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Air Pollution’s Daily Pulse Over the Northeast

✇NASA Science
By: Michala Garrison
7:05 am
3:05 pm
A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison
A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison
A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison
A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison
7:05 am
3:05 pm
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor.The instrument detected lower levels of the gas at 3:05 p.m. EDT (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon. NASA Earth Observatory images by Michala Garrison.

More than 35 million people live along the New York–Washington corridor and breathe the region’s air. While air quality has improved significantly in recent decades, outbreaks of ground-level ozone remain common, particularly in the warm summer months, when the chemical reactions that produce the pollutant accelerate and stagnant air allows ozone to accumulate.

A reminder of this seasonal phenomenon came earlier than usual in 2026, when a mid-May heat wave prompted the New York State Department of Health and the New York Department of Environmental Conservation to issue a health advisory on May 17 over concerns about ozone. The code orange advisory warned young people, older adults, and those working or exercising outdoors to limit activity due to ozone’s respiratory and cardiovascular health impacts.

As expected, ground-based air-quality sensors operated by state and federal agencies showed ozone reaching unhealthy levels for sensitive groups on May 18, something that typically happens several times per year. Meanwhile, NASA’s TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument observed the event from geostationary orbit 22,000 miles (35,000 kilometers) above the equator, a unique vantage point that allows the sensor to collect frequent observations of air pollution.

TEMPO detects nitrogen dioxide (NO2), a gas emitted by burning fuels, particularly by motor vehicles, that contributes to ozone formation. “There’s often a clear and interesting pattern in TEMPO’s nitrogen dioxide data during ozone alert days,” said Hazem Mahmoud, an atmospheric scientist at NASA’s Atmospheric Science Data Center at Langley Research Center. “We see high concentrations of nitrogen dioxide during the early morning commute that drop off sharply in the late afternoon as ozone increases.”

The decline occurs as sunlight fuels photochemical reactions involving nitrogen dioxide, volatile organic compounds, and oxygen that lead to ozone formation. By late afternoon, these reactions deplete much of the available nitrogen dioxide, slowing ozone production until the cycle begins again the next day.

The pair of images above underscores the pattern. The image on the left was acquired at 7:05 a.m. local time when nitrogen dioxide concentrations were high during the morning commute. By 3:05 p.m. (right), most of the nitrogen dioxide had declined substantially, and surface ozone levels were elevated (below). Meanwhile, afternoon sea breezes appear to have transported the remaining nitrogen dioxide slightly to the west. Note that the data shown is provisional, and processing methods are still being refined.

Sensors on earlier polar-orbiting satellites, such as OMI (Ozone Monitoring Instrument) and TROPOMI (Tropospheric Monitoring Instrument), sampled nitrogen dioxide over New York once per day. After its launch in 2023, TEMPO began providing data every hour, allowing researchers to track the evolution and dispersion of air pollution at much finer time scales. 

“TEMPO is helping fill data gaps between ground stations and allowing us to ask new questions,” Mahmoud said. The mission provides data that can improve not only air quality forecasts during crisis situations, such as wildfires, but also the atmospheric models used to forecast the daily rhythms of urban pollution. Such models help researchers understand how natural factors such as winds, humidity levels, and air temperatures influence pollution plumes over the course of a day.

In a map of the eastern U.S., elevated ozone concentrations appear as a purple patch in an area extending from New York City to Washington, D.C.
TEMPO detected elevated ozone concentrations in an area extending from New York City to Washington, D.C., at 5:05 p.m. on May 18, 2026.
NASA Earth Observatory/Michala Garrison

TEMPO also detects ozone directly, but determining how much of that ozone is near the surface versus higher in the atmosphere can be challenging. Most of Earth’s ozone resides in the stratosphere, well above the troposphere, where people live and breathe. At times, however, stratospheric ozone can be transported downward into the troposphere. During events known as stratospheric intrusions, it can even descend far enough to affect air quality at the surface and add to the ozone produced at ground level.

