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    An AI dataset carves new paths to tornado detection

    The return of spring in the Northern Hemisphere touches off tornado season. A tornado’s twisting funnel of dust and debris seems an unmistakable sight. But that sight can be obscured to radar, the tool of meteorologists. It’s hard to know exactly when a tornado has formed, or even why.

    A new dataset could hold answers. It contains radar returns from thousands of tornadoes that have hit the United States in the past 10 years. Storms that spawned tornadoes are flanked by other severe storms, some with nearly identical conditions, that never did. MIT Lincoln Laboratory researchers who curated the dataset, called TorNet, have now released it open source. They hope to enable breakthroughs in detecting one of nature’s most mysterious and violent phenomena.

    “A lot of progress is driven by easily available, benchmark datasets. We hope TorNet will lay a foundation for machine learning algorithms to both detect and predict tornadoes,” says Mark Veillette, the project’s co-principal investigator with James Kurdzo. Both researchers work in the Air Traffic Control Systems Group. 

    Along with the dataset, the team is releasing models trained on it. The models show promise for machine learning’s ability to spot a twister. Building on this work could open new frontiers for forecasters, helping them provide more accurate warnings that might save lives. 

    Swirling uncertainty

    About 1,200 tornadoes occur in the United States every year, causing millions to billions of dollars in economic damage and claiming 71 lives on average. Last year, one unusually long-lasting tornado killed 17 people and injured at least 165 others along a 59-mile path in Mississippi.  

    Yet tornadoes are notoriously difficult to forecast because scientists don’t have a clear picture of why they form. “We can see two storms that look identical, and one will produce a tornado and one won’t. We don’t fully understand it,” Kurdzo says.

    A tornado’s basic ingredients are thunderstorms with instability caused by rapidly rising warm air and wind shear that causes rotation. Weather radar is the primary tool used to monitor these conditions. But tornadoes lay too low to be detected, even when moderately close to the radar. As the radar beam with a given tilt angle travels further from the antenna, it gets higher above the ground, mostly seeing reflections from rain and hail carried in the “mesocyclone,” the storm’s broad, rotating updraft. A mesocyclone doesn’t always produce a tornado.

    With this limited view, forecasters must decide whether or not to issue a tornado warning. They often err on the side of caution. As a result, the rate of false alarms for tornado warnings is more than 70 percent. “That can lead to boy-who-cried-wolf syndrome,” Kurdzo says.  

    In recent years, researchers have turned to machine learning to better detect and predict tornadoes. However, raw datasets and models have not always been accessible to the broader community, stifling progress. TorNet is filling this gap.

    The dataset contains more than 200,000 radar images, 13,587 of which depict tornadoes. The rest of the images are non-tornadic, taken from storms in one of two categories: randomly selected severe storms or false-alarm storms (those that led a forecaster to issue a warning but that didn’t produce a tornado).

    Each sample of a storm or tornado comprises two sets of six radar images. The two sets correspond to different radar sweep angles. The six images portray different radar data products, such as reflectivity (showing precipitation intensity) or radial velocity (indicating if winds are moving toward or away from the radar).

    A challenge in curating the dataset was first finding tornadoes. Within the corpus of weather radar data, tornadoes are extremely rare events. The team then had to balance those tornado samples with difficult non-tornado samples. If the dataset were too easy, say by comparing tornadoes to snowstorms, an algorithm trained on the data would likely over-classify storms as tornadic.

    “What’s beautiful about a true benchmark dataset is that we’re all working with the same data, with the same level of difficulty, and can compare results,” Veillette says. “It also makes meteorology more accessible to data scientists, and vice versa. It becomes easier for these two parties to work on a common problem.”

    Both researchers represent the progress that can come from cross-collaboration. Veillette is a mathematician and algorithm developer who has long been fascinated by tornadoes. Kurdzo is a meteorologist by training and a signal processing expert. In grad school, he chased tornadoes with custom-built mobile radars, collecting data to analyze in new ways.

    “This dataset also means that a grad student doesn’t have to spend a year or two building a dataset. They can jump right into their research,” Kurdzo says.

    This project was funded by Lincoln Laboratory’s Climate Change Initiative, which aims to leverage the laboratory’s diverse technical strengths to help address climate problems threatening human health and global security.

    Chasing answers with deep learning

    Using the dataset, the researchers developed baseline artificial intelligence (AI) models. They were particularly eager to apply deep learning, a form of machine learning that excels at processing visual data. On its own, deep learning can extract features (key observations that an algorithm uses to make a decision) from images across a dataset. Other machine learning approaches require humans to first manually label features. 

    “We wanted to see if deep learning could rediscover what people normally look for in tornadoes and even identify new things that typically aren’t searched for by forecasters,” Veillette says.

    The results are promising. Their deep learning model performed similar to or better than all tornado-detecting algorithms known in literature. The trained algorithm correctly classified 50 percent of weaker EF-1 tornadoes and over 85 percent of tornadoes rated EF-2 or higher, which make up the most devastating and costly occurrences of these storms.

    They also evaluated two other types of machine-learning models, and one traditional model to compare against. The source code and parameters of all these models are freely available. The models and dataset are also described in a paper submitted to a journal of the American Meteorological Society (AMS). Veillette presented this work at the AMS Annual Meeting in January.

    “The biggest reason for putting our models out there is for the community to improve upon them and do other great things,” Kurdzo says. “The best solution could be a deep learning model, or someone might find that a non-deep learning model is actually better.”

    TorNet could be useful in the weather community for others uses too, such as for conducting large-scale case studies on storms. It could also be augmented with other data sources, like satellite imagery or lightning maps. Fusing multiple types of data could improve the accuracy of machine learning models.

    Taking steps toward operations

    On top of detecting tornadoes, Kurdzo hopes that models might help unravel the science of why they form.

