What makes MISR extraordinary among Earth-observing instruments is its geometry. Flying on Terra, the flagship spacecraft of NASA’s Earth Observing System, the push-broom imager has been collecting radiance data since late February 2000, viewing the planet at nine angles ranging from straight down to 70.5 degrees fore and aft along the flight path, in four spectral bands centered at 446, 558, 672, and 866 nanometers. Because aerosol particles scatter and absorb light differently depending on the angle of observation, this multi-angle approach can constrain particle size, shape, and light-absorbing properties in ways that single-view imagers simply cannot. The instrument covers the globe roughly once a week, and even though Terra began constellation exit procedures in October 2022, MISR leaves behind a legacy of more than 24 years of multi-angle, multi-spectral observations of the entire planet.

The new MRPA is the culmination of a long-running parallel effort. NASA’s operational MISR Standard Algorithm, currently at version 23, was designed from the start to run automatically on the entire dataset, producing aerosol retrievals at 4.4 kilometer resolution. Alongside it, the research team built the MISR Research Algorithm, a more experimental code designed to push the sensitivity of the retrieval further, exploring innovations such as corrections for radiometric calibration errors like flat-field anomalies and ghosting in high-contrast scenes, and improved performance over shallow, turbid, and biologically productive waters. The catch was that the research algorithm demanded considerable hands-on user involvement and could only be run case by case, on limited subsets of data. The MRPA changes that equation by streamlining and automating the research approach so that any user can generate research-quality results across most of the MISR data record, spanning February 2000 through October 2022.

At the heart of the algorithm lies a clever dual strategy for dealing with the hardest problem in satellite aerosol retrieval: separating the signal of particles in the air from the light reflected by the ground below. The Retrieved-Surface Algorithm solves for surface reflectance self-consistently along with the aerosol properties, using a shape-similarity assumption in which the angular shape of surface reflectance is treated as independent of wavelength. In the updated MRPA, the shape-similarity coefficient is now calculated exclusively from the near-infrared band, because near-infrared light penetrates the atmosphere more reliably than the shorter blue and green wavelengths, giving better constraint on the surface. The Prescribed Surface Algorithm, by contrast, takes surface brightness from an external dataset, the Multi-Angle Implementation of Atmospheric Correction product derived from NASA’s MODIS instrument, and then solves for aerosol quantities directly using a bounded-variable least squares approach. This prescribed-surface route excels precisely where the retrieved-surface method struggles, namely in high-aerosol-optical-depth cases where the thick haze itself makes it hard to pin down what the surface is doing.

The team blends the two approaches into a Combined Surface Algorithm, weighting the contributions of each according to the Normalized Difference Vegetation Index of the underlying terrain. Bright, barren landscapes such as deserts are where small errors in prescribed surface reflectance can balloon into large aerosol errors, so the new weighting scheme, tuned against an extensive validation dataset, leans more heavily on the retrieved-surface results in low-vegetation scenes. The payoff is striking: in scenes where the prescribed-surface NDVI falls below 0.1, the new combined algorithm shows a root-mean-square error 45 percent lower than the previous version, with a 43 percent lower bias. The MRPA also introduces new cloud masking interpolated to the MISR swath from the MAIAC cloud mask over land, and over deep ocean it compares 12-micrometer brightness temperatures from MODIS-Terra against MERRA-2 reanalysis sea surface temperatures, flagging pixels that run 7.5 kelvin warmer as cloudy. Column ozone for absorption corrections now comes from MERRA-2 as well, and ocean surface roughness is prescribed from Cross-Calibrated Multiplatform wind speeds.

Validation against ground truth is where the new product truly shines. The researchers compared the retrievals against the AErosol RObotic NETwork, or AERONET, a global federation of calibrated sun photometers whose direct-sun aerosol optical depth measurements carry an uncertainty of about 0.01. Using more than 75,000 over-land coincidences between February 2000 and October 2022, the combined algorithm achieved an AOD root-mean-square error of 0.088, compared with 0.154 for the MISR V23 standard product, roughly half the error, along with a correlation coefficient of 0.938 versus 0.81. The bias of 0.008 is somewhat higher than the standard product’s −0.002, one of the compromises of automation, but the overall statistical improvement is substantial. Over water, where more than 29,000 coincidences were available, the algorithm performed even better, with an RMSE of 0.069 and a correlation of 0.923, and the expected error envelope works out to roughly 17 percent of the retrieved AOD plus 0.01 over land.

