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      in Environment

      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      in Energy

      Timber or steel? Study helps builders reduce carbon footprint of truss structures

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      Buildings are a big contributor to global warming, not just in their ongoing operations but in the materials used in their construction. Truss structures — those crisscross arrays of diagonal struts used throughout modern construction, in everything from antenna towers to support beams for large buildings — are typically made of steel or wood or a combination of both. But little quantitative research has been done on how to pick the right materials to minimize these structures’ contribution global warming.

      The “embodied carbon” in a construction material includes the fuel used in the material’s production (for mining and smelting steel, for example, or for felling and processing trees) and in transporting the materials to a site. It also includes the equipment used for the construction itself.

      Now, researchers at MIT have done a detailed analysis and created a set of computational tools to enable architects and engineers to design truss structures in a way that can minimize their embodied carbon while maintaining all needed properties for a given building application. While in general wood produces a much lower carbon footprint, using steel in places where its properties can provide maximum benefit can provide an optimized result, they say.

      The analysis is described in a paper published today in the journal Engineering Structures, by graduate student Ernest Ching and MIT assistant professor of civil and environmental engineering Josephine Carstensen.

      “Construction is a huge greenhouse gas emitter that has kind of been flying under the radar for the past decades,” says Carstensen. But in recent years building designers “are starting to be more focused on how to not just reduce the operating energy associated with building use, but also the important carbon associated with the structure itself.” And that’s where this new analysis comes in.

      The two main options in reducing the carbon emissions associated with truss structures, she says, are substituting materials or changing the structure. However, there has been “surprisingly little work” on tools to help designers figure out emissions-minimizing strategies for a given situation, she says.

      The new system makes use of a technique called topology optimization, which allows for the input of basic parameters, such as the amount of load to be supported and the dimensions of the structure, and can be used to produce designs optimized for different characteristics, such as weight, cost, or, in this case, global warming impact.

      Wood performs very well under forces of compression, but not as well as steel when it comes to tension — that is, a tendency to pull the structure apart. Carstensen says that in general, wood is far better than steel in terms of embedded carbon, so “especially if you have a structure that doesn’t have any tension, then you should definitely only use timber” in order to minimize emissions. One tradeoff is that “the weight of the structure is going to be bigger than it would be with steel,” she says.

      The tools they developed, which were the basis for Ching’s master’s thesis, can be applied at different stages, either in the early planning phase of a structure, or later on in the final stages of a design.

      As an exercise, the team developed a proposal for reengineering several trusses using these optimization tools, and demonstrated that a significant savings in embodied greenhouse gas emissions could be achieved with no loss of performance. While they have shown improvements of at least 10 percent can be achieved, she says those estimates are “not exactly apples to apples” and likely savings could actually be two to three times that.

      “It’s about choosing materials more smartly,” she says, for the specifics of a given application. Often in existing buildings “you will have timber where there’s compression, and where that makes sense, and then it will have really skinny steel members, in tension, where that makes sense. And that’s also what we see in our design solutions that are suggested, but perhaps we can see it even more clearly.” The tools are not ready for commercial use though, she says, because they haven’t yet added a user interface.

      Carstensen sees a trend to increasing use of timber in large construction, which represents an important potential for reducing the world’s overall carbon emissions. “There’s a big interest in the construction industry in mass timber structures, and this speaks right into that area. So, the hope is that this would make inroads into the construction business and actually make a dent in that very large contribution to greenhouse gas emissions.” More

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      in Environment

      New “risk triage” platform pinpoints compounding threats to US infrastructure

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      Over a 36-hour period in August, Hurricane Henri delivered record rainfall in New York City, where an aging storm-sewer system was not built to handle the deluge, resulting in street flooding. Meanwhile, an ongoing drought in California continued to overburden aquifers and extend statewide water restrictions. As climate change amplifies the frequency and intensity of extreme events in the United States and around the world, and the populations and economies they threaten grow and change, there is a critical need to make infrastructure more resilient. But how can this be done in a timely, cost-effective way?

      An emerging discipline called multi-sector dynamics (MSD) offers a promising solution. MSD homes in on compounding risks and potential tipping points across interconnected natural and human systems. Tipping points occur when these systems can no longer sustain multiple, co-evolving stresses, such as extreme events, population growth, land degradation, drinkable water shortages, air pollution, aging infrastructure, and increased human demands. MSD researchers use observations and computer models to identify key precursory indicators of such tipping points, providing decision-makers with critical information that can be applied to mitigate risks and boost resilience in infrastructure and managed resources.

      At MIT, the Joint Program on the Science and Policy of Global Change has since 2018 been developing MSD expertise and modeling tools and using them to explore compounding risks and potential tipping points in selected regions of the United States. In a two-hour webinar on Sept. 15, MIT Joint Program researchers presented an overview of the program’s MSD research tool set and its applications.  

      MSD and the risk triage platform

      “Multi-sector dynamics explores interactions and interdependencies among human and natural systems, and how these systems may adapt, interact, and co-evolve in response to short-term shocks and long-term influences and stresses,” says MIT Joint Program Deputy Director C. Adam Schlosser, noting that such analysis can reveal and quantify potential risks that would likely evade detection in siloed investigations. “These systems can experience cascading effects or failures after crossing tipping points. The real question is not just where these tipping points are in each system, but how they manifest and interact across all systems.”