By combining TEMPO observations with other sources of information, researchers are studying the processes that influence the distribution of ozone vertically in the atmosphere. On May 18, NASA’s ground-based tropospheric lidar network (TOLNet) in New York City recorded high concentrations of ozone near the surface, indicating that TEMPO was detecting mostly surface-level ozone associated with urban emissions and not ozone aloft, said Mahmoud.

However, on May 19, the same sensor observed a layer of ozone descending from above 5 kilometers (3 miles), he added, a clue that some of the ozone TEMPO detected that day may have originated in the stratosphere. “This is the type of information that leads to better air quality forecast models and more accurate alerts,” Mahmoud said. “Alerts can affect tens of millions of people and lead to disruptions in school, sports, and other activities, so it’s essential that they be as accurate as possible.”

On June 6, New York authorities issued another health advisory for ozone. People interested in following the event can access daily near-real-time TEMPO observations of ozone, nitrogen dioxide, and other gases on NASA’s Worldview browser, on an interactive Harvard & Smithsonian Center for Astrophysics browsing tool, and on NASA’s Earthdata portal.   

NASA Earth Observatory images by Michala Garrison, using TEMPO data from NASA Earthdata. Story by Adam Voiland.

Downloads

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.

Nitrogen Dioxide – May 18, 2026 7:05 AM EDT

JPEG (2.52 MB)

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.

Nitrogen Dioxide – May 18, 2026 3:05 PM EDT

JPEG (2.25 MB)

In a map of the eastern U.S., elevated ozone concentrations appear as a purple patch in an area extending from New York City to Washington, D.C.

Ozone – May 18, 2026 5:05 PM EDT

JPEG (2.10 MB)

References & Resources

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The post Air Pollution’s Daily Pulse Over the Northeast appeared first on NASA Science.

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Air Pollution’s Daily Pulse Over the Northeast

✇NASA




7:05 am
3:05 pm

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison
A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.
TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor. The instrument detected lower levels of the gas at 3:05 p.m. (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon.
NASA Earth Observatory/Michala Garrison

7:05 am

3:05 pm


TEMPO detected high concentrations of nitrogen dioxide during the morning commute at 7:05 a.m. local time on May 18, 2026 (left), along the New York-Washington corridor.The instrument detected lower levels of the gas at 3:05 p.m. EDT (right), after chemical reactions involving nitrogen dioxide had contributed to elevated ozone concentrations in the afternoon. NASA Earth Observatory images by Michala Garrison.

More than 35 million people live along the New York–Washington corridor and breathe the region’s air. While air quality has improved significantly in recent decades, outbreaks of ground-level ozone remain common, particularly in the warm summer months, when the chemical reactions that produce the pollutant accelerate and stagnant air allows ozone to accumulate.

A reminder of this seasonal phenomenon came earlier than usual in 2026, when a mid-May heat wave prompted the New York State Department of Health and the New York Department of Environmental Conservation to issue a health advisory on May 17 over concerns about ozone. The code orange advisory warned young people, older adults, and those working or exercising outdoors to limit activity due to ozone’s respiratory and cardiovascular health impacts.

As expected, ground-based air-quality sensors operated by state and federal agencies showed ozone reaching unhealthy levels for sensitive groups on May 18, something that typically happens several times per year. Meanwhile, NASA’s TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument observed the event from geostationary orbit 22,000 miles (35,000 kilometers) above the equator, a unique vantage point that allows the sensor to collect frequent observations of air pollution.

TEMPO detects nitrogen dioxide (NO2), a gas emitted by burning fuels, particularly by motor vehicles, that contributes to ozone formation. “There’s often a clear and interesting pattern in TEMPO’s nitrogen dioxide data during ozone alert days,” said Hazem Mahmoud, an atmospheric scientist at NASA’s Atmospheric Science Data Center at Langley Research Center. “We see high concentrations of nitrogen dioxide during the early morning commute that drop off sharply in the late afternoon as ozone increases.”

