Episode 70: Urban Heat Island Data Tool

Lisa Peña (LP): Heat waves, heat domes, heat advisories - cities across the US are experiencing the effects of extreme temperatures this summer. In some areas, record breaking heat is exacerbated by lack of green space and too much concrete and pavement. These areas are known as urban heat islands. On this episode of Technology Today, we're talking to SwRI engineers who have developed a new tool to identify urban heat island danger zones where conditions put human health and safety at risk.

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Hello, and welcome to Technology Today. I'm Lisa Peña. The summer has been brutal here in SwRI's home city, San Antonio, Texas. We've had our share of triple digit temperatures. So this is definitely the right place to research ways to mitigate the effects of Urban Heat Islands or UHIs. They occur with dense concentrations of pavement and buildings, which absorb heat and raise surrounding temperatures. UHI temperatures can be as much as 20 degrees higher than other nearby locations. During the hot summer months, the temperature spike could be dangerous or even deadly.

Our guests today are SwRI engineers, Shane Siebenaler and Justin Long. In collaboration with the city of San Antonio, they have developed a comprehensive data analysis tool to help metropolitan areas curb the effects of urban heat islands for vulnerable populations. Thank you for being here, Shane and Justin.

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This map of San Antonio illustrates the distribution of urban heat islands (red) near downtown. Southwest Research Institute has created a comprehensive data analysis tool to help the city identify urban heat islands and pursue viable mitigation methods for especially vulnerable populations.

Shane Siebenaler (SS): Thanks for having us.

Justin Long (JL): Thanks for having us. Happy to do it.

LP: So such a timely topic. We are in the depths of the summer heat right now. So let's start with discussing this concept of Urban Heat Islands or UHIs. How do you identify an area as a UHI, and what are the key characteristics of these locations? Shane, if you want to get us started.

SS: Sure. So imagine that you're at a park, you're playing in the grass, there's water features, tree shade. It may be a very hot day, but it's bearable. And then you go to walk to your car. And you notice that as soon as you get in the parking lot, it's much warmer than it was when you were playing. And the asphalt is absorbing sunlight and radiating heat. And that is what you're feeling.

So imagine not just your parking space, but imagine three city blocks of nothing but impervious cover, absorbing heat, maybe buildings that are impacting the ability of wind to move by you. And what you end up with are these UHIs or areas within an urban environment where the temperature can be significantly different even on a hot day. Just imagine multiple degrees hotter.

And then one thing that makes it even worse is that heat doesn't go away immediately at night. And so one of the problems that we have here in south Texas, even on a normal summer day, is running your air conditioner late into the night because it takes a while for that temperature to fall. UHIs are more prone to having those spikes last even longer.

LP: All right, too much pavement, not enough breeze or wind to cool things off. So what are some of the heat-related problems that people can face in a UHI zone?

SS: Sure. So let's start with the people side. There are a lot of vulnerable residents, so either people who are elderly, young children, who have different elements that are more affected by heat. When it is hot outside, you are less likely to exercise, which means you're less likely to get the health benefits from exercise.

If you have a job that is in a non-air conditioned space, it is going to be more uncomfortable and potentially deadly. You can run the risk of heat stroke, heat exhaustion. So on individuals, there are quite a number of impacts that these UHIs have.

And then if you kind of step back a little bit beyond like one person, so wherever you have these hot zones, you're going to consume more power. So there's higher energy costs. In a city like San Antonio that has a mixed electric portfolio, including coal, you're going to have poorer air quality from the fact that you are generating more electricity and also even things that you might not think of on a day-to-day basis.

So let's say that there is a little bit of rain or a leak from a pipe. That water runoff that goes into our creek system and eventually gets into our larger river system, the temperature of that water will greatly increase as it's going over the pavement and even a few degree change in that water temperature can affect the ecology of the streams that it's going into.

JL: Yeah, and I would add that one of the little less talked about concerns with UHIs is the link between heat and cognitive impairment. So there are a number of studies out there that show that we're not quite as sharp when we're under heat stress. So if you're a pedestrian, for example, crossing the street in a UHI zone, the concern is that your ability to make safe decisions could be compromised.