    “As scientists, we see all these precursors to tornadoes — an increase in low-level rotation, a hook echo in reflectivity data, specific differential phase (KDP) foot and differential reflectivity (ZDR) arcs. But how do they all go together? And are there physical manifestations we don’t know about?” he asks.

    Teasing out those answers might be possible with explainable AI. Explainable AI refers to methods that allow a model to provide its reasoning, in a format understandable to humans, of why it came to a certain decision. In this case, these explanations might reveal physical processes that happen before tornadoes. This knowledge could help train forecasters, and models, to recognize the signs sooner. 

    “None of this technology is ever meant to replace a forecaster. But perhaps someday it could guide forecasters’ eyes in complex situations, and give a visual warning to an area predicted to have tornadic activity,” Kurdzo says.

    Such assistance could be especially useful as radar technology improves and future networks potentially grow denser. Data refresh rates in a next-generation radar network are expected to increase from every five minutes to approximately one minute, perhaps faster than forecasters can interpret the new information. Because deep learning can process huge amounts of data quickly, it could be well-suited for monitoring radar returns in real time, alongside humans. Tornadoes can form and disappear in minutes.

    But the path to an operational algorithm is a long road, especially in safety-critical situations, Veillette says. “I think the forecaster community is still, understandably, skeptical of machine learning. One way to establish trust and transparency is to have public benchmark datasets like this one. It’s a first step.”

    The next steps, the team hopes, will be taken by researchers across the world who are inspired by the dataset and energized to build their own algorithms. Those algorithms will in turn go into test beds, where they’ll eventually be shown to forecasters, to start a process of transitioning into operations.

    In the end, the path could circle back to trust.

    “We may never get more than a 10- to 15-minute tornado warning using these tools. But if we could lower the false-alarm rate, we could start to make headway with public perception,” Kurdzo says. “People are going to use those warnings to take the action they need to save their lives.” More

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    Using deep learning to image the Earth’s planetary boundary layer

    Although the troposphere is often thought of as the closest layer of the atmosphere to the Earth’s surface, the planetary boundary layer (PBL) — the lowest layer of the troposphere — is actually the part that most significantly influences weather near the surface. In the 2018 planetary science decadal survey, the PBL was raised as an important scientific issue that has the potential to enhance storm forecasting and improve climate projections.  

    “The PBL is where the surface interacts with the atmosphere, including exchanges of moisture and heat that help lead to severe weather and a changing climate,” says Adam Milstein, a technical staff member in Lincoln Laboratory’s Applied Space Systems Group. “The PBL is also where humans live, and the turbulent movement of aerosols throughout the PBL is important for air quality that influences human health.” 

    Although vital for studying weather and climate, important features of the PBL, such as its height, are difficult to resolve with current technology. In the past four years, Lincoln Laboratory staff have been studying the PBL, focusing on two different tasks: using machine learning to make 3D-scanned profiles of the atmosphere, and resolving the vertical structure of the atmosphere more clearly in order to better predict droughts.  

    This PBL-focused research effort builds on more than a decade of related work on fast, operational neural network algorithms developed by Lincoln Laboratory for NASA missions. These missions include the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission as well as Aqua, a satellite that collects data about Earth’s water cycle and observes variables such as ocean temperature, precipitation, and water vapor in the atmosphere. These algorithms retrieve temperature and humidity from the satellite instrument data and have been shown to significantly improve the accuracy and usable global coverage of the observations over previous approaches. For TROPICS, the algorithms help retrieve data that are used to characterize a storm’s rapidly evolving structures in near-real time, and for Aqua, it has helped increase forecasting models, drought monitoring, and fire prediction. 

    These operational algorithms for TROPICS and Aqua are based on classic “shallow” neural networks to maximize speed and simplicity, creating a one-dimensional vertical profile for each spectral measurement collected by the instrument over each location. While this approach has improved observations of the atmosphere down to the surface overall, including the PBL, laboratory staff determined that newer “deep” learning techniques that treat the atmosphere over a region of interest as a three-dimensional image are needed to improve PBL details further.

    “We hypothesized that deep learning and artificial intelligence (AI) techniques could improve on current approaches by incorporating a better statistical representation of 3D temperature and humidity imagery of the atmosphere into the solutions,” Milstein says. “But it took a while to figure out how to create the best dataset — a mix of real and simulated data; we needed to prepare to train these techniques.”

    The team collaborated with Joseph Santanello of the NASA Goddard Space Flight Center and William Blackwell, also of the Applied Space Systems Group, in a recent NASA-funded effort showing that these retrieval algorithms can improve PBL detail, including more accurate determination of the PBL height than the previous state of the art. 

    While improved knowledge of the PBL is broadly useful for increasing understanding of climate and weather, one key application is prediction of droughts. According to a Global Drought Snapshot report released last year, droughts are a pressing planetary issue that the global community needs to address. Lack of humidity near the surface, specifically at the level of the PBL, is the leading indicator of drought. While previous studies using remote-sensing techniques have examined the humidity of soil to determine drought risk, studying the atmosphere can help predict when droughts will happen.  

    In an effort funded by Lincoln Laboratory’s Climate Change Initiative, Milstein, along with laboratory staff member Michael Pieper, are working with scientists at NASA’s Jet Propulsion Laboratory (JPL) to use neural network techniques to improve drought prediction over the continental United States. While the work builds off of existing operational work JPL has done incorporating (in part) the laboratory’s operational “shallow” neural network approach for Aqua, the team believes that this work and the PBL-focused deep learning research work can be combined to further improve the accuracy of drought prediction. 

    “Lincoln Laboratory has been working with NASA for more than a decade on neural network algorithms for estimating temperature and humidity in the atmosphere from space-borne infrared and microwave instruments, including those on the Aqua spacecraft,” Milstein says. “Over that time, we have learned a lot about this problem by working with the science community, including learning about what scientific challenges remain. Our long experience working on this type of remote sensing with NASA scientists, as well as our experience with using neural network techniques, gave us a unique perspective.”