Particle-type information, the real prize of multi-angle imaging, is harder to validate quantitatively because the ground-based inversion retrievals of size, sphericity, and single-scattering albedo carry their own assumptions and limitations, particularly at low aerosol load. Still, the Ångström exponent, a robust measure of particle size derived from how aerosol optical depth varies with wavelength, shows two clear clusters in the validation data, around 0.25 and 1.5, corresponding to coarse-mode dust and fine-mode smoke or pollution respectively, and the agreement with AERONET improves as aerosol loading increases, with RMSE dropping from 0.43 to 0.25 at higher AOD thresholds. When the team examined regions and seasons dominated by a single aerosol source, the retrievals behaved exactly as physics demands. Smoke-dominated scenes, once the mid-visible AOD exceeded roughly 0.2 over water and 0.7 over land, showed a preponderance of small-to-medium, spherical, light-absorbing particles with single-scattering albedo between about 0.9 and 0.975. Dust-dominated regions near the Sahara in late spring and early summer yielded distinctly larger, weakly absorbing, non-spherical particles, with the coarse-mode signal especially clear over the high-contrast backdrop of dark ocean water.

The research algorithm behind the MRPA has already proven its worth in a remarkable range of scientific applications, from characterizing the properties and evolution of wildfire smoke plumes across North America, Alaska, and Siberia, to tracking volcanic eruption plumes including the catastrophic 2022 Hunga Tonga-Hunga Ha’apai event, to air quality assessments and studies of how dust particles age and change during trans-Atlantic transport. With the algorithm now automated, publicly released, and archived for general use, researchers anywhere can run it on any portion of the MISR archive that interests them, opening the door to systematic studies of how particle properties vary and evolve across the entire 22-year record rather than only in hand-picked case studies. The code’s availability marks a shift from a specialist tool requiring expert knowledge to a community resource.

The broader significance of this work lies in what it preserves and what it promises. As single-view instruments like MODIS have built their own long aerosol records, MISR’s multi-angle perspective offers a complementary and uniquely information-rich dataset, one that can distinguish, for example, a plume of absorbing smoke from a layer of scattering desert dust even when both produce similar optical depths. The compromises required to automate the research algorithm do come at a cost, with somewhat diminished particle-type sensitivity compared to the fully manual version, and the MRPA is currently configured to process data only through October 2022 to maintain continuity with its calibration corrections, though new Level 1 radiance products accounting for Terra’s orbit drift are planned. Even so, with about half the retrieval error of the operational standard product, four times finer pixel resolution, and demonstrated ability to separate smoke from dust by size, shape, and absorption, the MRPA hands atmospheric scientists a powerful new lens on the invisible particles that shape our climate and our air, and it ensures that the full scientific value of one of NASA’s most inventive instruments will be realized for years to come.

Subject of Research: Automated multi-angle satellite retrieval of global aerosol optical depth and particle properties from MISR

Article Title: The MISR Research Product Algorithm – producing global, self-consistent, pixel-level aerosol retrievals for the Multi-angle Imaging SpectroRadiometer

Article References: Anstett, M., Limbacher, J. A., & Kahn, R. A. (2026). The MISR Research Product Algorithm – producing global, self-consistent, pixel-level aerosol retrievals for the Multi-angle Imaging SpectroRadiometer. Atmospheric Measurement Techniques, 19(20), 6503-6516. https://doi.org/10.5194/amt-19-6503-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6503-2026

Keywords: MISR, aerosol retrieval, Terra satellite, NASA, AERONET, aerosol optical depth, remote sensing, wildfire smoke, mineral dust, air quality, atmospheric measurement, multi-angle imaging

Fine particulate matter, known scientifically as PM2.5, refers to particles smaller than 2.5 micrometers in diameter, roughly one-thirtieth the width of a human hair. Because of their tiny size, these particles can bypass the body’s natural defenses in the nose and throat, penetrate deep into the lungs, and even cross into the bloodstream, where they damage the heart, blood vessels, and other vital organs. For decades, PM2.5 has been recognized as the leading environmental risk factor for cardiopulmonary disease, and regulatory efforts under the Clean Air Act drove steady improvements in US air quality through the early 2000s. But that progress has begun to reverse. In extreme smoke years, wildfire emissions have accounted for more than half of the annual PM2.5 concentration in many parts of the country, and climate change is expected to expand the extent of global fire-prone areas by roughly 29 percent by the late twenty-first century.