The decline occurs as sunlight fuels photochemical reactions involving nitrogen dioxide, volatile organic compounds, and oxygen that lead to ozone formation. By late afternoon, these reactions deplete much of the available nitrogen dioxide, slowing ozone production until the cycle begins again the next day.

The pair of images above underscores the pattern. The image on the left was acquired at 7:05 a.m. local time when nitrogen dioxide concentrations were high during the morning commute. By 3:05 p.m. (right), most of the nitrogen dioxide had declined substantially, and surface ozone levels were elevated (below). Meanwhile, afternoon sea breezes appear to have transported the remaining nitrogen dioxide slightly to the west. Note that the data shown is provisional, and processing methods are still being refined.

Sensors on earlier polar-orbiting satellites, such as OMI (Ozone Monitoring Instrument) and TROPOMI (Tropospheric Monitoring Instrument), sampled nitrogen dioxide over New York once per day. After its launch in 2023, TEMPO began providing data every hour, allowing researchers to track the evolution and dispersion of air pollution at much finer time scales. 

“TEMPO is helping fill data gaps between ground stations and allowing us to ask new questions,” Mahmoud said. The mission provides data that can improve not only air quality forecasts during crisis situations, such as wildfires, but also the atmospheric models used to forecast the daily rhythms of urban pollution. Such models help researchers understand how natural factors such as winds, humidity levels, and air temperatures influence pollution plumes over the course of a day.

In a map of the eastern U.S., elevated ozone concentrations appear as a purple patch in an area extending from New York City to Washington, D.C.
TEMPO detected elevated ozone concentrations in an area extending from New York City to Washington, D.C., at 5:05 p.m. on May 18, 2026.
NASA Earth Observatory/Michala Garrison

TEMPO also detects ozone directly, but determining how much of that ozone is near the surface versus higher in the atmosphere can be challenging. Most of Earth’s ozone resides in the stratosphere, well above the troposphere, where people live and breathe. At times, however, stratospheric ozone can be transported downward into the troposphere. During events known as stratospheric intrusions, it can even descend far enough to affect air quality at the surface and add to the ozone produced at ground level.

By combining TEMPO observations with other sources of information, researchers are studying the processes that influence the distribution of ozone vertically in the atmosphere. On May 18, NASA’s ground-based tropospheric lidar network (TOLNet) in New York City recorded high concentrations of ozone near the surface, indicating that TEMPO was detecting mostly surface-level ozone associated with urban emissions and not ozone aloft, said Mahmoud.

However, on May 19, the same sensor observed a layer of ozone descending from above 5 kilometers (3 miles), he added, a clue that some of the ozone TEMPO detected that day may have originated in the stratosphere. “This is the type of information that leads to better air quality forecast models and more accurate alerts,” Mahmoud said. “Alerts can affect tens of millions of people and lead to disruptions in school, sports, and other activities, so it’s essential that they be as accurate as possible.”

On June 6, New York authorities issued another health advisory for ozone. People interested in following the event can access daily near-real-time TEMPO observations of ozone, nitrogen dioxide, and other gases on NASA’s Worldview browser, on an interactive Harvard & Smithsonian Center for Astrophysics browsing tool, and on NASA’s Earthdata portal.   

NASA Earth Observatory images by Michala Garrison, using TEMPO data from NASA Earthdata. Story by Adam Voiland.

Downloads

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.

Nitrogen Dioxide – May 18, 2026 7:05 AM EDT

JPEG (2.52 MB)

A map of morning nitrogen dioxide shows elevated concentrations of the gas over a region stretching from New York City to Washington, D.C., at 7:05 a.m. on May 18, 2026. A second map of the same area shows much lower concentrations that afternoon.

Nitrogen Dioxide – May 18, 2026 3:05 PM EDT

JPEG (2.25 MB)

In a map of the eastern U.S., elevated ozone concentrations appear as a purple patch in an area extending from New York City to Washington, D.C.

