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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.
The three shades of gray in the center of the continent shows the path of smoke from the Canadian fires.MyRadar
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).
Information under the smoke tab on the AirNow app.AirNow
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.
The FireSmoke Canada website tracks PM2.5 with a handy animation but does not offer information about individual fires like MyRadar and AirNow do.FireSmoke Canada
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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Why the new “reasoning” models might gobble up more electricity — at least in the short term
What happens when artificial intelligence takes some time to think?
The newest set of models from OpenAI, o1-mini and o1-preview, exhibit more “reasoning” than existing large language models and associated interfaces, which spit out answers to prompts almost instantaneously.
Instead, the new model will sometimes “think” for as long as a minute or two. “Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes,” OpenAI announced in a blog post last week. The company said these models perform better than their existing ones on some tasks, especially related to math and science. “This is a significant advancement and represents a new level of AI capability,” the company said.
But is it also a significant advancement in energy usage?
In the short run at least, almost certainly, as spending more time “thinking” and generating more text will require more computing power. As Erik Johannes Husom, a researcher at SINTEF Digital, a Norwegian research organization, told me, “It looks like we’re going to get another acceleration of generative AI’s carbon footprint.”
Discussion of energy use and large language models has been dominated by the gargantuan requirements for “training,” essentially running a massive set of equations through a corpus of text from the internet. This requires hardware on the scale of tens of thousands of graphical processing units and an estimated 50 gigawatt-hours of electricity to run.
Training GPT-4 cost “more than” $100 million OpenAI chief executive Sam Altman has said; the next generation models will likely cost around $1 billion, according to Anthropic chief executive Dario Amodei, a figure that might balloon to $100 billion for further generation models, according to Oracle founder Larry Ellison.
While a huge portion of these costs are hardware, the energy consumption is considerable as well. (Meta reported that when training its Llama 3 models, power would sometimes fluctuate by “tens of megawatts,” enough to power thousands of homes). It’s no wonder that OpenAI’s chief executive Sam Altman has put hundreds of millions of dollars into a fusion company.
But the models are not simply trained, they're used out in the world, generating outputs (think of what ChatGPT spits back at you). This process tends to be comparable to other common activities like streaming Netflix or using a lightbulb. This can be done with different hardware and the process is more distributed and less energy intensive.
As large language models are being developed, most computational power — and therefore most electricity — is used on training, Charlie Snell, a PhD student at University of California at Berkeley who studies artificial intelligence, told me. “For a long time training was the dominant term in computing because people weren’t using models much.” But as these models become more popular, that balance could shift.
“There will be a tipping point depending on the user load, when the total energy consumed by the inference requests is larger than the training,” said Jovan Stojkovic, a graduate student at the University of Illinois who has written about optimizing inference in large language models.
And these new reasoning models could bring that tipping point forward because of how computationally intensive they are.
“The more output a model produces, the more computations it has performed. So, long chain-of-thoughts leads to more energy consumption,” Husom of SINTEF Digital told me.
OpenAI staffers have been downright enthusiastic about the possibilities of having more time to think, seeing it as another breakthrough in artificial intelligence that could lead to subsequent breakthroughs on a range of scientific and mathematical problems. “o1 thinks for seconds, but we aim for future versions to think for hours, days, even weeks. Inference costs will be higher, but what cost would you pay for a new cancer drug? For breakthrough batteries? For a proof of the Riemann Hypothesis? AI can be more than chatbots,” OpenAI researcher Noam Brown tweeted.
But those “hours, days, even weeks” will mean more computation and “there is no doubt that the increased performance requires a lot of computation,” Husom said, along with more carbon emissions.
But Snell told me that might not be the end of the story. It’s possible that over the long term, the overall computing demands for constructing and operating large language models will remain fixed or possibly even decline.
While “the default is that as capabilities increase, demand will increase and there will be more inference,” Snell told me, “maybe we can squeeze reasoning capability into a small model ... Maybe we spend more on inference but it’s a much smaller model.”
OpenAI hints at this possibility, describing their o1-mini as “a smaller model optimized for STEM reasoning,” in contrast to other, larger models that “are pre-trained on vast datasets” and “have broad world knowledge,” which can make them “expensive and slow for real-world applications.” OpenAI is suggesting that a model can know less but think more and deliver comparable or better results to larger models — which might mean more efficient and less energy hungry large language models.
In short, thinking might use less brain power than remembering, even if you think for a very long time.