      To address that question, the program’s MSD researchers have developed the MIT Socio-Environmental Triage (MST) platform, now publicly available for the first time. Focused on the continental United States, the first version of the platform analyzes present-day risks related to water, land, climate, the economy, energy, demographics, health, and infrastructure, and where these compound to create risk hot spots. It’s essentially a screening-level visualization tool that allows users to examine risks, identify hot spots when combining risks, and make decisions about how to deploy more in-depth analysis to solve complex problems at regional and local levels. For example, MST can identify hot spots for combined flood and poverty risks in the lower Mississippi River basin, and thereby alert decision-makers as to where more concentrated flood-control resources are needed.

      Successive versions of the platform will incorporate projections based on the MIT Joint Program’s Integrated Global System Modeling (IGSM) framework of how different systems and stressors may co-evolve into the future and thereby change the risk landscape. This enhanced capability could help uncover cost-effective pathways for mitigating and adapting to a wide range of environmental and economic risks.  

      MSD applications

      Five webinar presentations explored how MIT Joint Program researchers are applying the program’s risk triage platform and other MSD modeling tools to identify potential tipping points and risks in five key domains: water quality, land use, economics and energy, health, and infrastructure. 

      Joint Program Principal Research Scientist Xiang Gao described her efforts to apply a high-resolution U.S. water-quality model to calculate a location-specific, water-quality index over more than 2,000 river basins in the country. By accounting for interactions among climate, agriculture, and socioeconomic systems, various water-quality measures can be obtained ranging from nitrate and phosphate levels to phytoplankton concentrations. This modeling approach advances a unique capability to identify potential water-quality risk hot spots for freshwater resources.

      Joint Program Research Scientist Angelo Gurgel discussed his MSD-based analysis of how climate change, population growth, changing diets, crop-yield improvements and other forces that drive land-use change at the global level may ultimately impact how land is used in the United States. Drawing upon national observational data and the IGSM framework, the analysis shows that while current U.S. land-use trends are projected to persist or intensify between now and 2050, there is no evidence of any concerning tipping points arising throughout this period.  

      MIT Joint Program Research Scientist Jennifer Morris presented several examples of how the risk triage platform can be used to combine existing U.S. datasets and the IGSM framework to assess energy and economic risks at the regional level. For example, by aggregating separate data streams on fossil-fuel employment and poverty, one can target selected counties for clean energy job training programs as the nation moves toward a low-carbon future. 

      “Our modeling and risk triage frameworks can provide pictures of current and projected future economic and energy landscapes,” says Morris. “They can also highlight interactions among different human, built, and natural systems, including compounding risks that occur in the same location.”  

      MIT Joint Program research affiliate Sebastian Eastham, a research scientist at the MIT Laboratory for Aviation and the Environment, described an MSD approach to the study of air pollution and public health. Linking the IGSM with an atmospheric chemistry model, Eastham ultimately aims to better understand where the greatest health risks are in the United States and how they may compound throughout this century under different policy scenarios. Using the risk triage tool to combine current risk metrics for air quality and poverty in a selected county based on current population and air-quality data, he showed how one can rapidly identify cardiovascular and other air-pollution-induced disease risk hot spots.

      Finally, MIT Joint Program research affiliate Alyssa McCluskey, a lecturer at the University of Colorado at Boulder, showed how the risk triage tool can be used to pinpoint potential risks to roadways, waterways, and power distribution lines from flooding, extreme temperatures, population growth, and other stressors. In addition, McCluskey described how transportation and energy infrastructure development and expansion can threaten critical wildlife habitats.

      Enabling comprehensive, location-specific analyses of risks and hot spots within and among multiple domains, the Joint Program’s MSD modeling tools can be used to inform policymaking and investment from the municipal to the global level.

      “MSD takes on the challenge of linking human, natural, and infrastructure systems in order to inform risk analysis and decision-making,” says Schlosser. “Through our risk triage platform and other MSD models, we plan to assess important interactions and tipping points, and to provide foresight that supports action toward a sustainable, resilient, and prosperous world.”

      This research is funded by the U.S. Department of Energy’s Office of Science as an ongoing project. More

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      in Energy

      A new method for removing lead from drinking water

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      Engineers at MIT have developed a new approach to removing lead or other heavy-metal contaminants from water, in a process that they say is far more energy-efficient than any other currently used system, though there are others under development that come close. Ultimately, it might be used to treat lead-contaminated water supplies at the home level, or to treat contaminated water from some chemical or industrial processes.

      The new system is the latest in a series of applications based on initial findings six years ago by members of the same research team, initially developed for desalination of seawater or brackish water, and later adapted for removing radioactive compounds from the cooling water of nuclear power plants. The new version is the first such method that might be applicable for treating household water supplies, as well as industrial uses.

      The findings are published today in the journal Environmental Science and Technology – Water, in a paper by MIT graduate students Huanhuan Tian, Mohammad Alkhadra, and Kameron Conforti, and professor of chemical engineering Martin Bazant.

