour, the energy demands in 64 regions across the country.

      Much like the way the actual energy market operates, the model chooses to turn a plant’s production up or down based on cost: Plants producing the cheapest energy at any given time are given priority to supply the grid over more costly energy sources.

      The team fed the model available data on each plant’s changing emissions and energy costs throughout an entire year. They then ran the model under different scenarios, including: an energy grid with no nuclear power, a baseline grid similar to today’s that includes nuclear power, and a grid with no nuclear power that also incorporates the additional renewable sources that are expected to be added by 2030.

      They combined each simulation with an atmospheric chemistry model to simulate how each plant’s various emissions travel around the country and to overlay these tracks onto maps of population density. For populations in the path of pollution, they calculated the risk of premature death based on their degree of exposure.

      System response

      Play video

      Courtesy of the researchers, edited by MIT News

      Their analysis showed a clear pattern: Without nuclear power, air pollution worsened in general, mainly affecting regions in the East Coast, where nuclear power plants are mostly concentrated. Without those plants, the team observed an uptick in production from coal and gas plants, resulting in 5,200 pollution-related deaths across the country, compared to the baseline scenario.

      They also calculated that more people are also likely to die prematurely due to climate impacts from the increase in carbon dioxide emissions, as the grid compensates for nuclear power’s absence. The climate-related effects from this additional influx of carbon dioxide could lead to 160,000 additional deaths over the next century.

      “We need to be thoughtful about how we’re retiring nuclear power plants if we are trying to think about them as part of an energy system,” Freese says. “Shutting down something that doesn’t have direct emissions itself can still lead to increases in emissions, because the grid system will respond.”

      “This might mean that we need to deploy even more renewables, in order to fill the hole left by nuclear, which is essentially a zero-emissions energy source,” Selin adds. “Otherwise we will have a reduction in air quality that we weren’t necessarily counting on.”

      This study was supported, in part, by the U.S. Environmental Protection Agency. More

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      Improving health outcomes by targeting climate and air pollution simultaneously

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      Climate policies are typically designed to reduce greenhouse gas emissions that result from human activities and drive climate change. The largest source of these emissions is the combustion of fossil fuels, which increases atmospheric concentrations of ozone, fine particulate matter (PM2.5) and other air pollutants that pose public health risks. While climate policies may result in lower concentrations of health-damaging air pollutants as a “co-benefit” of reducing greenhouse gas emissions-intensive activities, they are most effective at improving health outcomes when deployed in tandem with geographically targeted air-quality regulations.

      Yet the computer models typically used to assess the likely air quality/health impacts of proposed climate/air-quality policy combinations come with drawbacks for decision-makers. Atmospheric chemistry/climate models can produce high-resolution results, but they are expensive and time-consuming to run. Integrated assessment models can produce results for far less time and money, but produce results at global and regional scales, rendering them insufficiently precise to obtain accurate assessments of air quality/health impacts at the subnational level.

      To overcome these drawbacks, a team of researchers at MIT and the University of California at Davis has developed a climate/air-quality policy assessment tool that is both computationally efficient and location-specific. Described in a new study in the journal ACS Environmental Au, the tool could enable users to obtain rapid estimates of combined policy impacts on air quality/health at more than 1,500 locations around the globe — estimates precise enough to reveal the equity implications of proposed policy combinations within a particular region.

      “The modeling approach described in this study may ultimately allow decision-makers to assess the efficacy of multiple combinations of climate and air-quality policies in reducing the health impacts of air pollution, and to design more effective policies,” says Sebastian Eastham, the study’s lead author and a principal research scientist at the MIT Joint Program on the Science and Policy of Global Change. “It may also be used to determine if a given policy combination would result in equitable health outcomes across a geographical area of interest.”

      To demonstrate the efficiency and accuracy of their policy assessment tool, the researchers showed that outcomes projected by the tool within seconds were consistent with region-specific results from detailed chemistry/climate models that took days or even months to run. While continuing to refine and develop their approaches, they are now working to embed the new tool into integrated assessment models for direct use by policymakers.

      “As decision-makers implement climate policies in the context of other sustainability challenges like air pollution, efficient modeling tools are important for assessment — and new computational techniques allow us to build faster and more accurate tools to provide credible, relevant information to a broader range of users,” says Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and supervising author of the study. “We are looking forward to further developing such approaches, and to working with stakeholders to ensure that they provide timely, targeted and useful assessments.”

