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    Q&A: Are far-reaching fires the new normal?

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    Where there’s smoke, there is fire. But with climate change, larger and longer-burning wildfires are sending smoke farther from their source, often to places that are unaccustomed to the exposure. That’s been the case this week, as smoke continues to drift south from massive wildfires in Canada, prompting warnings of hazardous air quality, and poor visibility in states across New England, the mid-Atlantic, and the Midwest.

    As wildfire season is just getting going, many may be wondering: Are the air-polluting effects of wildfires a new normal?

    MIT News spoke with Professor Colette Heald of the Department of Civil and Environmental Engineering and the Department of Earth, Atmospheric and Planetary Sciences, and Professor Noelle Selin of the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. Heald specializes in atmospheric chemistry and has studied the climate and health effects associated with recent wildfires, while Selin works with atmospheric models to track air pollutants around the world, which she uses to inform policy decisions on mitigating  pollution and climate change. The researchers shared some of their insights on the immediate impacts of Canada’s current wildfires and what downwind regions may expect in the coming months, as the wildfire season stretches into summer.  

    Q: What role has climate change and human activity played in the wildfires we’ve seen so far this year?

    Heald: Unusually warm and dry conditions have dramatically increased fire susceptibility in Canada this year. Human-induced climate change makes such dry and warm conditions more likely. Smoke from fires in Alberta and Nova Scotia in May, and Quebec in early June, has led to some of the worst air quality conditions measured locally in Canada. This same smoke has been transported into the United States and degraded air quality here as well. Local officials have determined that ignitions have been associated with lightning strikes, but human activity has also played a role igniting some of the fires in Alberta.

    Q: What can we expect for the coming months in terms of the pattern of wildfires and their associated air pollution across the United States?

    Heald: The Government of Canada is projecting higher-than-normal fire activity throughout the 2023 fire season. Fire susceptibility will continue to respond to changing weather conditions, and whether the U.S. is impacted will depend on the winds and how air is transported across those regions. So far, the fire season in the United States has been below average, but fire risk is expected to increase modestly through the summer, so we may see local smoke influences as well.

    Q: How has air pollution from wildfires affected human health in the U.S. this year so far?

    Selin: The pollutant of most concern in wildfire smoke is fine particulate matter (PM2.5) – fine particles in the atmosphere that can be inhaled deep into the lungs, causing health damages. Exposure to PM2.5 causes respiratory and cardiovascular damage, including heart attacks and premature deaths. It can also cause symptoms like coughing and difficulty breathing. In New England this week, people have been breathing much higher concentrations of PM2.5 than usual. People who are particularly vulnerable to the effects are likely experiencing more severe impacts, such as older people and people with underlying conditions. But PM2.5 affects everyone. While the number and impact of wildfires varies from year to year, the associated air pollution from them generally lead to tens of thousands of premature deaths in the U.S. overall annually. There is also some evidence that PM2.5 from fires could be particularly damaging to health.

    While we in New England usually have relatively lower levels of pollution, it’s important also to note that some cities around the globe experience very high PM2.5 on a regular basis, not only from wildfires, but other sources such as power plants and industry. So, while we’re feeling the effects over the past few days, we should remember the broader importance of reducing PM2.5 levels overall for human health everywhere.

    Q: While firefighters battle fires directly this wildfire season, what can we do to reduce the effects of associated air pollution? And what can we do in the long-term, to prevent or reduce wildfire impacts?

    Selin: In the short term, protecting yourself from the impacts of PM2.5 is important. Limiting time outdoors, avoiding outdoor exercise, and wearing a high-quality mask are some strategies that can minimize exposure. Air filters can help reduce the concentrations of particles in indoor air. Taking measures to avoid exposure is particularly important for vulnerable groups. It’s also important to note that these strategies aren’t equally possible for everyone (for example, people who work outside) — stressing the importance of developing new strategies to address the underlying causes of increasing wildfires.