The research team, led by investigators at the Harvard T.H. Chan School of Public Health, drew on the State Inpatient Databases, a collection of hospital discharge records that represents more than 95 percent of all inpatient admissions nationwide. They analyzed 57,264,966 hospitalizations for cardiovascular diseases and 32,691,394 for pulmonary diseases among residents of 20 states between 2006 and 2019, including heavily fire-affected states such as California, Oregon, and Washington. By linking each patient’s residential ZIP code to sophisticated spatiotemporal models of ambient pollution, the researchers estimated individual exposure to both wildfire-specific PM2.5 and non-wildfire PM2.5, then examined how two-year average concentrations related to the risk of hospital admission.

A central methodological innovation was the use of a self-controlled study design, an extension of the case-crossover approach traditionally reserved for short-term effects. Instead of comparing different people, the design compares each hospitalized individual with themselves during control years selected both before and after the hospitalization year. This structure inherently adjusts for time-invariant confounders such as genetic susceptibility, smoking history, and educational attainment, factors that are notoriously difficult to measure but can distort conventional epidemiological comparisons. The researchers additionally adjusted for measured time-varying covariates, including calendar year, neighborhood-level socioeconomic indicators, and seasonal temperature statistics, and applied Bonferroni-corrected confidence intervals to guard against false-positive findings across the many disease-outcome combinations tested.

The results were striking. For every 1-microgram-per-cubic-meter increase in two-year average wildfire-specific PM2.5, the risk of hospitalization rose by 13.1 percent for cerebrovascular disease, 12.9 percent for ischemic heart disease, 12.2 percent for hypertension, 11.7 percent for arrhythmia, and 10.0 percent for heart failure. Among pulmonary conditions, asthma showed the strongest association, with a 16.0 percent increase in hospitalization risk per unit of wildfire PM2.5, followed by acute respiratory infections and pneumonia. Non-wildfire PM2.5 was also associated with elevated risks across all examined conditions, but the per-unit effect sizes were consistently smaller, ranging from roughly 5.2 to 8.5 percent. In other words, when the same increment of pollution comes from a wildfire rather than a tailpipe or smokestack, the body appears to pay a substantially higher price.

This per-unit comparison, however, tells only part of the story. Because non-wildfire PM2.5 concentrations were approximately 20 to 27 times higher than wildfire-specific concentrations during the study period, the risk increase per standard deviation of exposure, a measure that better reflects real-world variation, was actually greater for non-wildfire sources. The average annual wildfire-specific PM2.5 concentration across the studied ZIP codes was just 0.29 micrograms per cubic meter, compared with 7.78 micrograms per cubic meter for non-wildfire PM2.5. California recorded the highest levels of both pollutant types. The researchers emphasize that this distinction matters for policy: wildfire smoke is currently more toxic per unit, but traditional sources still dominate the overall pollution burden, meaning both require attention.

Why would wildfire-derived particles be more dangerous? The answer likely lies in their unique chemical and physical composition. Wildfire-specific PM2.5 contains a higher proportion of submicron and ultrafine particles, elevated levels of elemental and organic carbon, and greater concentrations of polar organic compounds, including oxidative and pro-inflammatory substances such as polycyclic aromatic hydrocarbons. Because these particles are smaller, they penetrate more deeply into the respiratory tract and bloodstream. Toxicological experiments support the epidemiological findings: mice exposed to wildfire-derived particulate matter at doses equivalent to ambient levels developed stronger pulmonary inflammation and histological lung damage than mice exposed to comparable doses of PM from other sources, and exposure to particles from smoldering peat fires has been shown to trigger lung inflammation, reduced cardiac function, and post-ischemic myocardial infarction in animal models.

Perhaps the most consequential finding involved exposure duration. When the researchers tested alternative exposure windows using three- and four-year moving averages, the association between wildfire-specific PM2.5 and cardiopulmonary hospitalizations grew stronger with longer exposure, while the association with non-wildfire PM2.5 remained relatively stable. This pattern suggests that wildfire smoke exerts cumulative toxic effects that build up in the body over time, a mechanism consistent with prior evidence that chronic exposure to air pollutants poses substantially larger risks than acute exposures lasting only days. It also helps explain why earlier studies, which typically examined short-term wildfire episodes within a week of exposure, produced inconsistent and often null results for cardiovascular outcomes. One prior study found that cardiovascular effects of wildfire-related PM2.5 emerged on lag day 10, considerably later than the pulmonary effects, which appeared immediately.