      The study was funded, in part, by the U.S. Environmental Protection Agency and the Biogen Foundation. More

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      Putting clear bounds on uncertainty

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      In science and technology, there has been a long and steady drive toward improving the accuracy of measurements of all kinds, along with parallel efforts to enhance the resolution of images. An accompanying goal is to reduce the uncertainty in the estimates that can be made, and the inferences drawn, from the data (visual or otherwise) that have been collected. Yet uncertainty can never be wholly eliminated. And since we have to live with it, at least to some extent, there is much to be gained by quantifying the uncertainty as precisely as possible.

      Expressed in other terms, we’d like to know just how uncertain our uncertainty is.

      That issue was taken up in a new study, led by Swami Sankaranarayanan, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and his co-authors — Anastasios Angelopoulos and Stephen Bates of the University of California at Berkeley; Yaniv Romano of Technion, the Israel Institute of Technology; and Phillip Isola, an associate professor of electrical engineering and computer science at MIT. These researchers succeeded not only in obtaining accurate measures of uncertainty, they also found a way to display uncertainty in a manner the average person could grasp.

      Their paper, which was presented in December at the Neural Information Processing Systems Conference in New Orleans, relates to computer vision — a field of artificial intelligence that involves training computers to glean information from digital images. The focus of this research is on images that are partially smudged or corrupted (due to missing pixels), as well as on methods — computer algorithms, in particular — that are designed to uncover the part of the signal that is marred or otherwise concealed. An algorithm of this sort, Sankaranarayanan explains, “takes the blurred image as the input and gives you a clean image as the output” — a process that typically occurs in a couple of steps.

      First, there is an encoder, a kind of neural network specifically trained by the researchers for the task of de-blurring fuzzy images. The encoder takes a distorted image and, from that, creates an abstract (or “latent”) representation of a clean image in a form — consisting of a list of numbers — that is intelligible to a computer but would not make sense to most humans. The next step is a decoder, of which there are a couple of types, that are again usually neural networks. Sankaranarayanan and his colleagues worked with a kind of decoder called a “generative” model. In particular, they used an off-the-shelf version called StyleGAN, which takes the numbers from the encoded representation (of a cat, for instance) as its input and then constructs a complete, cleaned-up image (of that particular cat). So the entire process, including the encoding and decoding stages, yields a crisp picture from an originally muddied rendering.

      But how much faith can someone place in the accuracy of the resultant image? And, as addressed in the December 2022 paper, what is the best way to represent the uncertainty in that image? The standard approach is to create a “saliency map,” which ascribes a probability value — somewhere between 0 and 1 — to indicate the confidence the model has in the correctness of every pixel, taken one at a time. This strategy has a drawback, according to Sankaranarayanan, “because the prediction is performed independently for each pixel. But meaningful objects occur within groups of pixels, not within an individual pixel,” he adds, which is why he and his colleagues are proposing an entirely different way of assessing uncertainty.

      Their approach is centered around the “semantic attributes” of an image — groups of pixels that, when taken together, have meaning, making up a human face, for example, or a dog, or some other recognizable thing. The objective, Sankaranarayanan maintains, “is to estimate uncertainty in a way that relates to the groupings of pixels that humans can readily interpret.”

      Whereas the standard method might yield a single image, constituting the “best guess” as to what the true picture should be, the uncertainty in that representation is normally hard to discern. The new paper argues that for use in the real world, uncertainty should be presented in a way that holds meaning for people who are not experts in machine learning. Rather than producing a single image, the authors have devised a procedure for generating a range of images — each of which might be correct. Moreover, they can set precise bounds on the range, or interval, and provide a probabilistic guarantee that the true depiction lies somewhere within that range. A narrower range can be provided if the user is comfortable with, say, 90 percent certitude, and a narrower range still if more risk is acceptable.

      The authors believe their paper puts forth the first algorithm, designed for a generative model, which can establish uncertainty intervals that relate to meaningful (semantically-interpretable) features of an image and come with “a formal statistical guarantee.” While that is an important milestone, Sankaranarayanan considers it merely a step toward “the ultimate goal. So far, we have been able to do this for simple things, like restoring images of human faces or animals, but we want to extend this approach into more critical domains, such as medical imaging, where our ‘statistical guarantee’ could be especially important.”