      “It’s notoriously difficult to remove toxic heavy metal that’s persistent and present in a lot of different water sources,” Alkhadra says. “Obviously there are competing methods today that do this function, so it’s a matter of which method can do it at lower cost and more reliably.”

      The biggest challenge in trying to remove lead is that it is generally present in such tiny concentrations, vastly exceeded by other elements or compounds. For example, sodium is typically present in drinking water at a concentration of tens of parts per million, whereas lead can be highly toxic at just a few parts per billion. Most existing processes, such as reverse osmosis or distillation, remove everything at once, Alkhadra explains. This not only takes much more energy than would be needed for a selective removal, but it’s counterproductive since small amounts of elements such as sodium and magnesium are actually essential for healthy drinking water.

      The new approach is to use a process called shock electrodialysis, in which an electric field is used to produce a shockwave inside a pipe carrying the contaminated water. The shockwave separates the liquid into two streams, selectively pulling certain electrically charged atoms, or ions, toward one side of the flow by tuning the properties of the shockwave to match the target ions, while leaving a stream of relatively pure water on the other side. The stream containing the concentrated lead ions can then be easily separated out using a mechanical barrier in the pipe.

      In principle, “this makes the process much cheaper,” Bazant says, “because the electrical energy that you’re putting in to do the separation is really going after the high-value target, which is the lead. You’re not wasting a lot of energy removing the sodium.” Because the lead is present at such low concentration, “there’s not a lot of current involved in removing those ions, so this can be a very cost-effective way.”

      The process still has its limitations, as it has only been demonstrated at small laboratory scale and at quite slow flow rates. Scaling up the process to make it practical for in-home use will require further research, and larger-scale industrial uses will take even longer. But it could be practical within a few years for some home-based systems, Bazant says.

      For example, a home whose water supply is heavily contaminated with lead might have a system in the cellar that slowly processes a stream of water, filling a tank with lead-free water to be used for drinking and cooking, while leaving most of the water untreated for uses like toilet flushing or watering the lawn. Such uses might be appropriate as an interim measure for places like Flint, Michigan, where the water, mostly contaminated by the distribution pipes, will take many years to remediate through pipe replacements.

      The process could also be adapted for some industrial uses such as cleaning water produced in mining or drilling operations, so that the treated water can be safely disposed of or reused. And in some cases, this could also provide a way of recovering metals that contaminate water but could actually be a valuable product if they were separated out; for example, some such minerals could be used to process semiconductors or pharmaceuticals or other high-tech products, the researchers say.

      Direct comparisons of the economics of such a system versus existing methods is difficult, Bazant says, because in filtration systems, for example, the costs are mainly for replacing the filter materials, which quickly clog up and become unusable, whereas in this system the costs are mostly for the ongoing energy input, which is very small. At this point, the shock electrodialysis system has been operated for several weeks, but it’s too soon to estimate the real-world longevity of such a system, he says.

      Developing the process into a scalable commercial product will take some time, but “we have shown how this could be done, from a technical standpoint,” Bazant says. “The main issue would be on the economic side,” he adds. That includes figuring out the most appropriate applications and developing specific configurations that would meet those uses. “We do have a reasonable idea of how to scale this up. So it’s a question of having the resources,” which might be a role for a startup company rather than an academic research lab, he adds.

      “I think this is an exciting result,” he says, “because it shows that we really can address this important application” of cleaning the lead from drinking water. For example, he says, there are places now that perform desalination of seawater using reverse osmosis, but they have to run this expensive process twice in a row, first to get the salt out, and then again to remove the low-level but highly toxic contaminants like lead. This new process might be used instead of the second round of reverse osmosis, at a far lower expenditure of energy.

      The research received support from a MathWorks Engineering Fellowship and a fellowship awarded by MIT’s Abdul Latif Jameel Water and Food Systems Lab, funded by Xylem, Inc. More

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      in Environment

      Study: Global cancer risk from burning organic matter comes from unregulated chemicals

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      Whenever organic matter is burned, such as in a wildfire, a power plant, a car’s exhaust, or in daily cooking, the combustion releases polycyclic aromatic hydrocarbons (PAHs) — a class of pollutants that is known to cause lung cancer.

      There are more than 100 known types of PAH compounds emitted daily into the atmosphere. Regulators, however, have historically relied on measurements of a single compound, benzo(a)pyrene, to gauge a community’s risk of developing cancer from PAH exposure. Now MIT scientists have found that benzo(a)pyrene may be a poor indicator of this type of cancer risk.

      In a modeling study appearing today in the journal GeoHealth, the team reports that benzo(a)pyrene plays a small part — about 11 percent — in the global risk of developing PAH-associated cancer. Instead, 89 percent of that cancer risk comes from other PAH compounds, many of which are not directly regulated.

      Interestingly, about 17 percent of PAH-associated cancer risk comes from “degradation products” — chemicals that are formed when emitted PAHs react in the atmosphere. Many of these degradation products can in fact be more toxic than the emitted PAH from which they formed.