    According to Milstein, the next step for this project is to compare the deep learning results to datasets from the National Oceanic and Atmospheric Administration, NASA, and the Department of Energy collected directly in the PBL using radiosondes, a type of instrument flown on a weather balloon. “These direct measurements can be considered a kind of ‘ground truth’ to quantify the accuracy of the techniques we have developed,” Milstein says.

    This improved neural network approach holds promise to demonstrate drought prediction that can exceed the capabilities of existing indicators, Milstein says, and to be a tool that scientists can rely on for decades to come. More

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    Study: Global deforestation leads to more mercury pollution

    About 10 percent of human-made mercury emissions into the atmosphere each year are the result of global deforestation, according to a new MIT study.

    The world’s vegetation, from the Amazon rainforest to the savannahs of sub-Saharan Africa, acts as a sink that removes the toxic pollutant from the air. However, if the current rate of deforestation remains unchanged or accelerates, the researchers estimate that net mercury emissions will keep increasing.

    “We’ve been overlooking a significant source of mercury, especially in tropical regions,” says Ari Feinberg, a former postdoc in the Institute for Data, Systems, and Society (IDSS) and lead author of the study.

    The researchers’ model shows that the Amazon rainforest plays a particularly important role as a mercury sink, contributing about 30 percent of the global land sink. Curbing Amazon deforestation could thus have a substantial impact on reducing mercury pollution.

    The team also estimates that global reforestation efforts could increase annual mercury uptake by about 5 percent. While this is significant, the researchers emphasize that reforestation alone should not be a substitute for worldwide pollution control efforts.

    “Countries have put a lot of effort into reducing mercury emissions, especially northern industrialized countries, and for very good reason. But 10 percent of the global anthropogenic source is substantial, and there is a potential for that to be even greater in the future. [Addressing these deforestation-related emissions] needs to be part of the solution,” says senior author Noelle Selin, a professor in IDSS and MIT’s Department of Earth, Atmospheric and Planetary Sciences.

    Feinberg and Selin are joined on the paper by co-authors Martin Jiskra, a former Swiss National Science Foundation Ambizione Fellow at the University of Basel; Pasquale Borrelli, a professor at Roma Tre University in Italy; and Jagannath Biswakarma, a postdoc at the Swiss Federal Institute of Aquatic Science and Technology. The paper appears today in Environmental Science and Technology.

    Modeling mercury

    Over the past few decades, scientists have generally focused on studying deforestation as a source of global carbon dioxide emissions. Mercury, a trace element, hasn’t received the same attention, partly because the terrestrial biosphere’s role in the global mercury cycle has only recently been better quantified.

    Plant leaves take up mercury from the atmosphere, in a similar way as they take up carbon dioxide. But unlike carbon dioxide, mercury doesn’t play an essential biological function for plants. Mercury largely stays within a leaf until it falls to the forest floor, where the mercury is absorbed by the soil.

    Mercury becomes a serious concern for humans if it ends up in water bodies, where it can become methylated by microorganisms. Methylmercury, a potent neurotoxin, can be taken up by fish and bioaccumulated through the food chain. This can lead to risky levels of methylmercury in the fish humans eat.

    “In soils, mercury is much more tightly bound than it would be if it were deposited in the ocean. The forests are doing a sort of ecosystem service, in that they are sequestering mercury for longer timescales,” says Feinberg, who is now a postdoc in the Blas Cabrera Institute of Physical Chemistry in Spain.

    In this way, forests reduce the amount of toxic methylmercury in oceans.

    Many studies of mercury focus on industrial sources, like burning fossil fuels, small-scale gold mining, and metal smelting. A global treaty, the 2013 Minamata Convention, calls on nations to reduce human-made emissions. However, it doesn’t directly consider impacts of deforestation.

    The researchers launched their study to fill in that missing piece.

    In past work, they had built a model to probe the role vegetation plays in mercury uptake. Using a series of land use change scenarios, they adjusted the model to quantify the role of deforestation.

    Evaluating emissions

    This chemical transport model tracks mercury from its emissions sources to where it is chemically transformed in the atmosphere and then ultimately to where it is deposited, mainly through rainfall or uptake into forest ecosystems.

    They divided the Earth into eight regions and performed simulations to calculate deforestation emissions factors for each, considering elements like type and density of vegetation, mercury content in soils, and historical land use.

    However, good data for some regions were hard to come by.

    They lacked measurements from tropical Africa or Southeast Asia — two areas that experience heavy deforestation. To get around this gap, they used simpler, offline models to simulate hundreds of scenarios, which helped them improve their estimations of potential uncertainties.

    They also developed a new formulation for mercury emissions from soil. This formulation captures the fact that deforestation reduces leaf area, which increases the amount of sunlight that hits the ground and accelerates the outgassing of mercury from soils.

    The model divides the world into grid squares, each of which is a few hundred square kilometers. By changing land surface and vegetation parameters in certain squares to represent deforestation and reforestation scenarios, the researchers can capture impacts on the mercury cycle.

    Overall, they found that about 200 tons of mercury are emitted to the atmosphere as the result of deforestation, or about 10 percent of total human-made emissions. But in tropical and sub-tropical countries, deforestation emissions represent a higher percentage of total emissions. For example, in Brazil deforestation emissions are 40 percent of total human-made emissions.

    In addition, people often light fires to prepare tropical forested areas for agricultural activities, which causes more emissions by releasing mercury stored by vegetation.

    “If deforestation was a country, it would be the second highest emitting country, after China, which emits around 500 tons of mercury a year,” Feinberg adds.

    And since the Minamata Convention is now addressing primary mercury emissions, scientists can expect deforestation to become a larger fraction of human-made emissions in the future.