      Suppose that the film, or radiograph, of a chest X-ray is blurred, he adds, “and you want to reconstruct the image. If you are given a range of images, you want to know that the true image is contained within that range, so you are not missing anything critical” — information that might reveal whether or not a patient has lung cancer or pneumonia. In fact, Sankaranarayanan and his colleagues have already begun working with a radiologist to see if their algorithm for predicting pneumonia could be useful in a clinical setting.

      Their work may also have relevance in the law enforcement field, he says. “The picture from a surveillance camera may be blurry, and you want to enhance that. Models for doing that already exist, but it is not easy to gauge the uncertainty. And you don’t want to make a mistake in a life-or-death situation.” The tools that he and his colleagues are developing could help identify a guilty person and help exonerate an innocent one as well.

      Much of what we do and many of the things happening in the world around us are shrouded in uncertainty, Sankaranarayanan notes. Therefore, gaining a firmer grasp of that uncertainty could help us in countless ways. For one thing, it can tell us more about exactly what it is we do not know.

      Angelopoulos was supported by the National Science Foundation. Bates was supported by the Foundations of Data Science Institute and the Simons Institute. Romano was supported by the Israel Science Foundation and by a Career Advancement Fellowship from Technion. Sankaranarayanan’s and Isola’s research for this project was sponsored by the U.S. Air Force Research Laboratory and the U.S. Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2- 1000. MIT SuperCloud and the Lincoln Laboratory Supercomputing Center also provided computing resources that contributed to the results reported in this work. More

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      A healthy wind

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      Nearly 10 percent of today’s electricity in the United States comes from wind power. The renewable energy source benefits climate, air quality, and public health by displacing emissions of greenhouse gases and air pollutants that would otherwise be produced by fossil-fuel-based power plants.

      A new MIT study finds that the health benefits associated with wind power could more than quadruple if operators prioritized turning down output from the most polluting fossil-fuel-based power plants when energy from wind is available.

      In the study, published today in Science Advances, researchers analyzed the hourly activity of wind turbines, as well as the reported emissions from every fossil-fuel-based power plant in the country, between the years 2011 and 2017. They traced emissions across the country and mapped the pollutants to affected demographic populations. They then calculated the regional air quality and associated health costs to each community.

      The researchers found that in 2014, wind power that was associated with state-level policies improved air quality overall, resulting in $2 billion in health benefits across the country. However, only roughly 30 percent of these health benefits reached disadvantaged communities.

      The team further found that if the electricity industry were to reduce the output of the most polluting fossil-fuel-based power plants, rather than the most cost-saving plants, in times of wind-generated power, the overall health benefits could quadruple to $8.4 billion nationwide. However, the results would have a similar demographic breakdown.

      “We found that prioritizing health is a great way to maximize benefits in a widespread way across the U.S., which is a very positive thing. But it suggests it’s not going to address disparities,” says study co-author Noelle Selin, a professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences at MIT. “In order to address air pollution disparities, you can’t just focus on the electricity sector or renewables and count on the overall air pollution benefits addressing these real and persistent racial and ethnic disparities. You’ll need to look at other air pollution sources, as well as the underlying systemic factors that determine where plants are sited and where people live.”

      Selin’s co-authors are lead author and former MIT graduate student Minghao Qiu PhD ’21, now at Stanford University, and Corwin Zigler at the University of Texas at Austin.

      Turn-down service

      In their new study, the team looked for patterns between periods of wind power generation and the activity of fossil-fuel-based power plants, to see how regional electricity markets adjusted the output of power plants in response to influxes of renewable energy.

      “One of the technical challenges, and the contribution of this work, is trying to identify which are the power plants that respond to this increasing wind power,” Qiu notes.

      To do so, the researchers compared two historical datasets from the period between 2011 and 2017: an hour-by-hour record of energy output of wind turbines across the country, and a detailed record of emissions measurements from every fossil-fuel-based power plant in the U.S. The datasets covered each of seven major regional electricity markets, each market providing energy to one or multiple states.

      “California and New York are each their own market, whereas the New England market covers around seven states, and the Midwest covers more,” Qiu explains. “We also cover about 95 percent of all the wind power in the U.S.”

      In general, they observed that, in times when wind power was available, markets adjusted by essentially scaling back the power output of natural gas and sub-bituminous coal-fired power plants. They noted that the plants that were turned down were likely chosen for cost-saving reasons, as certain plants were less costly to turn down than others.