LP: So in UHIs, it really sounds like the stakes are higher. There are more consequences, more costs. All in all, these UHIs can cause issues. And Justin keeping with you, so how did you start to explore UHIs in San Antonio? How did you realize this is an issue here?

JL: Yeah. So in 2023, San Antonio saw 75 consecutive days of triple digit temperature. So this broke the previous record of-- I believe it was in 2009. It shattered that record.

So when you think about that, that's over 20% of the year that we're hitting triple digits. And this is just projected to grow exponentially in the near future. So this isn't something that we're predicting to happen. This is happening currently in our community and is an important thing that we start to address now.

So I'm an environmental engineer by education and training. So I like to think of the environmental engineering discipline as one where engineering and science and public health converge. So much of what we do as environmental engineers is try to make the planet a safer and healthier place for humans to live and flourish, not just humans, but all living things. But I also have a lot of interest in the data sciences, and I like to program. So when Shane brought this project to my attention, I was kind of the perfect match.

SS: And it started with a discussion with the city originally about how do we take socioeconomic data and tie that to other information, in this case, heat? We actually came into this process wanting to look at air quality. And when you overlay different social vulnerability indexes to things like temperature, air quality, et cetera, what you'll notice is that areas where you have poor health or low economic opportunities are often coincidentally in some of the areas where you have flooding, high temperatures, poor air quality.

So we came up with this idea of creating some kind of framework of being able to link technical information and socioeconomic data. And we went to the city of San Antonio to ask, what would you want as a case study to demonstrate this framework? And every single person we talked to said UHI, UHI, UHI. This was on the back end of a very brutal summer, the highest temperature summer in recorded history.

And the city of San Antonio actually has a multi-departmental effort studying UHIs, where they bring over seven different departments across the city every other week to talk about this issue. It was very timely that we were looking at doing this data framework around the same time. And so we pivoted from an initial idea that we had on air quality to jump two feet in on looking at urban heat islands.

LP: What are some of the ways that the effects of UHIs can be mitigated? What's the solution?

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SwRI engineers Shane Siebenaler, left, and Justin Long created a data fusion tool in collaboration with the City of San Antonio to curb urban heat islands, which occur when dense concentrations of pavement and buildings absorb heat and raise surrounding temperatures.

JL: Yeah, so there are several kind of common approaches used to mitigate UHIs. And probably the simplest and most obvious method is this concept of urban greening and vegetative cooling. So vegetation provides several benefits. Obviously, planting trees provides a much needed shade and blocks radiant heat from contacting our built environment and thus lowers the energy demand, the energy burden from cooling our indoor spaces.

But vegetation also provides a cooling effect on the surroundings through the process of transpiration. So when a plant uptakes water through its root system and transports that water to its leaves, the process of converting those liquid water molecules to the vapor phase before it's released through the stomata of the leaf actually absorbs heat energy in the form of latent heat.

So that process actually has a cooling effect on the surroundings. So plants are literally nature's air conditioners. So it's the most obvious and easiest method we can apply to mitigate UHIs.

Another common method is installing highly reflective materials to our surfaces. So these are surfaces with high albedo or high reflectivity. Albedo is a technical term for reflectance. So typically, high albedo surfaces are lighter in color, so think white. And low albedo surfaces are darker in color, so think black. So if you have two vehicles next to each other, one is black, one is white, in the middle of a hot summer day, the white car has much higher albedo, higher reflectivity, so it will feel much cooler to the human touch.

So some of the ways we can apply this concept is by applying certain high reflective materials to things like roofs and walls of our buildings and homes, as well as pavements and asphalt parking lots and roads, for example. So, for example, there's an ongoing cool pavement initiative in San Antonio in which different surface coating products are being tested on asphalt roads. And I believe one of the areas tested showed a maximum surface temperature of delta, of like 18 degrees relative to fresh asphalt. So this is an active area of research. There's a lot of companies developing new novel materials that have these highly reflective properties.