    “Policies to protect forests or cut them down have unintended effects beyond their target. It is important to consider the fact that these are systems, and they involve human activities, and we need to understand them better in order to actually solve the problems that we know are out there,” Selin says.

    By providing this first estimate, the team hopes to inspire more research in this area.

    In the future, they want to incorporate more dynamic Earth system models into their analysis, which would enable them to interactively track mercury uptake and better model the timescale of vegetation regrowth.

    “This paper represents an important advance in our understanding of global mercury cycling by quantifying a pathway that has long been suggested but not yet quantified. Much of our research to date has focused on primary anthropogenic emissions — those directly resulting from human activity via coal combustion or mercury-gold amalgam burning in artisanal and small-scale gold mining,” says Jackie Gerson, an assistant professor in the Department of Earth and Environmental Sciences at Michigan State University, who was not involved with this research. “This research shows that deforestation can also result in substantial mercury emissions and needs to be considered both in terms of global mercury models and land management policies. It therefore has the potential to advance our field scientifically as well as to promote policies that reduce mercury emissions via deforestation.

    This work was funded, in part, by the U.S. National Science Foundation, the Swiss National Science Foundation, and Swiss Federal Institute of Aquatic Science and Technology. More

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    Co-creating climate futures with real-time data and spatial storytelling

    Virtual story worlds and game engines aren’t just for video games anymore. They are now tools for scientists and storytellers to digitally twin existing physical spaces and then turn them into vessels to dream up speculative climate stories and build collective designs of the future. That’s the theory and practice behind the MIT WORLDING initiative.

    Twice this year, WORLDING matched world-class climate story teams working in XR (extended reality) with relevant labs and researchers across MIT. One global group returned for a virtual gathering online in partnership with Unity for Humanity, while another met for one weekend in person, hosted at the MIT Media Lab.

    “We are witnessing the birth of an emergent field that fuses climate science, urban planning, real-time 3D engines, nonfiction storytelling, and speculative fiction, and it is all fueled by the urgency of the climate crises,” says Katerina Cizek, lead designer of the WORLDING initiative at the Co-Creation Studio of MIT Open Documentary Lab. “Interdisciplinary teams are forming and blossoming around the planet to collectively imagine and tell stories of healthy, livable worlds in virtual 3D spaces and then finding direct ways to translate that back to earth, literally.”

    At this year’s virtual version of WORLDING, five multidisciplinary teams were selected from an open call. In a week-long series of research and development gatherings, the teams met with MIT scientists, staff, fellows, students, and graduates, as well as other leading figures in the field. Guests ranged from curators at film festivals such as Sundance and Venice, climate policy specialists, and award-winning media creators to software engineers and renowned Earth and atmosphere scientists. The teams heard from MIT scholars in diverse domains, including geomorphology, urban planning as acts of democracy, and climate researchers at MIT Media Lab.

    Mapping climate data

    “We are measuring the Earth’s environment in increasingly data-driven ways. Hundreds of terabytes of data are taken every day about our planet in order to study the Earth as a holistic system, so we can address key questions about global climate change,” explains Rachel Connolly, an MIT Media Lab research scientist focused in the “Future Worlds” research theme, in a talk to the group. “Why is this important for your work and storytelling in general? Having the capacity to understand and leverage this data is critical for those who wish to design for and successfully operate in the dynamic Earth environment.”

    Making sense of billions of data points was a key theme during this year’s sessions. In another talk, Taylor Perron, an MIT professor of Earth, atmospheric and planetary sciences, shared how his team uses computational modeling combined with many other scientific processes to better understand how geology, climate, and life intertwine to shape the surfaces of Earth and other planets. His work resonated with one WORLDING team in particular, one aiming to digitally reconstruct the pre-Hispanic Lake Texcoco — where current day Mexico City is now situated — as a way to contrast and examine the region’s current water crisis.

    Democratizing the future

    While WORLDING approaches rely on rigorous science and the interrogation of large datasets, they are also founded on democratizing community-led approaches.

    MIT Department of Urban Studies and Planning graduate Lafayette Cruise MCP ’19 met with the teams to discuss how he moved his own practice as a trained urban planner to include a futurist component involving participatory methods. “I felt we were asking the same limited questions in regards to the future we were wanting to produce. We’re very limited, very constrained, as to whose values and comforts are being centered. There are so many possibilities for how the future could be.”

    Scaling to reach billions

    This work scales from the very local to massive global populations. Climate policymakers are concerned with reaching billions of people in the line of fire. “We have a goal to reach 1 billion people with climate resilience solutions,” says Nidhi Upadhyaya, deputy director at Atlantic Council’s Adrienne Arsht-Rockefeller Foundation Resilience Center. To get that reach, Upadhyaya is turning to games. “There are 3.3 billion-plus people playing video games across the world. Half of these players are women. This industry is worth $300 billion. Africa is currently among the fastest-growing gaming markets in the world, and 55 percent of the global players are in the Asia Pacific region.” She reminded the group that this conversation is about policy and how formats of mass communication can be used for policymaking, bringing about change, changing behavior, and creating empathy within audiences.

    Socially engaged game development is also connected to education at Unity Technologies, a game engine company. “We brought together our education and social impact work because we really see it as a critical flywheel for our business,” said Jessica Lindl, vice president and global head of social impact/education at Unity Technologies, in the opening talk of WORLDING. “We upscale about 900,000 students, in university and high school programs around the world, and about 800,000 adults who are actively learning and reskilling and upskilling in Unity. Ultimately resulting in our mission of the ‘world is a better place with more creators in it,’ millions of creators who reach billions of consumers — telling the world stories, and fostering a more inclusive, sustainable, and equitable world.”