      The team hopes the results will encourage scientists and regulators to look beyond benzo(a)pyrene, to consider a broader class of PAHs when assessing a community’s cancer risk.

      “Most of the regulatory science and standards for PAHs are based on benzo(a)pyrene levels. But that is a big blind spot that could lead you down a very wrong path in terms of assessing whether cancer risk is improving or not, and whether it’s relatively worse in one place than another,” says study author Noelle Selin, a professor in MIT’s Institute for Data, Systems and Society, and the Department of Earth, Atmospheric and Planetary Sciences.

      Selin’s MIT co-authors include Jesse Kroll, Amy Hrdina, Ishwar Kohale, Forest White, and Bevin Engelward, and Jamie Kelly (who is now at University College London). Peter Ivatt and Mathew Evans at the University of York are also co-authors.

      Chemical pixels

      Benzo(a)pyrene has historically been the poster chemical for PAH exposure. The compound’s indicator status is largely based on early toxicology studies. But recent research suggests the chemical may not be the PAH representative that regulators have long relied upon.   

      “There has been a bit of evidence suggesting benzo(a)pyrene may not be very important, but this was from just a few field studies,” says Kelly, a former postdoc in Selin’s group and the study’s lead author.

      Kelly and his colleagues instead took a systematic approach to evaluate benzo(a)pyrene’s suitability as a PAH indicator. The team began by using GEOS-Chem, a global, three-dimensional chemical transport model that breaks the world into individual grid boxes and simulates within each box the reactions and concentrations of chemicals in the atmosphere.

      They extended this model to include chemical descriptions of how various PAH compounds, including benzo(a)pyrene, would react in the atmosphere. The team then plugged in recent data from emissions inventories and meteorological observations, and ran the model forward to simulate the concentrations of various PAH chemicals around the world over time.

      Risky reactions

      In their simulations, the researchers started with 16 relatively well-studied PAH chemicals, including benzo(a)pyrene, and traced the concentrations of these chemicals, plus the concentration of their degradation products over two generations, or chemical transformations. In total, the team evaluated 48 PAH species.

      They then compared these concentrations with actual concentrations of the same chemicals, recorded by monitoring stations around the world. This comparison was close enough to show that the model’s concentration predictions were realistic.

      Then within each model’s grid box, the researchers related the concentration of each PAH chemical to its associated cancer risk; to do this, they had to develop a new method based on previous studies in the literature to avoid double-counting risk from the different chemicals. Finally, they overlaid population density maps to predict the number of cancer cases globally, based on the concentration and toxicity of a specific PAH chemical in each location.

      Dividing the cancer cases by population produced the cancer risk associated with that chemical. In this way, the team calculated the cancer risk for each of the 48 compounds, then determined each chemical’s individual contribution to the total risk.

      This analysis revealed that benzo(a)pyrene had a surprisingly small contribution, of about 11 percent, to the overall risk of developing cancer from PAH exposure globally. Eighty-nine percent of cancer risk came from other chemicals. And 17 percent of this risk arose from degradation products.

      “We see places where you can find concentrations of benzo(a)pyrene are lower, but the risk is higher because of these degradation products,” Selin says. “These products can be orders of magnitude more toxic, so the fact that they’re at tiny concentrations doesn’t mean you can write them off.”

      When the researchers compared calculated PAH-associated cancer risks around the world, they found significant differences depending on whether that risk calculation was based solely on concentrations of benzo(a)pyrene or on a region’s broader mix of PAH compounds.

      “If you use the old method, you would find the lifetime cancer risk is 3.5 times higher in Hong Kong versus southern India, but taking into account the differences in PAH mixtures, you get a difference of 12 times,” Kelly says. “So, there’s a big difference in the relative cancer risk between the two places. And we think it’s important to expand the group of compounds that regulators are thinking about, beyond just a single chemical.”

      The team’s study “provides an excellent contribution to better understanding these ubiquitous pollutants,” says Elisabeth Galarneau, an air quality expert and PhD research scientist in Canada’s Department of the Environment. “It will be interesting to see how these results compare to work being done elsewhere … to pin down which (compounds) need to be tracked and considered for the protection of human and environmental health.”

      This research was conducted in MIT’s Superfund Research Center and is supported in part by the National Institute of Environmental Health Sciences Superfund Basic Research Program, and the National Institutes of Health. More

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      in Energy

      Predicting building emissions across the US

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      The United States is entering a building boom. Between 2017 and 2050, it will build the equivalent of New York City 20 times over. Yet, to meet climate targets, the nation must also significantly reduce the greenhouse gas (GHG) emissions of its buildings, which comprise 27 percent of the nation’s total emissions.

      A team of current and former MIT Concrete Sustainability Hub (CSHub) researchers is addressing these conflicting demands with the aim of giving policymakers the tools and information to act. They have detailed the results of their collaboration in a recent paper in the journal Applied Energy that projects emissions for all buildings across the United States under two GHG reduction scenarios.

      Their paper found that “embodied” emissions — those from materials production and construction — would represent around a quarter of emissions between 2016 and 2050 despite extensive construction.