The burden of this pollution is not distributed equally. Hispanic individuals and those from other racial and ethnic minority groups experienced greater cardiopulmonary hospitalization risks from both pollutant types, as did residents of metropolitan areas, communities with lower educational attainment, and communities with higher deprivation levels as measured by the Townsend Deprivation Index, which combines unemployment, household overcrowding, and lack of car and home ownership. Individuals with co-existing obesity or diabetes also faced heightened susceptibility, with obesity generally associated with stronger effects, possibly because obesity involves reduced cardiopulmonary function, chronic systemic inflammation, and elevated oxidative stress. The researchers attribute the racial and socioeconomic patterns to structural inequities, including poorer healthcare access, higher baseline pollution burdens, and fewer resources to mitigate smoke exposure. Metropolitan vulnerability may reflect interactions with other urban pollutants such as nitrogen dioxide and ozone, as well as social stress pathways that promote chronic inflammation.

The implications for air quality policy are profound. Current regulatory frameworks largely exempt wildfire smoke from determining attainment of the National Ambient Air Quality Standards, and traditional wildfire management approaches such as controlled burns have historically prioritized property protection over minimizing population smoke exposure. Under climate change scenarios, annual wildfire-specific PM2.5 concentrations in some US regions could reach 10 micrograms per cubic meter, levels that would rival or exceed today’s non-wildfire pollution. The study’s authors argue that wildfire management deserves far greater emphasis alongside conventional air quality control, and that the persistent, cumulative nature of smoke’s health effects demands long-term protective strategies rather than episodic responses. With wildfire now the fastest-growing source of ambient air pollution in the United States, the era of treating smoke as an unavoidable natural hazard, rather than a modifiable public health threat, may be coming to an end.

Subject of Research: Long-term cardiopulmonary hospitalization risks of wildfire-specific versus non-wildfire fine particulate matter exposure

Article Title: Cardiopulmonary hospitalization risks from wildfire-specific and non-wildfire PM2.5 in 20 US states

Article References: Zhang, M., Castro, E., Qiu, M., Danesh Yazdi, M., Li, B., Wright, R. J., Schwartz, J. D., O. Wright, R., & Wei, Y. (2026). Cardiopulmonary hospitalization risks from wildfire-specific and non-wildfire PM2.5 in 20 US states. Nature Communications, 17(1), Article 9349. https://doi.org/10.1038/s41467-026-76974-7

Image Credits: AI Generated

DOI: 10.1038/s41467-026-76974-7

Keywords: wildfire smoke, PM2.5, air pollution, cardiovascular disease, pulmonary disease, hospitalization risk, asthma, heart failure, climate change, environmental health, epidemiology, health disparities

The core problem TRAILS tackles is what atmospheric scientists call the reception height limitation. Earlier automated source attribution tools, including a widely used framework developed by Martin Radenz and colleagues in 2021, assumed that an air parcel picks up the properties of the surface it passes over only when it travels close to the ground, typically within the planetary boundary layer or below a fixed threshold of two kilometers. That assumption works reasonably well for surface emissions, but smoke plume injection heights are notoriously variable. In extreme cases, fire-triggered thunderstorms known as pyrocumulonimbus can inject smoke into the upper troposphere and lower stratosphere in quantities comparable to volcanic eruptions, where particles may persist for months and circulate globally. A fixed reception height simply cannot capture plumes injected directly into the free troposphere at altitudes of five, eight, or even twelve kilometers.

TRAILS overcomes this by making the reception height dynamic. The framework integrates ten-day backward trajectories simulated with the FLEXPART Lagrangian particle dispersion model, which represents turbulence and atmospheric mixing more realistically than conventional mean-wind trajectory methods, with a multi-sensor satellite detection algorithm. For each altitude level, 500 air parcels are released and tracked at three-hour intervals, producing 40,000 potential parcel positions per level. The tool then calculates a vertically resolved Smoke Occurrence Fraction, or SOF, quantifying the likelihood of smoke influence based on the cumulative residence time of air parcels within smoke-affected regions along their trajectories. A higher fraction of parcels with long smoke plume residence times at a given altitude implies a greater probability of detectable smoke aerosols there.