And water bodies and water features are another method used to mitigate UHIs. Water bodies act as a significant heat sink, just like the ocean does, and also to the process of evaporation. They also have a cooling effect on their surroundings. So water features such as fountains and splash pads, for example, not only have a cooling effect, but also provide aesthetic and recreational benefits as well.

And finally, probably the most important and proactive ways we can mitigate UHIs is through proper urban planning and design, as well as improvements to the efficiency of our homes and our buildings. So there are many ways we can incorporate vegetation and even engineered shaded structures early on in the design process, even in an artistic way in our public spaces.

There's a few examples in various parks around the downtown area in San Antonio, where they've incorporated these really nice artistic shaded structures. And again, this is all about manipulating the energy balance within our built environment and reducing that energy burden and the amount of anthropogenic waste heat that's being generated.

LP: All right, so many good solutions there. I love that, plants, water features, those reflective materials. And I really liked what you said about urban planning, starting thinking about UHIs and the concept of cooling things down at the design phase.

So I think that's really important. So tell us about your comprehensive data analysis tool. How does it work? What type of data is it collecting?

JL: Yeah, so the tool is what we refer to as the heat island exploration tool or the HIDE tool. I love that acronym. That's probably what we should all be doing in the middle of the summer is hiding, right? So it's a web-based application that was created using the Python programming language, specifically using an open source Python library called Dash, which provides the user interface kind of dashboard of the application.

So from the user perspective, what you see is a map of San Antonio with a zooming and panning capabilities, with a window that has several dropdown menus in which the user can choose what data is being shown on the map. So a feature of the tool is that it enables the user to layer specific metrics of interest on top of each other, and then you can filter, and tune, and kind of dial the knobs so that the map only shows areas that match those criteria of interest.

So for example, if you want to identify census tracts that have household incomes less than 50,000 per year, let's say, and have heat vulnerability scores, of 5 out of 5, with 5 being the worst, you could set those thresholds. And the map would only show the tracts that meet those criteria.

So in terms of the types of data, we work closely with the city of San Antonio to utilize data that is already being collected pursuant to their ongoing initiatives. So, for example, the city has previously collaborated with UTSA to develop a heat vulnerability assessment tool, which provides zip code level environmental measurement data, including surface temperature, surface albedo, vegetation quality, and various meteorological data. The city has also developed what they call the Equity Atlas, and this is actually something that can be viewed by the public through the sanantonio.gov website.

And the Equity Atlas provides various demographic and socioeconomic data for the city and provides kind of equity rankings for each census tract around San Antonio based on things like income, education level, population of color, unemployment rate, and other socioeconomic data. So we're able to leverage many of those data sets.

And then another kind of major source of data came from a nonprofit organization called Greenlink Analytics. And so Greenlink maintains a really robust database of a wide variety of census tract level socioeconomic and environmental metrics, really for the entire country. So different cities kind of have subscriptions to where they can get data from Greenlink. And then they can use that data for whatever purposes they need. So some example of the data that Greenlink provides is things like tree canopy percentage, income stress, energy burden, and even public health information, such as asthma rates.

So that kind of made up the core of the tool. But we also leveraged several open source data sets, including satellite imaging data. So this is like land surface temperature data from Landsat 8 and 9 satellites. We pulled data from the Climate and Economic Justice Screening Tool, which is a tool created by the federal government through President Biden's Justice40 Initiative.

And that provides a lot of demographic and environmental risk data for the entire country. And then we pulled a lot of data from the US Census Bureau, specifically their community resilience estimates data sets, which provides a lot of niche data sets related to economic and public health.

SS: So the tool uses over 230 different data sets. And the beauty of it is that you can draw correlations between multiple different parameters. So as an example, one of the case studies we used with the city was looking at bus stops. And so if you want to-- if you have a certain amount of money to install shade structures at bus stops, where do you put them?

And so what we did is looked at where do you have the highest temperature, so where do you have these urban heat islands, where do you have high ridership in areas of low income where you're most likely to have people needing to take public transportation to work and where you don't have some nearby tree or other shade structure? And so what it allows you to do is take multiple different parameters and then end up with sorting to give you candidates for any time that you have limited resources.