      Further, many regions would have varying priorities for GHG reductions; some, like the West, would benefit most from reductions to embodied emissions, while others, like parts of the Midwest, would see the greatest payoff from interventions to emissions from energy consumption. If these regional priorities were addressed aggressively, building sector emissions could be reduced by around 30 percent between 2016 and 2050.

      Quantifying contradictions

      Modern buildings are far more complex — and efficient — than their predecessors. Due to new technologies and more stringent building codes, they can offer lower energy consumption and operational emissions. And yet, more-efficient materials and improved construction standards can also generate greater embodied emissions.

      Concrete, in many ways, epitomizes this tradeoff. Though its durability can minimize energy-intensive repairs over a building’s operational life, the scale of its production means that it contributes to a large proportion of the embodied impacts in the building sector.

      As such, the team centered GHG reductions for concrete in its analysis.

      “We took a bottom-up approach, developing reference designs based on a set of residential and commercial building models,” explains Ehsan Vahidi, an assistant professor at the University of Nevada at Reno and a former CSHub postdoc. “These designs were differentiated by roof and slab insulation, HVAC efficiency, and construction materials — chiefly concrete and wood.”

      After measuring the operational and embodied GHG emissions for each reference design, the team scaled up their results to the county level and then national level based on building stock forecasts. This allowed them to estimate the emissions of the entire building sector between 2016 and 2050.

      To understand how various interventions could cut GHG emissions, researchers ran two different scenarios — a “projected” and an “ambitious” scenario — through their framework.

      The projected scenario corresponded to current trends. It assumed grid decarbonization would follow Energy Information Administration predictions; the widespread adoption of new energy codes; efficiency improvement of lighting and appliances; and, for concrete, the implementation of 50 percent low-carbon cements and binders in all new concrete construction and the adoption of full carbon capture, storage, and utilization (CCUS) of all cement and concrete emissions.

      “Our ambitious scenario was intended to reflect a future where more aggressive actions are taken to reduce GHG emissions and achieve the targets,” says Vahidi. “Therefore, the ambitious scenario took these same strategies [of the projected scenario] but featured more aggressive targets for their implementation.”

      For instance, it assumed a 33 percent reduction in grid emissions by 2050 and moved the projected deadlines for lighting and appliances and thermal insulation forward by five and 10 years, respectively. Concrete decarbonization occurred far more quickly as well.

      Reductions and variations

      The extensive growth forecast for the U.S. building sector will inevitably generate a sizable number of emissions. But how much can this figure be minimized?

      Without the implementation of any GHG reduction strategies, the team found that the building sector would emit 62 gigatons CO2 equivalent between 2016 and 2050. That’s comparable to the emissions generated from 156 trillion passenger vehicle miles traveled.

      But both GHG reduction scenarios could cut the emissions from this unmitigated, business-as-usual scenario significantly.

      Under the projected scenario, emissions would fall to 45 gigatons CO2 equivalent — a 27 percent decrease over the analysis period. The ambitious scenario would offer a further 6 percent reduction over the projected scenario, reaching 40 gigatons CO2 equivalent — like removing around 55 trillion passenger vehicle miles from the road over the period.

      “In both scenarios, the largest contributor to reductions was the greening of the energy grid,” notes Vahidi. “Other notable opportunities for reductions were from increasing the efficiency of lighting, HVAC, and appliances. Combined, these four attributes contributed to 85 percent of the emissions over the analysis period. Improvements to them offered the greatest potential emissions reductions.”

      The remaining attributes, such as thermal insulation and low-carbon concrete, had a smaller impact on emissions and, consequently, offered smaller reduction opportunities. That’s because these two attributes were only applied to new construction in the analysis, which was outnumbered by existing structures throughout the period.

      The disparities in impact between strategies aimed at new and existing structures underscore a broader finding: Despite extensive construction over the period, embodied emissions would comprise just 23 percent of cumulative emissions between 2016 and 2050, with the remainder coming primarily from operation.  

      “This is a consequence of existing structures far outnumbering new structures,” explains Jasmina Burek, a CSHub postdoc and an incoming assistant professor at the University of Massachusetts Lowell. “The operational emissions generated by all new and existing structures between 2016 and 2050 will always greatly exceed the embodied emissions of new structures at any given time, even as buildings become more efficient and the grid gets greener.”

      Yet the emissions reductions from both scenarios were not distributed evenly across the entire country. The team identified several regional variations that could have implications for how policymakers must act to reduce building sector emissions.

      “We found that western regions in the United States would see the greatest reduction opportunities from interventions to residential emissions, which would constitute 90 percent of the region’s total emissions over the analysis period,” says Vahidi.

      The predominance of residential emissions stems from the region’s ongoing population surge and its subsequent growth in housing stock. Proposed solutions would include CCUS and low-carbon binders for concrete production, and improvements to energy codes aimed at residential buildings.

      As with the West, ideal solutions for the Southeast would include CCUS, low-carbon binders, and improved energy codes.

      “In the case of Southeastern regions, interventions should equally target commercial and residential buildings, which we found were split more evenly among the building stock,” explains Burek. “Due to the stringent energy codes in both regions, interventions to operational emissions were less impactful than those to embodied emissions.”