      The team then used a sophisticated atmospheric chemistry model to simulate the wind patterns and chemical transport of emissions across the country, and determined where and at what concentrations the emissions generated fine particulates and ozone — two pollutants that are known to damage air quality and human health. Finally, the researchers mapped the general demographic populations across the country, based on U.S. census data, and applied a standard epidemiological approach to calculate a population’s health cost as a result of their pollution exposure.

      This analysis revealed that, in the year 2014, a general cost-saving approach to displacing fossil-fuel-based energy in times of wind energy resulted in $2 billion in health benefits, or savings, across the country. A smaller share of these benefits went to disadvantaged populations, such as communities of color and low-income communities, though this disparity varied by state.

      “It’s a more complex story than we initially thought,” Qiu says. “Certain population groups are exposed to a higher level of air pollution, and those would be low-income people and racial minority groups. What we see is, developing wind power could reduce this gap in certain states but further increase it in other states, depending on which fossil-fuel plants are displaced.”

      Tweaking power

      The researchers then examined how the pattern of emissions and the associated health benefits would change if they prioritized turning down different fossil-fuel-based plants in times of wind-generated power. They tweaked the emissions data to reflect several alternative scenarios: one in which the most health-damaging, polluting power plants are turned down first; and two other scenarios in which plants producing the most sulfur dioxide and carbon dioxide respectively, are first to reduce their output.

      They found that while each scenario increased health benefits overall, and the first scenario in particular could quadruple health benefits, the original disparity persisted: Communities of color and low-income communities still experienced smaller health benefits than more well-off communities.

      “We got to the end of the road and said, there’s no way we can address this disparity by being smarter in deciding which plants to displace,” Selin says.

      Nevertheless, the study can help identify ways to improve the health of the general population, says Julian Marshall, a professor of environmental engineering at the University of Washington.

      “The detailed information provided by the scenarios in this paper can offer a roadmap to electricity-grid operators and to state air-quality regulators regarding which power plants are highly damaging to human health and also are likely to noticeably reduce emissions if wind-generated electricity increases,” says Marshall, who was not involved in the study.

      “One of the things that makes me optimistic about this area is, there’s a lot more attention to environmental justice and equity issues,” Selin concludes. “Our role is to figure out the strategies that are most impactful in addressing those challenges.”

      This work was supported, in part, by the U.S. Environmental Protection Agency, and by the National Institutes of Health. More

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      in Data Management & Statistics

      Celebrating open data

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      The inaugural MIT Prize for Open Data, which included a $2,500 cash prize, was recently awarded to 10 individual and group research projects. Presented jointly by the School of Science and the MIT Libraries, the prize recognizes MIT-affiliated researchers who make their data openly accessible and reusable by others. The prize winners and 16 honorable mention recipients were honored at the Open Data @ MIT event held Oct. 28 at Hayden Library. 

      “By making data open, researchers create opportunities for novel uses of their data and for new insights to be gleaned,” says Chris Bourg, director of MIT Libraries. “Open data accelerates scholarly progress and discovery, advances equity in scholarly participation, and increases transparency, replicability, and trust in science.” 

      Recognizing shared values

      Spearheaded by Bourg and Rebecca Saxe, associate dean of the School of Science and John W. Jarve (1978) Professor of Brain and Cognitive Sciences, the MIT Prize for Open Data was launched to highlight the value of open data at MIT and to encourage the next generation of researchers. Nominations were solicited from across the Institute, with a focus on trainees: research technicians, undergraduate or graduate students, or postdocs.

      “By launching an MIT-wide prize and event, we aimed to create visibility for the scholars who create, use, and advocate for open data,” says Saxe. “Highlighting this research and creating opportunities for networking would also help open-data advocates across campus find each other.” 

      Recognizing researchers who share data was also one of the recommendations of the Ad Hoc Task Force on Open Access to MIT’s Research, which Bourg co-chaired with Hal Abelson, Class of 1922 Professor, Department of Electrical Engineering and Computer Science. An annual award was one of the strategies put forth by the task force to further the Institute’s mission to disseminate the fruits of its research and scholarship as widely as possible.