    Access to these technologies is key, especially the hardware. “Accessibility has been missing in XR,” explains Reginé Gilbert, who studies and teaches accessibility and disability in user experience design at New York University. “XR is being used in artificial intelligence, assistive technology, business, retail, communications, education, empathy, entertainment, recreation, events, gaming, health, rehabilitation meetings, navigation, therapy, training, video programming, virtual assistance wayfinding, and so many other uses. This is a fun fact for folks: 97.8 percent of the world hasn’t tried VR [virtual reality] yet, actually.”

    Meanwhile, new hardware is on its way. The WORLDING group got early insights into the highly anticipated Apple Vision Pro headset, which promises to integrate many forms of XR and personal computing in one device. “They’re really pushing this kind of pass-through or mixed reality,” said Dan Miller, a Unity engineer on the poly spatial team, collaborating with Apple, who described the experience of the device as “You are viewing the real world. You’re pulling up windows, you’re interacting with content. It’s a kind of spatial computing device where you have multiple apps open, whether it’s your email client next to your messaging client with a 3D game in the middle. You’re interacting with all these things in the same space and at different times.”

    “WORLDING combines our passion for social-impact storytelling and incredible innovative storytelling,” said Paisley Smith of the Unity for Humanity Program at Unity Technologies. She added, “This is an opportunity for creators to incubate their game-changing projects and connect with experts across climate, story, and technology.”

    Meeting at MIT

    In a new in-person iteration of WORLDING this year, organizers collaborated closely with Connolly at the MIT Media Lab to co-design an in-person weekend conference Oct. 25 – Nov. 7 with 45 scholars and professionals who visualize climate data at NASA, the National Oceanic and Atmospheric Administration, planetariums, and museums across the United States.

    A participant said of the event, “An incredible workshop that had had a profound effect on my understanding of climate data storytelling and how to combine different components together for a more [holistic] solution.”

    “With this gathering under our new Future Worlds banner,” says Dava Newman, director of the MIT Media Lab and Apollo Program Professor of Astronautics chair, “the Media Lab seeks to affect human behavior and help societies everywhere to improve life here on Earth and in worlds beyond, so that all — the sentient, natural, and cosmic — worlds may flourish.” 

    “WORLDING’s virtual-only component has been our biggest strength because it has enabled a true, international cohort to gather, build, and create together. But this year, an in-person version showed broader opportunities that spatial interactivity generates — informal Q&As, physical worksheets, and larger-scale ideation, all leading to deeper trust-building,” says WORLDING producer Srushti Kamat SM ’23.

    The future and potential of WORLDING lies in the ongoing dialogue between the virtual and physical, both in the work itself and in the format of the workshops. More

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    MIT campus goals in food, water, waste support decarbonization efforts

    With the launch of Fast Forward: MIT’s Climate Action Plan for the Decade, the Institute committed to decarbonize campus operations by 2050 — an effort that touches on every corner of MIT, from building energy use to procurement and waste. At the operational level, the plan called for establishing a set of quantitative climate impact goals in the areas of food, water, and waste to inform the campus decarbonization roadmap. After an 18-month process that engaged staff, faculty, and researchers, the goals — as well as high-level strategies to reach them — were finalized in spring 2023.

    The goal development process was managed by a team representing the areas of campus food, water, and waste, respectively, and includes Director of Campus Dining Mark Hayes and Senior Sustainability Project Manager Susy Jones (food), Director of Utilities Janine Helwig (water), Assistant Director of Campus Services Marty O’Brien, and Assistant Director of Sustainability Brain Goldberg (waste) to co-lead the efforts. The group worked together to set goals that leverage ongoing campus sustainability efforts. “It was important for us to collaborate in order to identify the strategies and goals,” explains Goldberg. “It allowed us to set goals that not only align, but build off of one another, enabling us to work more strategically.”

    In setting the goals, each team relied on data, community insight, and best practices. The co-leads are sharing their process to help others at the Institute understand the roles they can play in supporting these objectives.  

    Sustainable food systems

    The primary food impact goal aims for a 25 percent overall reduction in the greenhouse gas footprint of food purchases starting with academic year 2021-22 as a baseline, acknowledging that beef purchases make up a significant share of those emissions. Additionally, the co-leads established a goal to recover all edible food waste in dining hall and retail operations where feasible, as that reduces MIT’s waste impact and acknowledges that redistributing surplus food to feed people is critically important.

    The work to develop the food goal was uniquely challenging, as MIT works with nine different vendors — including main vendor Bon Appetit — to provide food on campus, with many vendors having their own sustainability targets. The goal-setting process began by understanding vendor strategies and leveraging their climate commitments. “A lot of this work is not about reinventing the wheel, but about gathering data,” says Hayes. “We are trying to connect the dots of what is currently happening on campus and to better understand food consumption and waste, ensuring that we area reaching these targets.”

    In identifying ways to reach and exceed these targets, Jones conducted listening sessions around campus, balancing input with industry trends, best-available science, and institutional insight from Hayes. “Before we set these goals and possible strategies, we wanted to get a grounding from the community and understand what would work on our campus,” says Jones, who recently began a joint role that bridges the Office of Sustainability and MIT Dining in part to support the goal work.

    By establishing the 25 percent reduction in the greenhouse gas footprint of food purchases across MIT residential dining menus, Jones and Hayes saw goal-setting as an opportunity to add more sustainable, local, and culturally diverse foods to the menu. “If beef is the most carbon-intensive food on the menu, this enables us to explore and expand so many recipes and menus from around the globe that incorporate alternatives,” Jones says.

    Strategies to reach the climate food goals focus on local suppliers, more plant-forward meals, food recovery, and food security. In 2019, MIT was a co-recipient of the New England Food Vision Prize provided by the Kendall Foundation to increase the amount of local food served on campus in partnership with CommonWealth Kitchen in Dorchester. While implementation of that program was put on pause due to the pandemic, work resumed this year. Currently, the prize is funding a collaborative effort to introduce falafel-like, locally manufactured fritters made from Maine-grown yellow field peas to dining halls at MIT and other university campuses, exemplifying the efforts to meet the climate impact goal, serve as a model for others, and provide demonstrable ways of strengthening the regional food system.