      Much of the Midwest saw the inverse outcome. Its energy mix remains one of the most carbon-intensive in the nation and improvements to energy efficiency and the grid would have a large payoff — particularly in Missouri, Kansas, and Colorado.

      New England and California would see the smallest reductions. As their already-strict energy codes would limit further operational reductions, opportunities to reduce embodied emissions would be the most impactful.

      This tremendous regional variation uncovered by the MIT team is in many ways a reflection of the great demographic and geographic diversity of the nation as a whole. And there are still further variables to consider.

      In addition to GHG emissions, future research could consider other environmental impacts, like water consumption and air quality. Other mitigation strategies to consider include longer building lifespans, retrofitting, rooftop solar, and recycling and reuse.

      In this sense, their findings represent the lower bounds of what is possible in the building sector. And even if further improvements are ultimately possible, they’ve shown that regional variation will invariably inform those environmental impact reductions.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Environment

      Concrete’s role in reducing building and pavement emissions

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      Encountering concrete is a common, even routine, occurrence. And that’s exactly what makes concrete exceptional.

      As the most consumed material after water, concrete is indispensable to the many essential systems — from roads to buildings — in which it is used.

      But due to its extensive use, concrete production also contributes to around 1 percent of emissions in the United States and remains one of several carbon-intensive industries globally. Tackling climate change, then, will mean reducing the environmental impacts of concrete, even as its use continues to increase.

      In a new paper in the Proceedings of the National Academy of Sciences, a team of current and former researchers at the MIT Concrete Sustainability Hub (CSHub) outlines how this can be achieved.

      They present an extensive life-cycle assessment of the building and pavements sectors that estimates how greenhouse gas (GHG) reduction strategies — including those for concrete and cement — could minimize the cumulative emissions of each sector and how those reductions would compare to national GHG reduction targets. 

      The team found that, if reduction strategies were implemented, the emissions for pavements and buildings between 2016 and 2050 could fall by up to 65 percent and 57 percent, respectively, even if concrete use accelerated greatly over that period. These are close to U.S. reduction targets set as part of the Paris Climate Accords. The solutions considered would also enable concrete production for both sectors to attain carbon neutrality by 2050.

      Despite continued grid decarbonization and increases in fuel efficiency, they found that the vast majority of the GHG emissions from new buildings and pavements during this period would derive from operational energy consumption rather than so-called embodied emissions — emissions from materials production and construction.

      Sources and solutions

      The consumption of concrete, due to its versatility, durability, constructability, and role in economic development, has been projected to increase around the world.

      While it is essential to consider the embodied impacts of ongoing concrete production, it is equally essential to place these initial impacts in the context of the material’s life cycle.

      Due to concrete’s unique attributes, it can influence the long-term sustainability performance of the systems in which it is used. Concrete pavements, for instance, can reduce vehicle fuel consumption, while concrete structures can endure hazards without needing energy- and materials-intensive repairs.

      Concrete’s impacts, then, are as complex as the material itself — a carefully proportioned mixture of cement powder, water, sand, and aggregates. Untangling concrete’s contribution to the operational and embodied impacts of buildings and pavements is essential for planning GHG reductions in both sectors.

      Set of scenarios

      In their paper, CSHub researchers forecast the potential greenhouse gas emissions from the building and pavements sectors as numerous emissions reduction strategies were introduced between 2016 and 2050.

      Since both of these sectors are immense and rapidly evolving, modeling them required an intricate framework.

      “We don’t have details on every building and pavement in the United States,” explains Randolph Kirchain, a research scientist at the Materials Research Laboratory and co-director of CSHub.

      “As such, we began by developing reference designs, which are intended to be representative of current and future buildings and pavements. These were adapted to be appropriate for 14 different climate zones in the United States and then distributed across the U.S. based on data from the U.S. Census and the Federal Highway Administration”

      To reflect the complexity of these systems, their models had to have the highest resolutions possible.

      “In the pavements sector, we collected the current stock of the U.S. network based on high-precision 10-mile segments, along with the surface conditions, traffic, thickness, lane width, and number of lanes for each segment,” says Hessam AzariJafari, a postdoc at CSHub and a co-author on the paper.

      “To model future paving actions over the analysis period, we assumed four climate conditions; four road types; asphalt, concrete, and composite pavement structures; as well as major, minor, and reconstruction paving actions specified for each climate condition.”

      Using this framework, they analyzed a “projected” and an “ambitious” scenario of reduction strategies and system attributes for buildings and pavements over the 34-year analysis period. The scenarios were defined by the timing and intensity of GHG reduction strategies.

      As its name might suggest, the projected scenario reflected current trends. For the building sector, solutions encompassed expected grid decarbonization and improvements to building codes and energy efficiency that are currently being implemented across the country. For pavements, the sole projected solution was improvements to vehicle fuel economy. That’s because as vehicle efficiency continues to increase, excess vehicle emissions due to poor road quality will also decrease.

      Both the projected scenarios for buildings and pavements featured the gradual introduction of low-carbon concrete strategies, such as recycled content, carbon capture in cement production, and the use of captured carbon to produce aggregates and cure concrete.