And that's really the strength of this tool or any sort of similar framework is that municipal governments are trying to solve problems for their residents with limited funding or limited number of opportunities. And you want to be able to target those initiatives to the people that would benefit the most or the most vulnerable. If you just imagine a scenario where the city, for example, through some federal program were to get a rebate where you could do, let's say, air conditioning replacement for 1,000 homes.

If you put that on the local electric provider's website, what you will find is that the most connected people who are probably very wealthy, who are on social media will find that first, and they will take up all the units. Instead, what you want to do is find where do you have people who don't have air conditioning, who live in hot spots, who may not have discretionary income to add air conditioning, and that would be a better use of those funds. And so the struggle that any Metropolitan area has of limited resources, you want to be able to take technical data, so things like likelihood of flooding, high temperature, poor air quality, but you want to link that to information about the people that live in that community to figure out where you should make those interventions.

LP: Okay, so the tool is looking at over 230 data sets, as you mentioned. It funnels all this information down to help you target the people who need it most. So how do you-- and you've already touched on it a bit. But really, how do you envision this tool making communities safer and helping the people who most need it?

SS: Sure. So at various conferences, you will hear municipal government officials all across the country talk about in the past 10 years this move to having data-driven decision-making. So they want to be able to use data and then understand the impact of different interventions.

So as an example, Justin was mentioning the city of San Antonio is starting some cool paving programs where they're going to take certain segments of street and apply a coating to the surface that's more reflective and should drive that temperature down. Well, one thing you can do with a tool like this is do a before and after. So you can look at did that intervention actually have a widespread positive effect? So you can look at temperature before and after. And if the answer is yes and let's say that tool was used to select where to do that pilot program, it's a living model.

So now you can update it because now you've checked off one of those streets from your list. And the tool can sort based on new information, including updated temperature data of where do you go to next. So the way it makes communities safer, more resilient is researchers, government officials using the tool and the data in the tool to inform decisions and then look at the impact as a way of predicting how to use similar programs in the future.

LP: So how long were you-- how long was this tool in development?

SS: Yeah, so this is a internally funded project that was done in several months. And actually the biggest time crunch here is getting access to the data. The data comes in all different forms. And also, we have data from satellites at 1 meter resolution.

We have some data that census tract data, some at the zip code level. So finding a way to put it in a common format was the biggest time crunch. But this can be replicated for other data sets. It can be replicated in other metropolitan areas. As long as there's data, this general framework without a heavy lift could be adapted to study very similar problems in other areas.

LP: All right. So yeah, you developed this tool using data from San Antonio. But as you just mentioned, this is a tool that can be used anywhere. Do you currently have plans to branch out and use it in other cities?

JL: Yeah. So the thing about UHIs is that they have significant spatiotemporal variability at all scales. So there's no two state cities or even tracts that are the same. So what that means is that it's critical for cities to perform their own independent analysis using city specific and even hyperlocal data sets to properly characterize their own UHI impacts and potential mitigation measures. So to answer your question, the current plan for the tool is to support San Antonio's heat resilience initiatives.

However, the framework, as Shane mentioned, of the tool in terms of how it integrates these various data sets, these multidisciplinary data sets, could be used as a model to be applied to other cities.

LP: Okay, so they would just need to take the initiative to gather their own data.

JL: Right, Right.

LP: And get that as accurate as possible for their communities. So did you have any unexpected or surprise findings when developing this tool?

SS: Sure. So as engineers and scientists, we focus a lot on the technical data, so in this case, temperature data. What we don't have as much exposure to is the socioeconomic information.

And it's not a surprise that we see a lot of variants across San Antonio. San Antonio is very infamous for having a lot of economic segregation. And so, for example, even though we have some very wealthy parts of town, almost 18% of San Antonio is below the poverty line.

As you start to look at what is the impact of that economic disparity on access to resources, health care, et cetera, to me, the most surprising piece of information related to life expectancy. So if you go just a few miles down the road from where we are today, recording this, to area code 78207, which is on the historic Westside of San Antonio, and you compare the average life expectancy of somebody in that zip code with somebody on the Northwest side of San Antonio, there is an almost 20-year gap in life expectancy.