Ozone – May 18, 2026 5:05 PM EDT

JPEG (2.10 MB)

References & Resources

You may also be interested in:

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Up, Up, and Away With Weather Balloons 

✇NASA Science
By: Christina Campen

By Erica McNamee of NASA’s Goddard Space Flight Center in Greenbelt, MD 

As a kid, you were always taught to hold tight to balloons—tie them around your wrist, even, to keep them close. This week, a group of a dozen undergraduate students participating in NASA’s 2026 Student Airborne Research Program (SARP), unlearned old balloon-wrangling habits—and let go. 

Their purpose:  to collect data about the atmosphere with the help of a weather balloon. For this fieldwork day, in a windy area on the Texas Gulf Coast, the group released an ozonesonde, a small instrument attached to a weather balloon that measures ozone concentrations as it rises through the atmosphere.   

“I’ve never done any atmospheric fieldwork outside like this, so this has been an awesome new opportunity for me,” said Marin Stevens, SARP intern and chemistry student from the University of Colorado at Colorado Springs.  

With the support of program mentors and faculty, and aboard the University of Houston’s Mobile Air Quality Laboratory (MAQL), the SARP interns participated in every step of the process.  

A large white truck is centered in the image. Stairs lead up to the back end of the truck, up to a currently closed door. The truck has a label on the back saying “University of Houston Mobile Air Quality Laboratory”. Two people are standing to the right of the image. Dark clouds billow in the background of the image.
The Mobile Air Quality Laboratory (MAQL) stands ready for some fieldwork.
NASA/Erica McNamee

Let’s set the scene: About 45 minutes away from this year’s SARP home base at the Lone Star Flight Museum in Houston, the MAQL drove down the Texas City Dike, a long strip of land jutting into the Texas Gulf Coast—an open space perfect for releasing the weather balloon. Among the 45 total students in the program, this group was assigned to meet the mobile lab at the balloon release location at  8 a.m. Central Time, just after a rainstorm rolled through. 

Inside the lab, the students prepared ozonesondes for takeoff, completing the final steps that include measuring and pipetting cathode and anode solutions into the instrument cells attached to the sensor and testing each instrument’s response and baseline. 

Outside the mobile laboratory, gusts of wind made it difficult to keep the balloon still while it was being filled with helium. Four students donned leather gloves to minimize oils from their fingers touching the balloon, which can make it pop faster after deployment. They wrangled the balloon while faculty advisor Travis Griggs, instructional assistant professor of atmospheric science from the University of Houston, filled it.  

The center of the image shows a large white balloon. Beneath the balloon are five people in a circle, gently holding at the bottom of the balloon to keep it upright. In the background of the image, other people watch.
SARP students prepare the weather balloon for takeoff by filling it up with helium, while making sure it doesn’t pop or fly away.
NASA/Sofie Bates

Five students worked together to keep the balloon from flying away as they strung it to the ozonesonde, protected in a small foam container. 

After confirming the launch location, time, and expected flight path with the Federal Aviation Administration (just a phone call away), it was time to set the weather instrument free.   

Four students stand in a line centered in the image facing away from the camera. They’re looking up at the sky — a dark and cloud filled sky — and standing on dark green grass. One student, second from the left, points up at the sky.
Even though it’s hard to see, there’s a tiny dot up in the sky—that’s the weather balloon! These students are admiring their work, watching their science experiment float up into the atmosphere to collect data.
NASA/Sofie Bates

“It was like a fight to stay on the ground, with the balloon whipping around in the wind,” said Zayna Haider, SARP intern and atmospheric and climate science student from the University of Washington. “It just flew away as soon as we let it go.” 

But the work had only just begun. The students huddled back inside the MAQL, monitoring data as long as the balloon kept sending it—until it popped or landed, whichever came first. Throughout the day, they repeated this process with two other balloons.  