      “In the ambitious scenario,” explains Kirchain, “we went beyond projected trends and explored reasonable changes that exceed current policies and [industry] commitments.”

      Here, the building sector strategies were the same, but implemented more aggressively. The pavements sector also abided by more aggressive targets and incorporated several novel strategies, including investing more to yield smoother roads, selectively applying concrete overlays to produce stiffer pavements, and introducing more reflective pavements — which can change the Earth’s energy balance by sending more energy out of the atmosphere.

      Results

      As the grid becomes greener and new homes and buildings become more efficient, many experts have predicted the operational impacts of new construction projects to shrink in comparison to their embodied emissions.

      “What our life-cycle assessment found,” says Jeremy Gregory, the executive director of the MIT Climate Consortium and the lead author on the paper, “is that [this prediction] isn’t necessarily the case.”

      “Instead, we found that more than 80 percent of the total emissions from new buildings and pavements between 2016 and 2050 would derive from their operation.”

      In fact, the study found that operations will create the majority of emissions through 2050 unless all energy sources — electrical and thermal — are carbon-neutral by 2040. This suggests that ambitious interventions to the electricity grid and other sources of operational emissions can have the greatest impact.

      Their predictions for emissions reductions generated additional insights.  

      For the building sector, they found that the projected scenario would lead to a reduction of 49 percent compared to 2016 levels, and that the ambitious scenario provided a 57 percent reduction.

      As most buildings during the analysis period were existing rather than new, energy consumption dominated emissions in both scenarios. Consequently, decarbonizing the electricity grid and improving the efficiency of appliances and lighting led to the greatest improvements for buildings, they found.

      In contrast to the building sector, the pavements scenarios had a sizeable gulf between outcomes: the projected scenario led to only a 14 percent reduction while the ambitious scenario had a 65 percent reduction — enough to meet U.S. Paris Accord targets for that sector. This gulf derives from the lack of GHG reduction strategies being pursued under current projections.

      “The gap between the pavement scenarios shows that we need to be more proactive in managing the GHG impacts from pavements,” explains Kirchain. “There is tremendous potential, but seeing those gains requires action now.”

      These gains from both ambitious scenarios could occur even as concrete use tripled over the analysis period in comparison to the projected scenarios — a reflection of not only concrete’s growing demand but its potential role in decarbonizing both sectors.

      Though only one of their reduction scenarios (the ambitious pavement scenario) met the Paris Accord targets, that doesn’t preclude the achievement of those targets: many other opportunities exist.

      “In this study, we focused on mainly embodied reductions for concrete,” explains Gregory. “But other construction materials could receive similar treatment.

      “Further reductions could also come from retrofitting existing buildings and by designing structures with durability, hazard resilience, and adaptability in mind in order to minimize the need for reconstruction.”

      This study answers a paradox in the field of sustainability. For the world to become more equitable, more development is necessary. And yet, that very same development may portend greater emissions.

      The MIT team found that isn’t necessarily the case. Even as America continues to use more concrete, the benefits of the material itself and the interventions made to it can make climate targets more achievable.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Energy

      3 Questions: Daniel Cohn on the benefits of high-efficiency, flexible-fuel engines for heavy-duty trucking

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      The California Air Resources Board has adopted a regulation that requires truck and engine manufacturers to reduce the nitrogen oxide (NOx) emissions from new heavy-duty trucks by 90 percent starting in 2027. NOx from heavy-duty trucks is one of the main sources of air pollution, creating smog and threatening respiratory health. This regulation requires the largest air pollution cuts in California in more than a decade. How can manufacturers achieve this aggressive goal efficiently and affordably?

      Daniel Cohn, a research scientist at the MIT Energy Initiative, and Leslie Bromberg, a principal research scientist at the MIT Plasma Science and Fusion Center, have been working on a high-efficiency, gasoline-ethanol engine that is cleaner and more cost-effective than existing diesel engine technologies. Here, Cohn explains the flexible-fuel engine approach and why it may be the most realistic solution — in the near term — to help California meet its stringent vehicle emission reduction goals. The research was sponsored by the Arthur Samberg MIT Energy Innovation fund.

      Q. How does your high-efficiency, flexible-fuel gasoline engine technology work?

      A. Our goal is to provide an affordable solution for heavy-duty vehicle (HDV) engines to emit low levels of nitrogen oxide (NOx) emissions that would meet California’s NOx regulations, while also quick-starting gasoline-consumption reductions in a substantial fraction of the HDV fleet.

      Presently, large trucks and other HDVs generally use diesel engines. The main reason for this is because of their high efficiency, which reduces fuel cost — a key factor for commercial trucks (especially long-haul trucks) because of the large number of miles that are driven. However, the NOx emissions from these diesel-powered vehicles are around 10 times greater than those from spark-ignition engines powered by gasoline or ethanol.

      Spark-ignition gasoline engines are primarily used in cars and light trucks (light-duty vehicles), which employ a three-way catalyst exhaust treatment system (generally referred to as a catalytic converter) that reduces vehicle NOx emissions by at least 98 percent and at a modest cost. The use of this highly effective exhaust treatment system is enabled by the capability of spark-ignition engines to be operated at a stoichiometric air/fuel ratio (where the amount of air matches what is needed for complete combustion of the fuel).