    Over the long term, mitigating climate change is important — because warm and dry conditions lead to wildfires, warming increases fire risk. Preventing the fires that are ignited by people or human activities can help.  Another way that damages can be mitigated in the longer term is by exploring land management strategies that could help manage fire intensity. More

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      in Data Management & Statistics

      Hurricane-resistant construction may be undervalued by billions of dollars annually

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      In Florida, June typically marks the beginning of hurricane season. Preparation for a storm may appear as otherworldly as it is routine: businesses and homes board up windows and doors, bottled water is quick to sell out, and public buildings cease operations to serve as emergency shelters.

      What happens next may be unpredictable. If things take a turn for the worse, myriad homes may be leveled. A 2019 Congressional Budget Office report estimated that hurricane-related wind damage causes $14 billion in losses to the residential sector annually. 

      However, new research led by Ipek Bensu Manav, an MIT graduate student in civil and environmental engineering and research assistant at MIT’s Concrete Sustainability Hub, suggests that the value of mitigating this wind damage through stronger construction methods may be significantly underestimated. 

      In fact, the failure of wind loss models to account for neighborhood texture — the density and configuration of surrounding buildings with respect to a building of interest — may result in an over 80 percent undervaluation of these methods in Florida.

      Methodology

      Hazus, a loss estimation tool developed and currently used by the Federal Emergency Management Agency (FEMA), estimates physical and economic damage to buildings due to wind and windborne debris. However, the tool assumes that all buildings in a neighborhood experience the same wind loading.

      Manav notes that this assumption disregards the complexity of neighborhood texture. Buildings of different shapes and sizes can be arranged in innumerable ways. This arrangement can amplify or reduce the wind load on buildings within the neighborhood. 

      Wind load amplifications and reductions result from effects referred to as tunneling and shielding. Densely built-up areas with grid-like layouts are particularly susceptible to wind tunneling effects. You might have experienced these effects yourself walking down a windy street, such as Main Street in Cambridge, Massachusetts, near the MIT campus, only to turn the corner and feel calmer air.

      To address this, Manav and her team sought to create a hurricane loss model that accounts for neighborhood texture. By combining GIS files, census tract data, and models of wind recurrence and structural performance, the researchers constructed a high-resolution estimate of expected wind-related structural losses, as well as the benefits of mitigation to reduce those losses. 

      The model builds on prior research led by Jacob Roxon, a recent CSHub postdoc and co-author of this paper, who developed an empirical relationship that estimates building-specific wind gusts with information about building layout in a given neighborhood. 

      A challenge the researchers had to overcome was the fact that the building footprints that were available for this estimation have little-to-no information on occupancy and building type.

      Manav addressed this by developing a novel statistical model that assigns occupancy and building types to structures based on characteristics of the census tract in which they are located.

      Analysis and cost perspective

      The researchers then estimated the value of stronger construction in a case study of residential buildings in Florida. This involved modeling the impact of several mitigation measures applied to over 9.3 million housing units spread across 6.9 million buildings.

      A map of effective wind speed ratio in Florida. Orange coloration indicates census tracts where, on average, structures experience amplifications in wind loads beyond what current tools estimate. Blue coloration indicates census tracts where, on average, structures experience reductions in wind loads.

      Image courtesy of the MIT Concrete Sustainability Hub.

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      Texture-related loss implications were found to be higher in census tracts along the coast. This occurs because these areas tend to be more dense and ordered, leading to higher wind load amplifications. Also, these loss implications are particularly high for single-family homes, which are more susceptible to damage and have a higher replacement cost per housing unit.

      “Our results sound the alarm that wind loads are more severe than we think,” says Manav. “That is not even accounting for climate change, which might make hurricanes more frequent and their wind speeds more intense over time.”

      The researchers computed expected losses and benefits statewide for hurricane wind damage and its mitigation. They found that $8.1 billion could be saved per year in a scenario where all homes were mitigated with simple measures such as stronger connections between roofs and walls or tighter nail spacing.

      Conventional loss estimation models value these same measures as saving only $4.4 billion per year. This means that conventional models are underestimating the value of stronger construction by over 80 percent.

      “It is important that the benefits of resilient design be quantified so that financial incentives — whether lending, insurance, or otherwise — can be brought to bear to increase mitigation. Manav’s research will move the industry forward toward justifying these benefits,” says structural engineer Evan Reis, who is the executive director of the U.S. Resiliency Council.