In the background of the image is a computer screen showing science data in green, orange, and red colors. Two students, in the foreground of the image, are looking at the screen, while a woman stands in front of it, looking at the students and pointing at the screen.
Students inside the Mobile Air Quality Laboratory reviewed the data from the weather balloon they had just launched.
NASA/Erica McNamee

This day gave many SARP participants, just starting their science careers, their first glimpse at hands-on fieldwork that reached the edges of the atmosphere. Next, they would use this data and what they learned from the experience for their end-of-internship research presentation.  

“This is beyond what I ever thought it could be,” Stevens said. “I’m super excited for the next seven weeks.” 

The post Up, Up, and Away With Weather Balloons  appeared first on NASA Science.

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Suicidal Men Don’t Need More Feminism

✇The Daily Sceptic
By: Bettina Arndt

Seven men die by suicide in Australia every day. Last month, the peak body for men's health decided to address this by teaching boys about gender equity. But men are not dying for lack of feminism, says Bettina Arndt.

The post Suicidal Men Don’t Need More Feminism appeared first on The Daily Sceptic.

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The next AI breakthrough won’t come from bigger models, but from better data

✇InfoWorld

Artificial intelligence does not advance at the same pace across industries. It presses forward in some directions while lagging behind in others.

Spend time with today’s most advanced AI applications, and this contrast becomes obvious. In software development, AI is quickly becoming ubiquitous. It writes production-ready code, explains obscure libraries, and iterates at a pace human teams have difficulty matching.

But place that same AI model inside a complex customer support workflow or ask it to reason through a nuanced clinical scenario, and the cracks begin to show. Multi-step reasoning falters. Context gets lost. Performance drops in ways that can seem inconsistent with the model’s strengths elsewhere.

These AI models are often similar. They run on similar hardware and are often trained in similar ways. So why the mismatch in performance across tasks? The simplest explanation is also the most overlooked: data.

Software engineering benefits from an immense, structured, and highly visible digital record. Code is written in standardized languages, benefits from robust documentation, is reviewed in public forums, and is discussed at scale. That ecosystem has generated a robust and massively useful pool of training material.

Other fields often do not. For example, healthcare data is scattered across institutions, wrapped in privacy constraints, expressed in multiple modalities, and rarely ready out-of-the-box for AI training. Enterprise workflows are captured in internal systems that were never designed for training AI. Multilingual speech data varies widely in quality and representation.

This imbalance creates what I describe as “the data gap.” This is the distance between what models are capable of in theory and what they can achieve in practice, because the right data does not yet exist in usable form. Closing this data gap may be the most important—and least glamorous—challenge in AI today.

The missing pillar of AI progress

Three forces are driving the recent advances in AI: the models, the chips, and the data.

On the AI models, the field has invested heavily. Major research organizations employ thousands of researchers and scientists who are actively refining architectures, training techniques, and evaluation methods. Breakthroughs are measured in benchmark scores, conference papers, and model performance on human tasks. On the computing chips, the investment has been equally intense. Hardware manufacturers and infrastructure providers are pouring billions of dollars into building and supporting data centers that deliver faster results via large-scale training.

Yet data has not received the same institutional focus for AI development. Conversations with researchers at frontier AI labs share a similar frustration that today’s model capabilities in key use cases, such as healthcare, are limited less by architectural imagination and more by the availability of high-quality, domain-specific data. The bottleneck is not always a lack of ideas, but a lack of reliable inputs.

We are long past scraping the internet for useful data, and this path does not scale. Progress depends on building and curating datasets that reflect the complexity of true lived experience and organizational processes. That work requires both scientific rigor and research specialization in the field of data for AI.

The dataset behind every leap

The history of AI reinforces a consistent lesson: major leaps in model capability follow major leaps in the availability of quality data. From early vision systems that relied on clearly labeled images to today’s language models trained on massive text collections, each major leap has depended on access to more high-quality data.

Architectural innovation alone is rarely enough. The value of these new approaches only emerges when paired with large, structured, and representative datasets that reveal what the models can actually do in practice. Whether in vision or language, progress has depended on the painstaking work of collecting, organizing, and validating the underlying data.