      Strong competition

      Winners and honorable mentions were chosen from more than 70 nominees, representing all five schools, the MIT Schwarzman College of Computing, and several research centers across MIT. A committee composed of faculty, staff, and a graduate student made the selections:

      Yunsie Chung, graduate student in the Department of Chemical Engineering, won for SolProp, the largest open-source dataset with temperature-dependent solubility values of organic compounds. 
      Matthew Groh, graduate student, MIT Media Lab, accepted on behalf of the team behind the Fitzpatrick 17k dataset, an open dataset consisting of nearly 17,000 images of skin disease alongside skin disease and skin tone annotations. 
      Tom Pollard, research scientist at the Institute for Medical Engineering and Science, accepted on behalf of the PhysioNet team. This data-sharing platform enables thousands of clinical and machine-learning research studies each year and allows researchers to share sensitive resources that would not be possible through typical data sharing platforms. 
      Joseph Replogle, graduate student with the Whitehead Institute for Biomedical Research, was recognized for the Genome-wide Perturb-seq dataset, the largest publicly available, single-cell transcriptional dataset collected to date. 
      Pedro Reynolds-Cuéllar, graduate student with the MIT Media Lab/Art, Culture, and Technology, and Diana Duarte, co-founder at Diversa, won for Retos, an open-data platform for detailed documentation and sharing of local innovations from under-resourced settings. 
      Maanas Sharma, an undergraduate student, led States of Emergency, a nationwide project analyzing and grading the responses of prison systems to Covid-19 using data scraped from public databases and manually collected data. 
      Djuna von Maydell, graduate student in the Department of Brain and Cognitive Sciences, created the first publicly available dataset of single-cell gene expression from postmortem human brain tissue of patients who are carriers of APOE4, the major Alzheimer’s disease risk gene. 
      Raechel Walker, graduate researcher in the MIT Media Lab, and her collaborators created a Data Activism Curriculum for high school students through the Mayor’s Summer Youth Employment Program in Cambridge, Massachusetts. Students learned how to use data science to recognize, mitigate, and advocate for people who are disproportionately impacted by systemic inequality. 
      Suyeol Yun, graduate student in the Department of Political Science, was recognized for DeepWTO, a project creating open data for use in legal natural language processing research using cases from the World Trade Organization. 
      Jonathan Zheng, graduate student in the Department of Chemical Engineering, won for an open IUPAC dataset for acid dissociation constants, or “pKas,” physicochemical properties that govern how acidic a chemical is in a solution.
      A full list of winners and honorable mentions is available on the Open Data @ MIT website.

      A campus-wide celebration

      Awards were presented at a celebratory event held in the Nexus in Hayden Library during International Open Access Week. School of Science Dean Nergis Mavalvala kicked off the program by describing the long and proud history of open scholarship at MIT, citing the Institute-wide faculty open access policy and the launch of the open-source digital repository DSpace. “When I was a graduate student, we were trying to figure out how to share our theses during the days of the nascent internet,” she said, “With DSpace, MIT was figuring it out for us.” 

      The centerpiece of the program was a series of five-minute presentations from the prize winners on their research. Presenters detailed the ways they created, used, or advocated for open data, and the value that openness brings to their respective fields. Winner Djuna von Maydell, a graduate student in Professor Li-Huei Tsai’s lab who studies the genetic causes of neurodegeneration, underscored why it is important to share data, particularly data obtained from postmortem human brains. 

      “This is data generated from human brains, so every data point stems from a living, breathing human being, who presumably made this donation in the hope that we would use it to advance knowledge and uncover truth,” von Maydell said. “To maximize the probability of that happening, we have to make it available to the scientific community.” 

      MIT community members who would like to learn more about making their research data open can consult MIT Libraries’ Data Services team.  More

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      in Data Management & Statistics

      Methane research takes on new urgency at MIT

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      One of the most notable climate change provisions in the 2022 Inflation Reduction Act is the first U.S. federal tax on a greenhouse gas (GHG). That the fee targets methane (CH4), rather than carbon dioxide (CO2), emissions is indicative of the urgency the scientific community has placed on reducing this short-lived but powerful gas. Methane persists in the air about 12 years — compared to more than 1,000 years for CO2 — yet it immediately causes about 120 times more warming upon release. The gas is responsible for at least a quarter of today’s gross warming. 

      “Methane has a disproportionate effect on near-term warming,” says Desiree Plata, the director of MIT Methane Network. “CH4 does more damage than CO2 no matter how long you run the clock. By removing methane, we could potentially avoid critical climate tipping points.” 