A key innovation lies in how TRAILS determines where smoke actually sits in the vertical column. The researchers established a new, statistically significant linear relationship between the Ultraviolet Aerosol Index measured by the Ozone Mapping and Profiler Suite Nadir Mapper on the Suomi-NPP satellite and smoke plume height for fresh tropospheric smoke. This relationship was derived by collocating OMPS observations with vertically resolved smoke plume heights from the CALIOP lidar aboard the CALIPSO satellite. The UVAI is a residual parameter that quantifies the difference in spectral dependence between measured and calculated outgoing radiances at the top of the atmosphere. UV-absorbing aerosols such as smoke and dust produce positive UVAI values, while clouds produce nearly zero, making the technique remarkably robust against subpixel cloud contamination. Because absorbing aerosols intercept Rayleigh-scattered radiation from below, the higher the aerosol layer, the greater the amount of molecular radiation it can absorb, producing a nearly linear dependence on layer height that the team confirmed with a coefficient of determination of 0.50 and a p-value below 0.001.

Using this relationship, TRAILS estimates theoretical plume height boundaries for each air parcel position. Rather than assuming parcels acquire smoke properties only near the surface, the method allows the reception height to be vertically dynamic and spatially variable, explicitly defined by the observed UVAI intensity and its corresponding estimated plume altitude. The framework also incorporates active fire data from MODIS on the Aqua and Terra satellites and VIIRS on Suomi-NPP, flagging parcels positioned directly over sufficiently intense fires. Stratospheric smoke identification remains an experimental product, since the linear UVAI-height relationship is not generally valid for aged stratospheric plumes, and ten-day trajectories cannot attribute smoke older than that window. Dust filtering is handled conservatively: parcels passing over Africa or the global dust belt with elevated UVAI values are categorically labeled as dust-influenced and excluded from smoke attribution, a choice that minimizes false positives from the dominant North American smoke source region even if it underestimates smoke influence for trajectories crossing African biomass burning zones.

The team evaluated TRAILS against ground-based measurements from Leipzig during the record-breaking 2023 Canadian wildfire season, which burned over 15.6 million hectares of boreal forest, mixedwood, and peatland across Alberta, British Columbia, Quebec, Ontario, and Saskatchewan. Two complementary lidar systems provided the reference: MARTHA, a Multiwavelength Atmospheric Raman lidar for Temperature, Humidity and Aerosol profiling equipped since 2022 with a laser-induced fluorescence channel, and PollyXT, a portable automated Raman lidar operating continuously. Fluorescence detection is particularly powerful because biogenic and biomass burning aerosols fluoresce when excited by ultraviolet light, while water does not, allowing researchers to study fluorescent aerosols even in aerosol-cloud coexistence. The fluorescence capacity, the ratio of fluorescence backscatter to elastic backscatter at 532 nanometers, is up to fifty times higher for biomass burning smoke than for mineral dust, enabling discrimination between smoke, dust, pollen, and urban aerosols when combined with depolarization measurements.

In case studies from late May and early July 2023, TRAILS effectively reproduced the vertical distribution and temporal evolution of long-range smoke layers. During the 29 May to 2 June period, several fluorescent aerosol layers were detected throughout the troposphere, with the Smoke Occurrence Fraction showing enhanced values exceeding 15 percent on 29 and 30 May that coincided with lidar-observed layer altitudes. Trajectory analysis revealed that smoke-affected parcels originated in the mid-troposphere over the western Atlantic before being lofted within the warm conveyor belt of a mid-latitude cyclone south of Iceland, ascending at roughly one kilometer per day, a characteristic rate for frontal ascent. The July case demonstrated the tool’s capability to identify smoke even in the presence of mixed-phase clouds and coexisting dust, with a dust layer identified at ten kilometers altitude alongside smoke from fires in northern Quebec, underscoring that combining the SOF with lidar depolarization ratios enhances robustness in complex scenes.

Statistical validation against 23 fluorescence capacity profiles revealed a layer detection performance of 76 percent relative to fluorescent aerosol layers, with 62 of 82 fluorescence layers matched within a two-kilometer height tolerance. Matched layers showed a strong positive correlation between mean and peak heights, but TRAILS systematically underestimated layer heights by about 0.4 kilometers on average, with the discrepancy increasing to roughly one kilometer for layers in the upper troposphere and lower stratosphere. This systematic height mismatch provides indirect observational evidence for diabatic self-lofting, a process in which optically thick smoke layers absorb solar radiation, heat the ambient air, and generate buoyancy that causes the layer to ascend, reaching rates of up to one kilometer per day according to CALIOP observations following the 2017 Canadian wildfires. Because FLEXPART’s passive trajectory calculations do not represent post-injection self-lofting, the inferred ascent rate of 0.2 to 0.4 kilometers per day for upper-level layers offers a new observational constraint on this otherwise difficult-to-observe process.