    “This sort of innovation is where we’re a leader,” says Hayes. “In addition to the Kendall Prize, we are looking to focus on food justice, growing our BIPOC [Black, Indigenous, and people of color] vendors, and exploring ideas such as local hydroponic and container vegetable growing companies, and how to scale these types of products into institutional settings.”

    Reduce and reuse for campus water

    The 2030 water impact goal aims to achieve a 10 percent reduction in water use compared to the 2019 baseline and to update the water reduction goal to align with the new metering program and proposed campus decarbonization plans as they evolve.

    When people think of campus water use, they may think of sprinklers, lab sinks, or personal use like drinking water and showers. And while those uses make up around 60 percent of campus water use, the Central Utilities Plant (CUP) accounts for the remaining 40 percent. “The CUP generates electricity and delivers heating and cooling to the campus through steam and chilled water — all using what amounts to a large percentage of water use on campus,” says Helwig. As such, the water goal focuses as much on reuse as reduction, with one approach being to expand water capture from campus cooling towers for reuse in CUP operations. “People often think of water use and energy separately, but they often go hand-in-hand,” Helwig explains.

    Data also play a central part in the water impact goal — that’s why a new metering program is called for in the implementation strategy. “We have access to a lot of data at MIT, but in reviewing the water data to inform the goal, we learned that it wasn’t quite where we needed it,” explains Helwig. “By ensuring we have the right meter and submeters set up, we can better set boundaries to understand where there is the potential to reduce water use.” Irrigation on campus is one such target with plans to soon release new campuswide landscaping standards that minimize water use.

    Reducing campus waste

    The waste impact goal aims to reduce campus trash by 30 percent compared to 2019 baseline totals. Additionally, the goal outlines efforts to improve the accuracy of indicators tracking campus waste; reduce the percentage of food scraps in trash and percent of recycling in trash in select locations; reduce the percentage of trash and recycling comprised of single use items; and increase the percentage of residence halls and other campus spaces where food is consumed at scale, implementing an MIT food scrap collection program.

    In setting the waste goals, Goldberg and O’Brien studied available campus waste data from past waste audits, pilot programs, and MIT’s waste haulers. They factored in state and city policies that regulate things like the type and amount of waste large institutions can transport. “Looking at all the data it became clear that a 30 percent trash reduction goal will make a tremendous impact on campus and help us drive toward the goal of completely designing out waste from campus,” Goldberg says. The strategies to reach the goals include reducing the amount of materials that come into campus, increasing recycling rates, and expanding food waste collection on campus.

    While reducing the waste created from material sources is outlined in the goals, food waste is a special focus on campus because it comprises approximately 40 percent of campus trash, it can be easily collected separately from trash and recycled locally, and decomposing food waste is one of the largest sources of greenhouse gas emissions found in landfills. “There is a lot of greenhouse gas emissions that result from production, distribution, transportation, packaging, processing, and disposal of food,” explains Goldberg. “When food travels to campus, is removed from campus as waste, and then breaks down in a landfill, there are emissions every step of the way.”

    To reduce food waste, Goldberg and O’Brien outlined strategies that include working with campus suppliers to identify ordering volumes and practices to limit waste. Once materials are on campus, another strategy kicks in, with a new third stream of waste collection that joins recycling and trash — food waste. By collecting the food waste separately — in bins that are currently rolling out across campus — the waste can be reprocessed into fertilizer, compost, and/or energy without the off-product of greenhouse gases. The waste impact goal also relies on behavioral changes to reduce waste, with education materials part of the process to reduce waste and decontaminate reprocessing streams.

    Tracking progress

    As work toward the goals advances, community members can monitor progress in the Sustainability DataPool Material Matters and Campus Water Use dashboards, or explore the Impact Goals in depth.

    “From food to water to waste, everyone on campus interacts with these systems and can grapple with their impact either from a material they need to dispose of, to water they’re using in a lab, or leftover food from an event,” says Goldberg. “By setting these goals we as an institution can lead the way and help our campus community understand how they can play a role, plug in, and make an impact.” More

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    Forging climate connections across the Institute

    Climate change is the ultimate cross-cutting issue: Not limited to any one discipline, it ranges across science, technology, policy, culture, human behavior, and well beyond. The response to it likewise requires an all-of-MIT effort.

    Now, to strengthen such an effort, a new grant program spearheaded by the Climate Nucleus, the faculty committee charged with the oversight and implementation of Fast Forward: MIT’s Climate Action Plan for the Decade, aims to build up MIT’s climate leadership capacity while also supporting innovative scholarship on diverse climate-related topics and forging new connections across the Institute.

    Called the Fast Forward Faculty Fund (F^4 for short), the program has named its first cohort of six faculty members after issuing its inaugural call for proposals in April 2023. The cohort will come together throughout the year for climate leadership development programming and networking. The program provides financial support for graduate students who will work with the faculty members on the projects — the students will also participate in leadership-building activities — as well as $50,000 in flexible, discretionary funding to be used to support related activities. 

    “Climate change is a crisis that truly touches every single person on the planet,” says Noelle Selin, co-chair of the nucleus and interim director of the Institute for Data, Systems, and Society. “It’s therefore essential that we build capacity for every member of the MIT community to make sense of the problem and help address it. Through the Fast Forward Faculty Fund, our aim is to have a cohort of climate ambassadors who can embed climate everywhere at the Institute.”

    F^4 supports both faculty who would like to begin doing climate-related work, as well as faculty members who are interested in deepening their work on climate. The program has the core goal of developing cohorts of F^4 faculty and graduate students who, in addition to conducting their own research, will become climate leaders at MIT, proactively looking for ways to forge new climate connections across schools, departments, and disciplines.

