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At a New York Mets game this weekend, I saw something I’m not used to seeing until late summer. No, not the inexplicable late-season implosion of a beloved local franchise — a neon-red setting sun.
West Coasters know this sun well. I call it the “Eye of Sauron” sun; others say it’s “blood-red.” In reality, the color is harder to describe, more like the red-orange-pink insides of a halved grapefruit. It feels unnatural and eerie, and is the effect of shorter wavelengths of light being filtered out of a sunrise or sunset by particles in the air caused by pollution, including wildfire smoke.
I immediately grabbed my phone to find the source of the haze, but a quick Google search of “Where is this smoke coming from?” didn’t turn up any immediate results. In fact, it can be frustratingly difficult to figure out the origin of wildfire smoke when you see or smell it. This only gets more difficult as fire season wears on; is the smoke you’re inhaling from a small nearby fire, or part of a bigger burn blowing in from somewhere else?
Unfortunately, there isn’t a handy app yet that will simply tell you “the smoke overhead is from the Canadian fires” — which, in the case of the pollution I was experiencing in Flushing Meadows, it actually was. But you can cobble together an answer about where smoke is coming from by using a few different methods.
When you climb a mountain, an inaccurate forecast can be the difference between life and death. I learned about the MyRadar app from an experienced mountaineering guide who swears it is the most accurate weather app on the market. It’s also become my go-to app for figuring out where the wildfire smoke I’m inhaling is coming from.
The app pulls data from the United States Geological Survey, InciWeb, and the United States Forest Service’s Risk Incident Information Management System to build its smoke and fire maps (it also received a wildfire detection grant from the National Oceanic and Atmospheric Administration last year). Hovering over my house at around 2 p.m. on Monday, the app clearly told me I was experiencing “moderate air quality” and “heavy smoke hazard.” Zooming out, it’s easy to guess based on the shape of the “heavy smoke hazard” cloud that the pollution is wrapping down from the massive fires burning in Alberta, Saskatchewan, and British Columbia. You can also overlay wind patterns on the app for further confirmation.

MyRadar’s visuals can get a little cluttered, though, and parsing this information still leaves you with an informed guess. But it’s one that can be easily cross-checked against the EPA’s AirNow app.
The EPA app is a little more straightforward: It gives users an upfront measurement of their air quality index, or AQI, which, with a click, can be broken down into “primary pollutants.” In the case of New York City on Monday, it was PM2.5, the expected particle from wildfire smoke (and also “the bad one” when it comes to your health). The app also shows a “forecast” of how the AQI is expected to develop over the coming hours; in New York’s case, it was going to get worse before it got better.
The AirNow app additionally has a “smoke” tab that shows a similar smoke plume overlay as MyRadar’s. By clicking on the globe in the upper left-hand corner, you can view additional fire information, including how far away the nearest burn is, and confirm if you’re under a smoke plume. Using these two pieces of information together, you can further deduce if the smoke you’re experiencing is blowing in from somewhere far away or nearby (some of New York’s smoke may be from the Cannon Ball 2 Fire to our northwest, in Passaic County, New Jersey, but the app shows me that fire is fairly small — 107 total acres — and so in this case, it is not the main culprit).

The BlueSky Canada Smoke Forecasting System (FireSmoke Canada) is run by the University of British Columbia, and while it has an emphasis on Canadian air quality, it is run in partnership with the United States Forest Service and includes U.S. data too. The FireSmoke Canada website specifically tracks PM2.5 smoke particles at ground level from wildfires across North America (“ground level” is an important distinction because sometimes smoke plumes will be too high in the atmosphere to actually affect your health). The FireSmoke Canada map throws in a time-lapse animation and for my purposes, it clearly showed that the smoke in New York was coming down from the Canadian fires.
The FireSmoke Canada map is also a great way to figure out the origin of local fire smoke too since it often shows plumes from even small blazes (though it has technical limitations too, which it details in its FAQ). Unfortunately, the service doesn’t allow users to click on a fire to learn more information about it, which means toggling back and forth between the AirNow or MyRadar app, or the FIRMS U.S./Canada website, to get a fuller picture of what is going on.

Other discrepancies between the apps can be frustrating; AirNow, for example, still shows the Great Lakes Wildfire as burning in North Carolina, though it’s not appearing on FireSmoke Canada’s tracker; MyRadar provides the most context, showing the containment at 90% and labeling its status as “minimal.” On the other hand, MyRadar and AirNow don’t show a fire near Hanford, Washington, while MySmoke Canada does.
Short of doing your own detective work with various wildfire tracking services, local media otherwise remain the best option for figuring out where smoke is coming from. The Hanford blaze that eluded MyRadar and AirNow, for example, is easily confirmed by the regional press; started by lightning, the fire reportedly burned around 1,000 acres and is now 100% contained.
Turns out, Googling “Why is it smoky outside” — while it might feel archaic — still might be one of the best options.
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation.What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.