The researchers are careful to note the framework’s limitations. TRAILS identifies where and at what altitudes smoke is present but does not automatically attribute these layers to specific fire sources; in the presented case studies, source attribution was performed by visually comparing the first smoke detection along trajectories with major active fire hotspots. The UVAI-plume height relationship was derived primarily from North American, Russian, and Australian smoke plumes, and its applicability to other fire regimes, such as Southern Hemisphere burns or different fuel types, may require recalibration of thresholds and carries additional uncertainties related to dust contamination. The FLEXPART setup also omits wet deposition, so removal of smoke through precipitation is not represented, and the coarse 50-kilometer spatial resolution of OMPS-NM introduces potential cloud contamination effects that the empirical relationship does not explicitly correct.

Despite these caveats, TRAILS provides a valuable, observationally constrained method for time-height-resolved smoke attribution, particularly at sites lacking advanced fluorescence lidars. The algorithm is computationally efficient and modular, relying on the longest continuous UVAI record available, extending back to 2012 and unaffected by the row anomaly that has degraded OMI observations since 2007. By delivering continuous, automated estimates of smoke influence, the tool can help disentangle the effects of smoke aerosols from other cloud condensation nuclei and ice-nucleating particle sources, enhancing investigations of aerosol-cloud interactions and the long-range transport of biomass burning aerosols. Future developments aim to automate the fire source attribution step, transforming TRAILS from a smoke detection tool into a complete source attribution system, and to extend its application to Southern Hemisphere biomass burning events, including the Australian bushfires of 2019-2020, testing the method’s robustness under different meteorological regimes and fire characteristics.

Subject of Research: TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke

Article Title: TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke

Article References: Roschke, J., Gast, B., Radenz, M., Ansmann, A., Seifert, P., McCosh, G., & Kalesse-Los, H. (2026). TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke. Geoscientific Model Development, 19(20), 9905-9934. https://doi.org/10.5194/gmd-19-9905-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9905-2026

Keywords: wildfire smoke, atmospheric science, remote sensing, lidar, satellite observations, aerosol-cloud interactions, climate modeling, TRAILS, novel, framework, time-height-resolved, attribution

The scale of the exposure is striking. On average, 916 million people each year endured at least one day on which they were simultaneously exposed to heatwave conditions and to air pollution originating from landscape fires, a category that includes wildfires, and the burning of forests, grasslands, and agricultural land. That figure is not static. The analysis found that the number of people experiencing co-exposure grew by 287 million people per decade, meaning that the population caught in the crossfire of heat and smoke expanded dramatically within the study window itself. In total, 55.9 percent of the global population lived through an increasing trend in co-exposure over the twenty-year period.

The intensity of exposure rose alongside its reach. The global annual average of co-exposure days was 1.3 days per person across the full study period, but that average conceals a clear temporal shift: the figure climbed from 1 day per person in 2000 to 2009 to 1.7 days per person in 2010 to 2019. That near-doubling within a single decade suggests that the mechanisms driving joint exposure are accelerating rather than plateauing. Researchers measure these exposures using person-days, a metric that combines the number of affected individuals with the duration of their exposure, which allows comparisons across countries of very different population sizes and fire climates.

The geography of the burden is uneven in ways that carry significant implications for public health policy. The five countries with the highest annual averages of person-days of co-exposure were the Democratic Republic of the Congo, Indonesia, Mexico, Brazil, and the Russian Federation. This list spans tropical, temperate, and boreal fire regimes, underscoring that the problem is not confined to any single biome or latitude. The Democratic Republic of the Congo’s prominence reflects the intense and recurring fire activity associated with savanna and agricultural burning in central Africa, while Indonesia’s position recalls the catastrophic peatland fire episodes that have repeatedly blanketed Southeast Asia in hazardous smoke.

Brazil and the Russian Federation round out the list from opposite ends of the fire spectrum. Brazil’s exposure is tied to deforestation-related and drought-driven fires in the Amazon basin, where fire activity has intensified during severe dry years, and Russia’s reflects the enormous boreal wildfires that have burned across Siberia with increasing frequency as high northern latitudes warm faster than the global average. Mexico’s inclusion highlights the role of seasonal fire activity interacting with heat episodes in North and Central America. Together, these five countries illustrate that co-exposure is a shared global challenge shaped by local land management, climate variability, and the underlying warming trend.