      Further implications

      The paper recommends that coastal states enhance their building codes, especially in densely built-up areas, to save dollars and save lives. Manav notes that current building codes do not sufficiently account for texture-induced load amplifications. 

      “Even a building built to code may not be able to protect you and your family,” says Manav. “We need to properly quantify the benefits of mitigating in areas that are exposed to high winds so we promote the right standards of construction where losses can be catastrophic.”

      A goal of Manav’s work is to provide citizens with the information they need before disaster strikes. She has created an online dashboard where you can preview the potential benefits of applying mitigation measures in different communities — perhaps even your own.

      “During my research, I kept hitting a wall. I found that it was difficult to use publicly available information to piece together the bigger picture,” she comments. “We started developing the dashboard to equip homeowners and stakeholders with accessible and actionable information.”

      As a next step, Manav is investigating socioeconomic consequences of hurricane wind damage. 

      “High-resolution analysis, like our case study, allows us to simulate individual household impacts within a geographical context,” adds Manav. “With this, we can capture how differing availability of financial resources may influence how communities cope with the aftermath of natural hazards.” More

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      in Data Management & Statistics

      Looking forward to forecast the risks of a changing climate

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      On April 11, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the third in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

      Extreme weather events that were once considered rare have become noticeably less so, from intensifying hurricane activity in the North Atlantic to wildfires generating massive clouds of ozone-damaging smoke. But current climate models are unprepared when it comes to estimating the risk that these increasingly extreme events pose — and without adequate modeling, governments are left unable to take necessary precautions to protect their communities.

      MIT Department of Earth, Atmospheric and Planetary Science (EAPS) Professor Paul O’Gorman researches this trend by studying how climate affects the atmosphere and incorporating what he learns into climate models to improve their accuracy. One particular focus for O’Gorman has been changes in extreme precipitation and midlatitude storms that hit areas like New England.

      “These extreme events are having a lot of impact, but they’re also difficult to model or study,” he says. Seeing the pressing need for better climate models that can be used to develop preparedness plans and climate change mitigation strategies, O’Gorman and collaborators Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in EAPS, and Miho Mazereeuw, associate professor in MIT’s Department of Architecture, are leading an interdisciplinary group of scientists, engineers, and designers to tackle this problem with their MIT Climate Grand Challenges flagship project, “Preparing for a new world of weather and climate extremes.”

      “We know already from observations and from climate model predictions that weather and climate extremes are changing and will change more,” O’Gorman says. “The grand challenge is preparing for those changing extremes.”

      Their proposal is one of five flagship projects recently announced by the MIT Climate Grand Challenges initiative — an Institute-wide effort catalyzing novel research and engineering innovations to address the climate crisis. Selected from a field of almost 100 submissions, the team will receive additional funding and exposure to help accelerate and scale their project goals. Other MIT collaborators on the proposal include researchers from the School of Engineering, the School of Architecture and Planning, the Office of Sustainability, the Center for Global Change Science, and the Institute for Data, Systems and Society.

      Weather risk modeling

      Fifteen years ago, Kerry Emanuel developed a simple hurricane model. It was based on physics equations, rather than statistics, and could run in real time, making it useful for modeling risk assessment. Emanuel wondered if similar models could be used for long-term risk assessment of other things, such as changes in extreme weather because of climate change.

      “I discovered, somewhat to my surprise and dismay, that almost all extant estimates of long-term weather risks in the United States are based not on physical models, but on historical statistics of the hazards,” says Emanuel. “The problem with relying on historical records is that they’re too short; while they can help estimate common events, they don’t contain enough information to make predictions for more rare events.”

      Another limitation of weather risk models which rely heavily on statistics: They have a built-in assumption that the climate is static.

      “Historical records rely on the climate at the time they were recorded; they can’t say anything about how hurricanes grow in a warmer climate,” says Emanuel. The models rely on fixed relationships between events; they assume that hurricane activity will stay the same, even while science is showing that warmer temperatures will most likely push typical hurricane activity beyond the tropics and into a much wider band of latitudes.