Large language models illustrate this clearly. Their emergence was not just the result of better training techniques, but of access to an unprecedented volume of data. The models did not generate that data. They relied on it. That pattern raises a pressing question for the present: who is building the next generation of foundational datasets?

Across domains ranging from healthcare to audio to agentic task performance, there is no widely accepted blueprint. What constitutes a gold-standard dataset for training an AI agent to handle complex enterprise tasks? What does a clinically meaningful evaluation look like for a model that will assist in medical decision-making? How should multilingual speech data be curated to ensure broad representation and reliable performance?

These are not simple sourcing problems. They are fundamental research challenges that need to be solved.

When data is treated as a commodity

Too often, consequential data decisions are handled like procurement exercises. An organization requests “medical conversations” or “wildlife scenes,” and the request is routed to internal procurement or data sourcing teams, or to external data vendors, who assemble data that appears to match the description. The implicit assumption is that data is interchangeable, that one dataset is as good as another so long as it meets a basic specification.

Actual application suggests otherwise. Seemingly small choices about factors such as inclusion criteria, annotation standards, filtering rules, and validation protocols can dramatically alter downstream performance. Data design shapes model behavior as much as architecture does.

Three structural issues compound the problem:

  • Capacity: There are relatively few specialized teams dedicated to building domain-specific datasets at the highest level of rigor. Talent and funding have gravitated toward model development and hardware innovation. Data work often operates in the background, even though it underpins both.
  • Design: Constructing a dataset is a distinct discipline from designing a neural network. It requires expertise in experimental design, domain knowledge, and statistical validation. Expecting model researchers to simultaneously shoulder the full burden of data research, while also training and evaluating the models, overlooks the complexity of the upstream task.
  • Translation: The researchers who are requesting specific data sources to improve the models are often not the same people responsible for sourcing that data. As a result, nuances and research-backed expertise can often be missing or diluted as requests pass through layers of procurement and vendor relationships. The result can be data that meets the specification sheet, but in fact fails to advance model performance.

The rise of annotation providers and reinforcement learning services has addressed part of the need. Rating model outputs, labeling text, and evaluating structured information are essential for many optimization tasks. But these activities generate data that is carefully constructed for specific, bounded purposes.

The frontier challenges in AI require more. They demand datasets derived from real human activity and organic organizational processes. Such data is complex, multimodal, and sensitive. It is rarely AI-ready by default. And converting it into reliable training and evaluation material is a scientific undertaking.

The need for scientific rigor for the AI data layer

If high-quality data is a central bottleneck, then scientific rigor is part of the solution. Just as leading model-builders have dedicated research labs and hardware has dedicated development ecosystems, the data layer for AI requires focused, scientifically-grounded institutions.

This means engaging directly with core questions like dataset design, evaluation methodology, and quality control. The conversation cannot end at volume; it must address data structure, representativeness, and expert validation.

Dataset construction must be approached as experimental design. Protocols must be documented and validated. Evaluation frameworks must test whether the dataset truly reflects the intended applications.

The field also requires standards and benchmarks that reflect real-world complexity, not simplified proxies. In healthcare, for instance, evaluating a system intended for clinical assistance with generic question-and-answer tests is insufficient. Real-world clinical environments involve multimodal inputs and contextual judgment. Benchmarks must reflect that reality if they are to function as meaningful gates before deployment.

Quality measurement is another crucial frontier. Finance relies on standardized metrics such as credit scores to assess risk. AI lacks an equivalent for datasets and benchmarks. Developing clear methodology to quantify dataset quality and evaluation reliability brings clarity to model assessment.

The criteria for evaluating a multilingual audio library will differ from those of a multimodal oncology dataset. Yet the underlying principle remains constant. Better models require better-defined, better-measured data.

The risks of getting it wrong

As AI systems move closer to high-stakes deployment, weak data practices carry tangible risks.