Why does the combination of heat and fire smoke matter more than either hazard alone? The health risks of each component are individually well documented. Extreme heat is associated with heat exhaustion, heat stroke, and elevated mortality, particularly among elderly people, outdoor workers, and those with pre-existing conditions. Landscape fire smoke contains a complex mixture of fine particulate matter, known as PM2.5, along with ozone precursors, carbon monoxide, and volatile organic compounds. Chronic and acute exposure to this mixture is linked to respiratory illnesses such as asthma exacerbations and chronic obstructive pulmonary disease, as well as cardiovascular outcomes including heart attacks, strokes, and increased all-cause mortality.

The critical insight motivating this line of research is that these risks do not simply add together; they interact. Stretches of extreme temperatures amplify both the incidence of landscape fires and the severity of the health harms that follow from smoke exposure. Hot, dry conditions desiccate vegetation and extend the window in which ignitions can spread into large, uncontrollable fires. At the same time, heat stresses the human body’s cardiovascular and respiratory systems, which are precisely the systems most burdened by inhaled particulate pollution. When a heatwave and a smoke event coincide, populations face compounded physiological stress, and the most vulnerable groups, including children, the elderly, and people with chronic disease, face the steepest risks.

The methodological achievement of the new analysis lies in its global scope and its explicit treatment of co-exposure as a distinct metric. Rather than assessing heatwaves and fire pollution in separate silos, Wu and colleagues constructed a joint measure that identifies days on which both hazards affected the same populations at the same time. This approach matters because risk assessments and public health preparedness plans have historically treated heat and air quality as separate hazards, managed by separate warning systems and separate response protocols. A population experiencing both hazards simultaneously may require different and more urgent interventions than one facing either alone, from cooling-and-clean-air shelters to integrated emergency communications.

The upward trajectory documented in the study aligns with broader patterns that climate scientists have observed across the early twenty-first century. Heatwaves have become more frequent, more intense, and longer-lasting in many regions, and fire seasons have lengthened in parts of the Americas, Australia, the Mediterranean, and the boreal zone. The study’s finding that the co-exposure trend affected a majority of humanity indicates that these two trends are converging on the same places and the same people, rather than unfolding in separate geographic or demographic spheres. The increase of 287 million exposed people per decade provides a quantified measure of how rapidly that convergence is proceeding.

For policymakers and public health officials, the results carry a clear message about preparedness. Regions identified as co-exposure hotspots, including the five countries with the highest person-day averages, may need to plan for compound emergencies in which heat response and smoke response must operate in tandem. The study also provides a baseline against which future changes can be measured, and its person-day framework offers a template for tracking how the compound burden evolves as the century progresses. As the authors’ analysis demonstrates, the era in which heat and fire smoke could be managed as separate problems is receding; for more than half the world’s population, and for hundreds of millions more each decade, the two hazards now arrive together, and the institutions charged with protecting public health are only beginning to adapt to that reality.

Subject of Research: Global trends in human co-exposure to heatwaves and landscape fire smoke air pollution from 2000 to 2019

Article Title: Co-exposure to heatwaves and landscape fire smoke is rising globally

Article References: Co-exposure to heatwaves and landscape fire smoke is rising globally. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: heatwaves, landscape fires, wildfire smoke, air pollution, PM2.5, co-exposure, public health, climate change, Science Advances, global burden, cardiovascular risk, respiratory disease

The scientific problem at the heart of the effort is deceptively simple to state and notoriously difficult to solve. Aerosols — microscopic particles released by sources such as wildfire smoke, industrial activity, and sea spray — serve as the seeds around which cloud droplets form. When more aerosols enter a cloud, the available moisture is distributed across a larger number of smaller droplets, which can make the cloud brighter and more reflective. Smaller droplets can also suppress rainfall, allowing the cloud to persist longer before it dissipates. Both effects change how much sunlight the cloud reflects away from the planet, and both remain among the largest sources of uncertainty in projections of Earth’s future climate.

“Small changes in these clouds can have a surprisingly large effect on Earth’s energy balance and climate,” said Jian Wang, a professor of energy, environmental and chemical engineering in the McKelvey School of Engineering and director of the Center for Aerosol Science and Engineering at WashU. Wang, the principal investigator for the project, noted that scientists still do not fully understand how aerosols change marine low clouds. The difficulty lies in separating the genuine fingerprint of aerosols from the noise of natural meteorological variability — winds, temperature, humidity, and large-scale circulation patterns that shape clouds independently of any pollution or smoke influence.