    One of the projects, “Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies,” led by Professor Siqi Zheng of the MIT Center for Real Estate in collaboration with colleagues from the MIT Sloan School of Management, focuses on the roughly 40 percent of carbon dioxide emissions that come from the buildings and real estate sector. Zheng notes that this sector has been slow to respond to climate change, but says that is starting to change, thanks in part to the rising awareness of climate risks and new local regulations aimed at reducing emissions from buildings.

    Using a data-driven approach, the project seeks to understand the efficient and equitable market incentives, technology solutions, and public policies that are most effective at transforming the real estate industry. Johnattan Ontiveros, a graduate student in the Technology and Policy Program, is working with Zheng on the project.

    “We were thrilled at the incredible response we received from the MIT faculty to our call for proposals, which speaks volumes about the depth and breadth of interest in climate at MIT,” says Anne White, nucleus co-chair and vice provost and associate vice president for research. “This program makes good on key commitments of the Fast Forward plan, supporting cutting-edge new work by faculty and graduate students while helping to deepen the bench of climate leaders at MIT.”

    During the 2023-24 academic year, the F^4 faculty and graduate student cohorts will come together to discuss their projects, explore opportunities for collaboration, participate in climate leadership development, and think proactively about how to deepen interdisciplinary connections among MIT community members interested in climate change.

    The six inaugural F^4 awardees are:

    Professor Tristan Brown, History Section: Humanistic Approaches to the Climate Crisis  

    With this project, Brown aims to create a new community of practice around narrative-centric approaches to environmental and climate issues. Part of a broader humanities initiative at MIT, it brings together a global working group of interdisciplinary scholars, including Serguei Saavedra (Department of Civil and Environmental Engineering) and Or Porath (Tel Aviv University; Religion), collectively focused on examining the historical and present links between sacred places and biodiversity for the purposes of helping governments and nongovernmental organizations formulate better sustainability goals. Boyd Ruamcharoen, a PhD student in the History, Anthropology, and Science, Technology, and Society (HASTS) program, will work with Brown on this project.

    Professor Kerri Cahoy, departments of Aeronautics and Astronautics and Earth, Atmospheric, and Planetary Sciences (AeroAstro): Onboard Autonomous AI-driven Satellite Sensor Fusion for Coastal Region Monitoring

    The motivation for this project is the need for much better data collection from satellites, where technology can be “20 years behind,” says Cahoy. As part of this project, Cahoy will pursue research in the area of autonomous artificial intelligence-enabled rapid sensor fusion (which combines data from different sensors, such as radar and cameras) onboard satellites to improve understanding of the impacts of climate change, specifically sea-level rise and hurricanes and flooding in coastal regions. Graduate students Madeline Anderson, a PhD student in electrical engineering and computer science (EECS), and Mary Dahl, a PhD student in AeroAstro, will work with Cahoy on this project.

    Professor Priya Donti, Department of Electrical Engineering and Computer Science: Robust Reinforcement Learning for High-Renewables Power Grids 

    With renewables like wind and solar making up a growing share of electricity generation on power grids, Donti’s project focuses on improving control methods for these distributed sources of electricity. The research will aim to create a realistic representation of the characteristics of power grid operations, and eventually inform scalable operational improvements in power systems. It will “give power systems operators faith that, OK, this conceptually is good, but it also actually works on this grid,” says Donti. PhD candidate Ana Rivera from EECS is the F^4 graduate student on the project.

    Professor Jason Jackson, Department of Urban Studies and Planning (DUSP): Political Economy of the Climate Crisis: Institutions, Power and Global Governance

    This project takes a political economy approach to the climate crisis, offering a distinct lens to examine, first, the political governance challenge of mobilizing climate action and designing new institutional mechanisms to address the global and intergenerational distributional aspects of climate change; second, the economic challenge of devising new institutional approaches to equitably finance climate action; and third, the cultural challenge — and opportunity — of empowering an adaptive socio-cultural ecology through traditional knowledge and local-level social networks to achieve environmental resilience. Graduate students Chen Chu and Mrinalini Penumaka, both PhD students in DUSP, are working with Jackson on the project.

    Professor Haruko Wainwright, departments of Nuclear Science and Engineering (NSE) and Civil and Environmental Engineering: Low-cost Environmental Monitoring Network Technologies in Rural Communities for Addressing Climate Justice 

    This project will establish a community-based climate and environmental monitoring network in addition to a data visualization and analysis infrastructure in rural marginalized communities to better understand and address climate justice issues. The project team plans to work with rural communities in Alaska to install low-cost air and water quality, weather, and soil sensors. Graduate students Kay Whiteaker, an MS candidate in NSE, and Amandeep Singh, and MS candidate in System Design and Management at Sloan, are working with Wainwright on the project, as is David McGee, professor in earth, atmospheric, and planetary sciences.

    Professor Siqi Zheng, MIT Center for Real Estate and DUSP: Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies 

    See the text above for the details on this project. More

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    Improving US air quality, equitably

    Decarbonization of national economies will be key to achieving global net-zero emissions by 2050, a major stepping stone to the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius (and ideally 1.5 C), and thereby averting the worst consequences of climate change. Toward that end, the United States has pledged to reduce its greenhouse gas emissions by 50-52 percent from 2005 levels by 2030, backed by its implementation of the 2022 Inflation Reduction Act. This strategy is consistent with a 50-percent reduction in carbon dioxide (CO2) by the end of the decade.

    If U.S. federal carbon policy is successful, the nation’s overall air quality will also improve. Cutting CO2 emissions reduces atmospheric concentrations of air pollutants that lead to the formation of fine particulate matter (PM2.5), which causes more than 200,000 premature deaths in the United States each year. But an average nationwide improvement in air quality will not be felt equally; air pollution exposure disproportionately harms people of color and lower-income populations.