Benchmarks cannot be created with the same data that is used for training—that’s giving the test answers to the model ahead of time. Scaling data volume without prioritizing data quality and selection diminishes model performance gains, and can even bias against or omit underrepresented populations. These are methodological challenges, and ones that must be solved.

The rigor required at the data layer may not attract headlines. It does not typically lend itself to dramatic product launches. Yet the data layer for AI is foundational to trust, safety, and sustained progress for all AI progress.

An ecosystem for the data era

No single organization can resolve the data gap alone. What is needed is an ecosystem of AI data labs and research groups, each focused on different domains and challenges but united by a commitment to scientific discipline. These institutions would collaborate with model researchers and domain experts who would tackle challenges such as dataset contamination, factuality, groundedness, de-identification, international representation, and bias. They would design benchmarks that mirror real-world complexity rather than simplified abstractions.

AI’s trajectory will not be determined solely by larger models or faster chips. It will be shaped by the datasets we construct, the standards we adopt, and the rigor we apply at the foundation. The uneven frontier we see today reflects an uneven data landscape. Bridging the gap requires deliberate, research-driven dataset design.

If we want AI systems capable of operating reliably in clinical contexts, navigating enterprise workflows, and functioning responsibly across languages and cultures, we must treat data for AI as a first-class scientific endeavor.

AI models have their research labs. AI chip-builders have their fabrication plants. AI data needs institutions of equal seriousness and ambition.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

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NASA-Funded Study Shows Wildfire Smoke’s Hidden Ozone Toll

✇NASA
5 Min Read

NASA-Funded Study Shows Wildfire Smoke’s Hidden Ozone Toll

Canadian wildfire smoke carried carbon monoxide — a building block of ground-level ozone — thousands of miles downwind in June 2023.
Credits:
NASA’s Goddard Space Flight Center

Wildfire smoke is stoking a new challenge for cleaner air. A NASA-supported study published Thursday found that, over the last decade, wildfires have worsened ground-level ozone pollution across much of the contiguous United States, creating unhealthy air far from active flames.

Wildfires have become an increasingly important contributor to ground-level ozone, or smog, across much of the United States, researchers report June 4 in the journal Science. Nationally, fires offset nearly four years’ worth of ozone-control gains, with larger setbacks in the West and Midwest.

Smoke often is associated with the soot, ash, and other fine particles that make the air look hazy. But wildfires also emit gases such as carbon monoxide, which can help form surface ozone in sunlight when other pollutants are present. Surface ozone is an invisible pollutant harmful to human health, plants, and crops. As smoke plumes travel and mix with other pollution, those reactions can drive ozone increases hundreds or even thousands of miles from active fires.

“NASA Earth observations, along with ground monitoring networks, help reveal air quality risks from wildfires that can cross state lines, giving air quality managers better decision-making information as wildfire smoke affects more communities,” said John Haynes, manager of NASA Earth Action’s Health and Air Quality program at the agency’s Headquarters in Washington. “This is a strong example of NASA science serving communities here in the U.S.”

Building a clearer ozone picture

High in the atmosphere, ozone shields Earth from harmful ultraviolet radiation. Near the ground, however, ozone can irritate lungs, worsen asthma and other respiratory diseases, and increase health risks for children, older adults, outdoor workers, and people with existing health conditions.

To track surface ozone changes, researchers turned to deep learning, a form of artificial intelligence that finds patterns across large datasets. They used it to build a first-of-its-kind dataset estimating daily surface ozone from 2003 to 2024 on a kilometer-by-kilometer grid — about 0.6 miles on each side — across the contiguous U.S. The work received support from NASA’s Health and Air Quality program and other NASA grants.

The scientists combined data from about 1,000 ground-based air quality stations with atmospheric model data, weather information, wildfire pollution data, and satellite-derived information, including products from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments.