      Because GHGs have a runaway effect on climate, reductions made now will have a far greater impact than the same reductions made in the future. Cutting methane emissions will slow the thawing of permafrost, which could otherwise lead to massive methane releases, as well as reduce increasing emissions from wetlands.  

      “The goal of MIT Methane Network is to reduce methane emissions by 45 percent by 2030, which would save up to 0.5 degree C of warming by 2100,” says Plata, an associate professor of civil and environmental engineering at MIT and director of the Plata Lab. “When you consider that governments are trying for a 1.5-degree reduction of all GHGs by 2100, this is a big deal.” 

      Under normal concentrations, methane, like CO2, poses no health risks. Yet methane assists in the creation of high levels of ozone. In the lower atmosphere, ozone is a key component of air pollution, which leads to “higher rates of asthma and increased emergency room visits,” says Plata. 

      Methane-related projects at the Plata Lab include a filter made of zeolite — the same clay-like material used in cat litter — designed to convert methane into CO2 at dairy farms and coal mines. At first glance, the technology would appear to be a bit of a hard sell, since it converts one GHG into another. Yet the zeolite filter’s low carbon and dollar costs, combined with the disproportionate warming impact of methane, make it a potential game-changer.

      The sense of urgency about methane has been amplified by recent studies that show humans are generating far more methane emissions than previously estimated, and that the rates are rising rapidly. Exactly how much methane is in the air is uncertain. Current methods for measuring atmospheric methane, such as ground, drone, and satellite sensors, “are not readily abundant and do not always agree with each other,” says Plata.  

      The Plata Lab is collaborating with Tim Swager in the MIT Department of Chemistry to develop low-cost methane sensors. “We are developing chemiresisitive sensors that cost about a dollar that you could place near energy infrastructure to back-calculate where leaks are coming from,” says Plata.  

      The researchers are working on improving the accuracy of the sensors using machine learning techniques and are planning to integrate internet-of-things technology to transmit alerts. Plata and Swager are not alone in focusing on data collection: the Inflation Reduction Act adds significant funding for methane sensor research. 

      Other research at the Plata Lab includes the development of nanomaterials and heterogeneous catalysis techniques for environmental applications. The lab also explores mitigation solutions for industrial waste, particularly those related to the energy transition. Plata is the co-founder of an lithium-ion battery recycling startup called Nth Cycle. 

      On a more fundamental level, the Plata Lab is exploring how to develop products with environmental and social sustainability in mind. “Our overarching mission is to change the way that we invent materials and processes so that environmental objectives are incorporated along with traditional performance and cost metrics,” says Plata. “It is important to do that rigorous assessment early in the design process.”

      Play video

      MIT amps up methane research 

      The MIT Methane Network brings together 26 researchers from MIT along with representatives of other institutions “that are dedicated to the idea that we can reduce methane levels in our lifetime,” says Plata. The organization supports research such as Plata’s zeolite and sensor projects, as well as designing pipeline-fixing robots, developing methane-based fuels for clean hydrogen, and researching the capture and conversion of methane into liquid chemical precursors for pharmaceuticals and plastics. Other members are researching policies to encourage more sustainable agriculture and land use, as well as methane-related social justice initiatives. 

      “Methane is an especially difficult problem because it comes from all over the place,” says Plata. A recent Global Carbon Project study estimated that half of methane emissions are caused by humans. This is led by waste and agriculture (28 percent), including cow and sheep belching, rice paddies, and landfills.  

      Fossil fuels represent 18 percent of the total budget. Of this, about 63 percent is derived from oil and gas production and pipelines, 33 percent from coal mining activities, and 5 percent from industry and transportation. Human-caused biomass burning, primarily from slash-and-burn agriculture, emits about 4 percent of the global total.  

      The other half of the methane budget includes natural methane emissions from wetlands (20 percent) and other natural sources (30 percent). The latter includes permafrost melting and natural biomass burning, such as forest fires started by lightning.  

      With increases in global warming and population, the line between anthropogenic and natural causes is getting fuzzier. “Human activities are accelerating natural emissions,” says Plata. “Climate change increases the release of methane from wetlands and permafrost and leads to larger forest and peat fires.”  

      The calculations can get complicated. For example, wetlands provide benefits from CO2 capture, biological diversity, and sea level rise resiliency that more than compensate for methane releases. Meanwhile, draining swamps for development increases emissions. 