LP: Wow. 20 years.

SS: 20 years. And when you hear that, you think maybe the comparison between an industrialized country like the United States versus a third world country, not something that geographically is 10 miles apart, and this relates to things like access to resources, access to health care.

And a tool like this can also help point to what those solutions are. So these are compounding factors. But that big change in life expectancy is very alarming, And it's very real. And that was, I would say, the most surprising element of doing this work.

LP: I mean, that's really shocking. So you're saying that beyond finding these urban heat islands, when you put all this data together and you're sifting through it, you can find other ways to help these communities or find out what's lacking in these communities.

SS: You can find out what's lacking in these communities, and it can also inform how you solve the problem. So Justin, for example, was mentioning the most robust way of reducing urban heat islands is through planning, urban design, trees, et cetera. Well, take a part of the city that doesn't have sidewalks, that doesn't have any sort of right of way that the city owns to be able to put in trees, that maybe has very narrow streets.

You may be limited in retrofitting an area based on the resources available, including San Antonio recently, not related to urban heat islands, had a program to hand out free trees that people could plant in their yard. One of the problems they ran into is that in some of the areas that you would want tree canopy cover for UHIs, there's not adequate irrigation systems at these homes to handle the tree in its infancy.

Also, trees take many, many years to grow to the size that would actually have a positive benefit. So it is not a pick one size fits all. So a tool like this allows you to look at not just where do you need to have an intervention, but what are the most practical interventions based on the physical characteristics of that area.

LP: So that's interesting because you think, oh, we need more greenery. You think about trees. But is there a plant that you think would work well to have some sort of almost immediate positive effect on a UHI?

SS: So most plants that would not consume a lot of water, that can provide shade are typically smaller. And so artificial means of generating shade like artificial green space, using water features, reflective coatings, reflective street surfaces are going to be more shorter term solutions than planting trees. By the way, we don't have to pick just one.

San Antonio's population grew significantly in the past year. And so as the state and the city continue to attract residents, every summer seems to be getting hotter. We're attracting more people.

And as our population increases, we are going to be putting down more concrete. So we are going to be exasperating the fundamentals that drive UHIs. And so we're going to need to continue to look at solutions. And so green space is an important thing, particularly for any kind of new development that we incorporate that into the design of new neighborhoods.

LP: Yeah, I think I'm still kind of shocked by that lifespan discussion and how significant that is and how critical solutions are in these areas. So it sounds like there are a few uses for this tool. And it can do a lot. And it can do a lot of good in a community.

So what's next for this data analysis tool? Any upcoming projects that will build off this tool? What else can it be used for? We just discussed one example.

SS: So another one, to go back to maybe the original motivation for putting together this framework, is to look at air quality. So coincidentally, here in San Antonio, air quality is a significant issue. We have high levels of ozone, which is a respiratory irritant. So residents with COPD, asthma, et cetera can have distress on these days where we have poor air quality.

And so this is something where the framework is actually already there in the tool to be able to ingest air quality data and already link it to the socioeconomic information. And so as San Antonio and the county we live in, Bexar County, continue to struggle with poor air quality, a natural pivot for the use of this tool would be to expand some of our learnings and incorporate not just temperature information but air quality.

JL: Yeah. And it would also be interesting to look at the impact of temperature on air quality because we know temperature is an amazing catalyst to promote some of those photochemical reactions that convert some of our anthropogenic pollutants, such as volatile organic compounds into ozone. So seeing those correlations and maybe highly or more polluted areas and how those correlate with UHIs would be interesting as well.

LP: Seeing all this data come in and learning about this, has this changed your way of doing things at all, hearing about how certain elements impact heat or health? Has it changed your way of life at all?

SS: I'll start by answering actually on the air quality side and not the temperature side. So when you get on the highway here in San Antonio during what we call ozone season, you will see these air quality advisories that will say, don't mow your lawn, don't fill up your car until after 6:00.