Smoke from Canada’s 2023 wildfires spread across North America. Tan to deep red colors show smoke intensity, estimated from black carbon in NASA’s GEOS-FP model.
NASA’s Scientific Visualization Studio (SVS) and NASA’s Global Modeling Assimilation Office (GMAO)

Their analysis revealed two distinct periods. From 2003 to 2015, U.S. ground-level ozone generally declined as emissions of ozone-forming pollutants decreased. After 2015, however, those gains slowed or reversed in many places. By comparing estimated ozone levels with scenarios that removed wildfire influence, the researchers found that pollution from wildfires was a main factor in that shift.

Without the wildfire contribution, ground-level ozone in the Midwest, for example, would likely have continued to decline. Instead, wildfires erased about 5.3 years’ worth of ozone-control progress since 2015.

“People in the Midwest may think fires burning far away will not affect them,” said the study’s corresponding author Jun Wang, an atmospheric scientist at the University of Iowa in Iowa City. “But once wildfire pollution is in the air, it can move across regions. Pollution from one place can affect air quality in another.”

Measuring the health toll

The study also found that wildfire-driven ozone increased exposure to unhealthy air and likely contributed to premature deaths. Premature deaths associated with long-term wildfire-related ozone exposure in the U.S. increased by an estimated 318 deaths per year after 2013, with the post-2013 average 46% higher than in the previous decade. The researchers calculated premature deaths using average lifespan, ozone exposure estimates, and population density.

The 2023 Canadian wildfires showed how widely those risks can spread, with smoke-driven ozone increases stretching across the Midwest and into parts of the Northeast and South. Overall, from 2022 to 2024, wildfires exposed an additional 43 million people in the U.S. to conditions that did not meet current federal air quality standards for ozone, the researchers estimated.

Capturing that national picture is difficult from ground monitors alone. Ground monitors remain the backbone of U.S. air quality tracking, but they do not cover every community. NASA’s scientifically validated satellite observations and models help researchers and agencies see air quality patterns across states, regions, and fire seasons.

That broader air quality work includes newer missions such as TEMPO (Tropospheric Emissions: Monitoring of Pollution). Launched in 2023, TEMPO is NASA’s first mission to use a space-based spectrometer to provide hourly daytime measurements of air quality over North America. Its view is sharp enough to distinguish pollution patterns, including surface ozone, across areas only a few square miles wide, a major improvement over earlier satellites.

Together, these capabilities help researchers and agencies see smoke-related ozone patterns that might otherwise be harder to detect, especially in rural and remote areas.

The work also points toward a practical use of NASA science during fire season. Wang’s team has used NASA support to develop FireAQ, a decision-support system that brings satellite observations, model forecasts, and fire and aerosol products into weekly briefings with state and local air quality officials. The goal is to help officials see where smoke-related pollution may move next and give communities better information.

About the Author

Emily DeMarco

Emily DeMarco

Writer/Editor (IV), Earth Science Division

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Last Updated
Jun 05, 2026
Contact
Emily DeMarco
Location
Goddard Space Flight Center
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‘Happiness is not just about GDP’: ambitious plan or utopia?

✇The Guardian - Energy
By: Jonathan Watts

Some will question its credibility – but the alternative future to the one imagined in the Global Justice Report is far more bleak

In our increasingly dystopian world, who wouldn’t want to at least be open to a utopian antidote? The Global Justice Report, published on Thursday, outlines how to build a prosperous, equitable world within safe planetary boundaries. It’s a push from the modern eco-socialist left in a global battle for ideas that will shape the future.

Based on past social achievements and future energy transformation, it indicates that the overwhelming majority of people on the planet could, by the end of the century, work less and earn more – while keeping temperatures down and avoiding much of the current destruction of nature. It is an ambitious, comprehensive and upbeat plan, and a stronger argument around which to build a political campaign than abstract goals of net zero or decarbonisation.

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© Photograph: Emmanuel Croset/AFP/Getty Images

© Photograph: Emmanuel Croset/AFP/Getty Images

© Photograph: Emmanuel Croset/AFP/Getty Images

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