      Over 100 nations have signed onto the U.N.’s Global Methane Pledge to reduce at least 30 percent of anthropogenic emissions within the next 10 years. The U.N. report estimates that this goal can be achieved using proven technologies and that about 60 percent of these reductions can be accomplished at low cost. 

      Much of the savings would come from greater efficiencies in fossil fuel extraction, processing, and delivery. The methane fees in the Inflation Reduction Act are primarily focused on encouraging fossil fuel companies to accelerate ongoing efforts to cap old wells, flare off excess emissions, and tighten pipeline connections.  

      Fossil fuel companies have already made far greater pledges to reduce methane than they have with CO2, which is central to their business. This is due, in part, to the potential savings, as well as in preparation for methane regulations expected from the Environmental Protection Agency in late 2022. The regulations build upon existing EPA oversight of drilling operations, and will likely be exempt from the U.S. Supreme Court’s ruling that limits the federal government’s ability to regulate GHGs. 

      Zeolite filter targets methane in dairy and coal 

      The “low-hanging fruit” of gas stream mitigation addresses most of the 20 percent of total methane emissions in which the gas is released in sufficiently high concentrations for flaring. Plata’s zeolite filter aims to address the thornier challenge of reducing the 80 percent of non-flammable dilute emissions. 

      Plata found inspiration in decades-old catalysis research for turning methane into methanol. One strategy has been to use an abundant, low-cost aluminosilicate clay called zeolite.  

      “The methanol creation process is challenging because you need to separate a liquid, and it has very low efficiency,” says Plata. “Yet zeolite can be very efficient at converting methane into CO2, and it is much easier because it does not require liquid separation. Converting methane to CO2 sounds like a bad thing, but there is a major anti-warming benefit. And because methane is much more dilute than CO2, the relative CO2 contribution is minuscule.”  

      Using zeolite to create methanol requires highly concentrated methane, high temperatures and pressures, and industrial processing conditions. Yet Plata’s process, which dopes the zeolite with copper, operates in the presence of oxygen at much lower temperatures under typical pressures. “We let the methane proceed the way it wants from a thermodynamic perspective from methane to methanol down to CO2,” says Plata. 

      Researchers around the world are working on other dilute methane removal technologies. Projects include spraying iron salt aerosols into sea air where they react with natural chlorine or bromine radicals, thereby capturing methane. Most of these geoengineering solutions, however, are difficult to measure and would require massive scale to make a difference.  

      Plata is focusing her zeolite filters on environments where concentrations are high, but not so high as to be flammable. “We are trying to scale zeolite into filters that you could snap onto the side of a cross-ventilation fan in a dairy barn or in a ventilation air shaft in a coal mine,” says Plata. “For every packet of air we bring in, we take a lot of methane out, so we get more bang for our buck.”  

      The major challenge is creating a filter that can handle high flow rates without getting clogged or falling apart. Dairy barn air handlers can push air at up to 5,000 cubic feet per minute and coal mine handlers can approach 500,000 CFM. 

      Plata is exploring engineering options including fluidized bed reactors with floating catalyst particles. Another filter solution, based in part on catalytic converters, features “higher-order geometric structures where you have a porous material with a long path length where the gas can interact with the catalyst,” says Plata. “This avoids the challenge with fluidized beds of containing catalyst particles in the reactor. Instead, they are fixed within a structured material.”  

      Competing technologies for removing methane from mine shafts “operate at temperatures of 1,000 to 1,200 degrees C, requiring a lot of energy and risking explosion,” says Plata. “Our technology avoids safety concerns by operating at 300 to 400 degrees C. It reduces energy use and provides more tractable deployment costs.” 

      Potentially, energy and dollar costs could be further reduced in coal mines by capturing the heat generated by the conversion process. “In coal mines, you have enrichments above a half-percent methane, but below the 4 percent flammability threshold,” says Plata. “The excess heat from the process could be used to generate electricity using off-the-shelf converters.” 

      Plata’s dairy barn research is funded by the Gerstner Family Foundation and the coal mining project by the U.S. Department of Energy. “The DOE would like us to spin out the technology for scale-up within three years,” says Plata. “We cannot guarantee we will hit that goal, but we are trying to develop this as quickly as possible. Our society needs to start reducing methane emissions now.”  More