      Diesel engines do not operate with stoichiometric air/fuel ratios, making it much more difficult to reduce NOx emissions. Their state-of-the-art exhaust treatment system is much more complex and expensive than catalytic converters, and even with it, vehicles produce NOx emissions around 10 times higher than spark-ignition engine vehicles. Consequently, it is very challenging for diesel engines to further reduce their NOx emissions to meet the new California regulations.

      Our approach uses spark-ignition engines that can be powered by gasoline, ethanol, or mixtures of gasoline and ethanol as a substitute for diesel engines in HDVs. Gasoline has the attractive feature of being widely available and having a comparable or lower cost than diesel fuel. In addition, presently available ethanol in the U.S. produces up to 40 percent less greenhouse gas (GHG) emissions than diesel fuel or gasoline and has a widely available distribution system.

      To make gasoline- and/or ethanol-powered spark-ignition engine HDVs attractive for widespread HDV applications, we developed ways to make spark-ignition engines more efficient, so their fuel costs are more palatable to owners of heavy-duty trucks. Our approach provides diesel-like high efficiency and high power in gasoline-powered engines by using various methods to prevent engine knock (unwanted self-ignition that can damage the engine) in spark-ignition gasoline engines. This enables greater levels of turbocharging and use of higher engine compression ratios. These features provide high efficiency, comparable to that provided by diesel engines. Plus, when the engine is powered by ethanol, the required knock resistance is provided by the intrinsic high knock resistance of the fuel itself. 

      Q. What are the major challenges to implementing your technology in California?

      A. California has always been the pioneer in air pollutant control, with states such as Washington, Oregon, and New York often following suit. As the most populous state, California has a lot of sway — it’s a trendsetter. What happens in California has an impact on the rest of the United States.

      The main challenge to implementation of our technology is the argument that a better internal combustion engine technology is not needed because battery-powered HDVs — particularly long-haul trucks — can play the required role in reducing NOx and GHG emissions by 2035. We think that substantial market penetration of battery electric vehicles (BEV) in this vehicle sector will take a considerably longer time. In contrast to light-duty vehicles, there has been very little penetration of battery power into the HDV fleet, especially in long-haul trucks, which are the largest users of diesel fuel. One reason for this is that long-haul trucks using battery power face the challenge of reduced cargo capability due to substantial battery weight. Another challenge is the substantially longer charging time for BEVs compared to that of most present HDVs.

      Hydrogen-powered trucks using fuel cells have also been proposed as an alternative to BEV trucks, which might limit interest in adopting improved internal combustion engines. However, hydrogen-powered trucks face the formidable challenges of producing zero GHG hydrogen at affordable cost, as well as the cost of storage and transportation of hydrogen. At present the high purity hydrogen needed for fuel cells is generally very expensive.

      Q. How does your idea compare overall to battery-powered and hydrogen-powered HDVs? And how will you persuade people that it is an attractive pathway to follow?

      A. Our design uses existing propulsion systems and can operate on existing liquid fuels, and for these reasons, in the near term, it will be economically attractive to the operators of long-haul trucks. In fact, it can even be a lower-cost option than diesel power because of the significantly less-expensive exhaust treatment and smaller-size engines for the same power and torque. This economic attractiveness could enable the large-scale market penetration that is needed to have a substantial impact on reducing air pollution. Alternatively, we think it could take at least 20 years longer for BEVs or hydrogen-powered vehicles to obtain the same level of market penetration.

      Our approach also uses existing corn-based ethanol, which can provide a greater near-term GHG reduction benefit than battery- or hydrogen-powered long-haul trucks. While the GHG reduction from using existing ethanol would initially be in the 20 percent to 40 percent range, the scale at which the market is penetrated in the near-term could be much greater than for BEV or hydrogen-powered vehicle technology. The overall impact in reducing GHGs could be considerably greater.

      Moreover, we see a migration path beyond 2030 where further reductions in GHG emissions from corn ethanol can be possible through carbon capture and sequestration of the carbon dioxide (CO2) that is produced during ethanol production. In this case, overall CO2 reductions could potentially be 80 percent or more. Technologies for producing ethanol (and methanol, another alcohol fuel) from waste at attractive costs are emerging, and can provide fuel with zero or negative GHG emissions. One pathway for providing a negative GHG impact is through finding alternatives to landfilling for waste disposal, as this method leads to potent methane GHG emissions. A negative GHG impact could also be obtained by converting biomass waste into clean fuel, since the biomass waste can be carbon neutral and CO2 from the production of the clean fuel can be captured and sequestered.

      In addition, our flex-fuel engine technology may be synergistically used as range extenders in plug-in hybrid HDVs, which use limited battery capacity and obviates the cargo capability reduction and fueling disadvantages of long-haul trucks powered by battery alone.

      With the growing threats from air pollution and global warming, our HDV solution is an increasingly important option for near-term reduction of air pollution and offers a faster start in reducing heavy-duty fleet GHG emissions. It also provides an attractive migration path for longer-term, larger GHG reductions from the HDV sector. More