    How effective are current federal decarbonization policies in reducing U.S. racial and economic disparities in PM2.5 exposure, and what changes will be needed to improve their performance? To answer that question, researchers at MIT and Stanford University recently evaluated a range of policies which, like current U.S. federal carbon policies, reduce economy-wide CO2 emissions by 40-60 percent from 2005 levels by 2030. Their findings appear in an open-access article in the journal Nature Communications.

    First, they show that a carbon-pricing policy, while effective in reducing PM2.5 exposure for all racial/ethnic groups, does not significantly mitigate relative disparities in exposure. On average, the white population undergoes far less exposure than Black, Hispanic, and Asian populations. This policy does little to reduce exposure disparities because the CO2 emissions reductions that it achieves primarily occur in the coal-fired electricity sector. Other sectors, such as industry and heavy-duty diesel transportation, contribute far more PM2.5-related emissions.

    The researchers then examine thousands of different reduction options through an optimization approach to identify whether any possible combination of carbon dioxide reductions in the range of 40-60 percent can mitigate disparities. They find that that no policy scenario aligned with current U.S. carbon dioxide emissions targets is likely to significantly reduce current PM2.5 exposure disparities.

    “Policies that address only about 50 percent of CO2 emissions leave many polluting sources in place, and those that prioritize reductions for minorities tend to benefit the entire population,” says Noelle Selin, supervising author of the study and a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences. “This means that a large range of policies that reduce CO2 can improve air quality overall, but can’t address long-standing inequities in air pollution exposure.”

    So if climate policy alone cannot adequately achieve equitable air quality results, what viable options remain? The researchers suggest that more ambitious carbon policies could narrow racial and economic PM2.5 exposure disparities in the long term, but not within the next decade. To make a near-term difference, they recommend interventions designed to reduce PM2.5 emissions resulting from non-CO2 sources, ideally at the economic sector or community level.

    “Achieving improved PM2.5 exposure for populations that are disproportionately exposed across the United States will require thinking that goes beyond current CO2 policy strategies, most likely involving large-scale structural changes,” says Selin. “This could involve changes in local and regional transportation and housing planning, together with accelerated efforts towards decarbonization.” More

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    New clean air and water labs to bring together researchers, policymakers to find climate solutions

    MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) is launching the Clean Air and Water Labs, with support from Community Jameel, to generate evidence-based solutions aimed at increasing access to clean air and water.

    Led by J-PAL’s Africa, Middle East and North Africa (MENA), and South Asia regional offices, the labs will partner with government agencies to bring together researchers and policymakers in areas where impactful clean air and water solutions are most urgently needed.

    Together, the labs aim to improve clean air and water access by informing the scaling of evidence-based policies and decisions of city, state, and national governments that serve nearly 260 million people combined.

    The Clean Air and Water Labs expand the work of J-PAL’s King Climate Action Initiative, building on the foundational support of King Philanthropies, which significantly expanded J-PAL’s work at the nexus of climate change and poverty alleviation worldwide. 

    Air pollution, water scarcity and the need for evidence 

    Africa, MENA, and South Asia are on the front lines of global air and water crises. 

    “There is no time to waste investing in solutions that do not achieve their desired effects,” says Iqbal Dhaliwal, global executive director of J-PAL. “By co-generating rigorous real-world evidence with researchers, policymakers can have the information they need to dedicate resources to scaling up solutions that have been shown to be effective.”

    In India, about 75 percent of households did not have drinking water on premises in 2018. In MENA, nearly 90 percent of children live in areas facing high or extreme water stress. Across Africa, almost 400 million people lack access to safe drinking water. 

    Simultaneously, air pollution is one of the greatest threats to human health globally. In India, extraordinary levels of air pollution are shortening the average life expectancy by five years. In Africa, rising indoor and ambient air pollution contributed to 1.1 million premature deaths in 2019. 

    There is increasing urgency to find high-impact and cost-effective solutions to the worsening threats to human health and resources caused by climate change. However, data and evidence on potential solutions are limited.

    Fostering collaboration to generate policy-relevant evidence 

    The Clean Air and Water Labs will foster deep collaboration between government stakeholders, J-PAL regional offices, and researchers in the J-PAL network. 

    Through the labs, J-PAL will work with policymakers to:

    co-diagnose the most pressing air and water challenges and opportunities for policy innovation;
    expand policymakers’ access to and use of high-quality air and water data;
    co-design potential solutions informed by existing evidence;
    co-generate evidence on promising solutions through rigorous evaluation, leveraging existing and new data sources; and
    support scaling of air and water policies and programs that are found to be effective through evaluation. 
    A research and scaling fund for each lab will prioritize resources for co-generated pilot studies, randomized evaluations, and scaling projects. 

    The labs will also collaborate with C40 Cities, a global network of mayors of the world’s leading cities that are united in action to confront the climate crisis, to share policy-relevant evidence and identify opportunities for potential new connections and research opportunities within India and across Africa.

    This model aims to strengthen the use of evidence in decision-making to ensure solutions are highly effective and to guide research to answer policymakers’ most urgent questions. J-PAL Africa, MENA, and South Asia’s strong on-the-ground presence will further bridge research and policy work by anchoring activities within local contexts. 

    “Communities across the world continue to face challenges in accessing clean air and water, a threat to human safety that has only been exacerbated by the climate crisis, along with rising temperatures and other hazards,” says George Richards, director of Community Jameel. “Through our collaboration with J-PAL and C40 in creating climate policy labs embedded in city, state, and national governments in Africa and South Asia, we are committed to innovative and science-based approaches that can help hundreds of millions of people enjoy healthier lives.”

    J-PAL Africa, MENA, and South Asia will formally launch Clean Air and Water Labs with government partners over the coming months. J-PAL is housed in the MIT Department of Economics, within the School of Humanities, Arts, and Social Sciences. More