      As a flagship project, the goal is to eliminate this reliance on the historical record by emphasizing physical principles (e.g., the laws of thermodynamics and fluid mechanics) in next-generation models. The downside to this is that there are many variables that have to be included. Not only are there planetary-scale systems to consider, such as the global circulation of the atmosphere, but there are also small-scale, extremely localized events, like thunderstorms, that influence predictive outcomes.

      Trying to compute all of these at once is costly and time-consuming — and the results often can’t tell you the risk in a specific location. But there is a way to correct for this: “What’s done is to use a global model, and then use a method called downscaling, which tries to infer what would happen on very small scales that aren’t properly resolved by the global model,” explains O’Gorman. The team hopes to improve downscaling techniques so that they can be used to calculate the risk of very rare but impactful weather events.

      Global climate models, or general circulation models (GCMs), Emanuel explains, are constructed a bit like a jungle gym. Like the playground bars, the Earth is sectioned in an interconnected three-dimensional framework — only it’s divided 100 to 200 square kilometers at a time. Each node comprises a set of computations for characteristics like wind, rainfall, atmospheric pressure, and temperature within its bounds; the outputs of each node are connected to its neighbor. This framework is useful for creating a big picture idea of Earth’s climate system, but if you tried to zoom in on a specific location — like, say, to see what’s happening in Miami or Mumbai — the connecting nodes are too far apart to make predictions on anything specific to those areas.

      Scientists work around this problem by using downscaling. They use the same blueprint of the jungle gym, but within the nodes they weave a mesh of smaller features, incorporating equations for things like topography and vegetation or regional meteorological models to fill in the blanks. By creating a finer mesh over smaller areas they can predict local effects without needing to run the entire global model.

      Of course, even this finer-resolution solution has its trade-offs. While we might be able to gain a clearer picture of what’s happening in a specific region by nesting models within models, it can still make for a computing challenge to crunch all that data at once, with the trade-off being expense and time, or predictions that are limited to shorter windows of duration — where GCMs can be run considering decades or centuries, a particularly complex local model may be restricted to predictions on timescales of just a few years at a time.

      “I’m afraid that most of the downscaling at present is brute force, but I think there’s room to do it in better ways,” says Emanuel, who sees the problem of finding new and novel methods of achieving this goal as an intellectual challenge. “I hope that through the Grand Challenges project we might be able to get students, postdocs, and others interested in doing this in a very creative way.”

      Adapting to weather extremes for cities and renewable energy

      Improving climate modeling is more than a scientific exercise in creativity, however. There’s a very real application for models that can accurately forecast risk in localized regions.

      Another problem is that progress in climate modeling has not kept up with the need for climate mitigation plans, especially in some of the most vulnerable communities around the globe.

      “It is critical for stakeholders to have access to this data for their own decision-making process. Every community is composed of a diverse population with diverse needs, and each locality is affected by extreme weather events in unique ways,” says Mazereeuw, the director of the MIT Urban Risk Lab. 

      A key piece of the team’s project is building on partnerships the Urban Risk Lab has developed with several cities to test their models once they have a usable product up and running. The cities were selected based on their vulnerability to increasing extreme weather events, such as tropical cyclones in Broward County, Florida, and Toa Baja, Puerto Rico, and extratropical storms in Boston, Massachusetts, and Cape Town, South Africa.

      In their proposal, the team outlines a variety of deliverables that the cities can ultimately use in their climate change preparations, with ideas such as online interactive platforms and workshops with stakeholders — such as local governments, developers, nonprofits, and residents — to learn directly what specific tools they need for their local communities. By doing so, they can craft plans addressing different scenarios in their region, involving events such as sea-level rise or heat waves, while also providing information and means of developing adaptation strategies for infrastructure under these conditions that will be the most effective and efficient for them.

      “We are acutely aware of the inequity of resources both in mitigating impacts and recovering from disasters. Working with diverse communities through workshops allows us to engage a lot of people, listen, discuss, and collaboratively design solutions,” says Mazereeuw.

      By the end of five years, the team is hoping that they’ll have better risk assessment and preparedness tool kits, not just for the cities that they’re partnering with, but for others as well.

      “MIT is well-positioned to make progress in this area,” says O’Gorman, “and I think it’s an important problem where we can make a difference.” More