And for many, many years, I saw those signs. I was that's nice, but just kind of continued on with my day-to-day life. In doing this kind of work and understanding the impact of what if you spill a little bit of gasoline while you're filling up at the pump can have on the air quality of your neighbors, it does drive you to make behavioral changes. And so one thing that this kind of tool or this kind of assessment can do is allow you to make personal decisions that, while small, can have a positive impact.

The bigger impact is, as researchers, we are now armed with data that we can use to help influence policy, the use of resources. And we are now working with our partners in the city to try to use this data to actually go solve problems in the neighborhoods that are most in need.

JL: Yeah, I mean, I've become the father that yells at my kids to close the door and stop letting all the cool air out.

LP: Yeah.

JL: But no, I certainly think about this in my day to day in terms of my water usage, my energy use at home, things I'm doing in my landscape, for example. So I'm big on trying to plant natives, things with low water usage, and that can provide the shade around your home. And as Shane mentioned as well, like thinking about our day-to-day impacts in terms of how we're commuting to our work and especially on those ozone action days and what we're contributing, right, in terms of air quality and our overall impacts.

KS: So a tricky thing about urban heat islands is that it's the sum of a lot of parts. We started off this conversation by talking about what happens when you walk into a parking lot and you feel that it's warmer. So let's say that you trying to be a concerned citizen say, well, I'm going to apply a reflective coating to my roof to drive down the temperature in my house. That will work for your house, but it is not going to cool your entire neighborhood. You need to have a critical mass of reflective features.

So, for example, you could apply a coating to a very short segment of road. And if you're standing immediately on that road, you'll notice the difference. If you are walking on a sidewalk a block away, you're not going to feel a difference. But if you have enough roads in that area that have this reflective coating, if you have enough roofs, if you have enough tree canopy, there's an aggregate effect where you can lower the temperature of the entire neighborhood.

And so, yes, we do encourage people to be the change that they can make. And it goes a long way. But to actually systemically drive down these large islands within metropolitan areas, it's going to require resources beyond just what one person is doing.

LP: Needs an entire community--

SS: Needs an entire community.

LP: --to take action. I feel like it's so important to ask our engineers, what are you doing in your lives for the rest of us who don't have access to the data that you do or don't see the information coming in like you do. So thank you for sharing what you do personally and how you feel personally about that. I did have another question for both of you. What have you enjoyed about this research project and developing this tool?

SS: So here at Southwest, we have this kind of unofficial tagline of deep sea to deep space. And in the group that Justin and I work on, we work in things that are underwater. We work in things that are in space. And often, the problems that we're trying to solve are somewhere else. They're in a foreign country. They're at the bottom of the ocean, they're in low Earth orbit.

It is rarer in something we're actually trying to work hard on to actually solve problems that directly affect the people in our own community. So whether we're talking about UHIs, air quality, resilience to flooding, these are problems that, for example, San Antonio is encountering. And it's very rewarding to be able to do the job that we do, our normal day-to-day research on projects that can have a positive benefit to the people that live around us, our coworkers et cetera.

JL: Yeah, I would second that. So I essentially have the same answer. So many of the projects I've worked on throughout my career have been in other places around the country and even the world. So what I've enjoyed most is finally getting to work on something that is local and has that positive impact on the community that I reside in and the city I'm from.

LP: And while local, it can be expanded to other areas at some point, so not just helping us but potentially other areas, other cities.

SS: Absolutely.

LP: Well, Shane and Justin, your tool is a real solution, helping real people and cities plan for a potentially dangerous situation. I want to thank you both for joining us today and telling us about your important work. I'm really looking forward to the impact it will have in San Antonio and beyond.

SS: Thanks for having us.

JL: Thank you. Happy to do it.

And thank you to our listeners for learning along with us today. You can hear all of our Technology Today episodes, and see photos, and complete transcripts at podcast.swri.org. Remember to share our podcast and subscribe on your favorite podcast platform.

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Ian McKinney and Bryan Ortiz are the podcast audio engineers and editors. I am producer and host, Lisa Peña.

Thanks for listening.

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