On Azerbaijan’s plans, offshore wind auctions, and solar jobs
Current conditions: Thousands of firefighters are battling raging blazes in Portugal • Shanghai could be hit by another typhoon this week • More than 18 inches of rain fell in less than 24 hours in Carolina Beach, which forecasters say is a one-in-a-thousand-year event.
Azerbaijan, the host of this year’s COP29, today put forward a list of “non-negotiated” initiatives for the November climate summit that will “supplement” the official mandated program. The action plan includes the creation of a new “Climate Finance Action Fun” that will take (voluntary) contributions from fossil fuel producing countries, a call for increasing battery storage capacity, an appeal for a global “truce” during the event, and a declaration aimed at curbing methane emissions from waste (which the Financial Times noted is “only the third most common man-made source of methane, after the energy and agricultural sectors”). The plan makes no mention of furthering efforts to phase out fossil fuels in the energy system.
The Interior Department set a date for an offshore wind energy lease sale in the Gulf of Maine, an area which the government sees as suitable for developing floating offshore wind technology. The auction will take place on October 29 and cover eight areas on the Outer Continental Shelf off Massachusetts, New Hampshire, and Maine. The area could provide 13 gigawatts of offshore wind energy, if fully developed. The Biden administration has a goal of installing 30 GW of offshore wind by 2030, and has approved about half that amount so far. The DOI’s terms and conditions for the October lease sale include “stipulations designed to promote the development of a robust domestic U.S. supply chain for floating wind.” Floating offshore wind turbines can be deployed in much deeper waters than traditional offshore projects, and could therefore unlock large areas for clean power generation. Last month the government gave the green light for researchers to study floating turbines in the Gulf of Maine.
In other wind news, BP is selling its U.S. onshore wind business, bp Wind Energy. The firm’s 10 wind farm projects have a total generating capacity of 1.3 gigawatts and analysts think they could be worth $2 billion. When it comes to renewables, the fossil fuel giant said it is focusing on investing in solar growth, and onshore wind is “not aligned” with those plans.
The number of jobs in the U.S. solar industry last year grew to 279,447, up 6% from 2022, according to a new report from the nonprofit Interstate Renewable Energy Council. Utility-scale solar added 1,888 jobs in 2023, a 6.8% increase and a nice rebound from 2022, when the utility-scale solar market recorded a loss in jobs. The report warns that we might not see the same kind of growth for solar jobs in 2024, though. Residential installations have dropped, and large utility-scale projects are struggling with grid connection. The report’s authors also note that as the industry grows, it faces a shortage of skilled workers.
Interstate Renewable Energy Council
Most employers reported that hiring qualified solar workers was difficult, especially in installation and project development. “It’s difficult because our projects are built in very rural areas where there just aren't a lot of people,” one interviewee who works at a utility-scale solar firm said. “We strive to hire as many local people as possible because we want local communities to feel the economic impact or benefit from our projects. So in some communities where we go, it is difficult to find local people that are skilled and can perform the work.”
The torrential rain that has battered central Europe is tapering off a bit, but the danger of rising water remains. “The massive amounts of rain that fell is now working its way through the river systems and we are starting to see flooding in areas that avoided the worst of the rain,” BBC meteorologist Matt Taylor explained. The Polish city of Nysa told its 44,000 residents to leave yesterday as water rose. In the Czech Republic, 70% of the town of Litovel was submerged in 3 feet of flooding. The death toll from the disaster has risen to 18. Now the forecast is calling for heavy rain in Italy. “The catastrophic rainfall hitting central Europe is exactly what scientists expect with climate change,” Joyce Kimutai, a climate scientist with Imperial College London’s Grantham Institute, toldThe Guardian.
A recent study examining the effects of London’s ultra-low emissions zone on how students get to school found that a year after the rules came into effect, many students had switched to walking, biking, or taking public transport instead of being driven in private vehicles.
Welcome to Decarbonize Your Life, Heatmap’s special report that aims to help you make decisions in your own life that are better for the climate, better for you, and better for the world we all live in. This is our attempt, in other words, to assist you in living something like a normal life while also making progress in the fight against climate change.
That means making smarter and more informed decisions about how climate change affects your life — and about how your life affects climate change. The point is not what you shouldn’t do (although there is some of that). It’s about what you should do to exert the most leverage on the global economic system and, hopefully, nudge things toward decarbonization just a little bit faster.
We certainly think we’ve hit upon a better way to think about climate action, but you don’t have to take our word for it. Keep reading here for more on how (and why) we think about decarbonizing your life — or just skip ahead to our recommendations, below.