That separation problem is precisely where the new AI-driven approach comes in. The WashU team, which includes co-principal investigators Bo Li and Maxine Yu of the Department of Statistics and Data Science in WashU Arts & Sciences, plans to develop three physics-informed generative AI models trained on real satellite observations and field measurements. The data will capture episodic smoke plumes drifting from North American wildfires over the ocean, providing natural experiments in which a known aerosol source interacts with marine cloud systems. Rather than treating the atmosphere as a snapshot at a single place and time, the models will trace the full history of an air mass as it travels, incorporating the emissions and meteorological conditions it encounters along its path.

The first two models will predict what aerosol concentrations and cloud properties would plausibly look like in the absence of wildfire influence — a statistical counterfactual. The third model will then quantify the probability distributions of aerosol concentration and cloud properties under actual wildfire-smoke influence. Comparing the two worlds allows the researchers to isolate the causal effect of the smoke. “The AI creates the counterfactual: what would the aerosols and clouds likely have looked like if the wildfire had not occurred?” Yu said. “This allows us to separate the aerosol effect from the many meteorological factors that also influence clouds.”

This counterfactual strategy represents a meaningful departure from conventional approaches to aerosol-cloud research. Traditional statistical studies often correlate cloud properties with aerosol loading at a given location and moment, an approach vulnerable to confounding: meteorology influences both where smoke goes and how clouds behave, so simple correlations can misattribute causes. By following air-mass histories and explicitly modeling the world with and without the smoke, the WashU framework borrows ideas from causal inference — a branch of statistics concerned with identifying genuine cause-and-effect relationships rather than mere associations. Li and Yu contribute expertise in AI, statistical learning, environmental and spatial statistics, causal inference, and uncertainty quantification, complementing Wang’s strengths in atmospheric aerosols, clouds, and field observations.

“Bringing these perspectives together within WashU allows us to develop AI methods that are not only technically advanced, but also physically meaningful and statistically rigorous,” Li said. That emphasis on physical meaning matters because purely data-driven models can learn spurious patterns that fail when applied to new conditions. By embedding physical constraints into the generative models, the researchers aim to ensure that the AI’s outputs remain consistent with the known behavior of aerosols, droplets, and radiation, while the statistical machinery quantifies how confident scientists can be in each prediction. The result, the team says, should be a tool that improves understanding of aerosol effects on marine low clouds and strengthens scientists’ capability to predict the environment.

The stakes extend well beyond academic curiosity. According to the NSF, which is funding the work, the research could help American communities prepare for extreme weather, protect public health, and guide sound investments in infrastructure and risk management. “Even for people living far from the ocean, including here in the Midwest, understanding these processes matters because they affect our ability to predict future warming, precipitation and other climate changes,” Wang said. Marine low clouds influence regional and global temperature trends, and better constraints on how aerosols modify them could narrow the range of warming projected for the coming decades — a refinement with direct consequences for policy, planning, and climate adaptation worldwide.

The project also carries a significant workforce dimension. The researchers will train students and researchers in advanced AI methods, supporting the NSF’s priority of building a national AI-ready scientific workforce. “Training is an important part of the project, because the next generation of scientists increasingly needs to understand both the physical sciences and modern AI and statistics,” Li said. The team’s goal is to give students hands-on experience applying sophisticated machine learning to consequential real-world scientific problems, bridging the gap between atmospheric science and data science that often separates the two fields.

Beyond its immediate scientific targets, the framework is designed for reuse. The researchers plan to develop a human-in-the-loop agentic AI framework that can automate parts of the scientific workflow — from preparing and screening data to training models and analyzing results — while keeping scientists in charge of the critical decisions. “Our goal is therefore not just one scientific result, but a reusable tool for tackling similar problems in earth science,” Wang said. The AI and statistical machinery being developed could, in principle, be adapted to disentangle the effects of other environmental drivers from background weather and climate variability, offering earth scientists a general-purpose instrument for one of their field’s most persistent challenges: hearing a clear signal through a noisy atmosphere.

Subject of Research: Aerosol effects on marine low clouds and climate prediction using AI and statistics

Article Title: In the clouds

Article References: In the clouds. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: aerosols, marine low clouds, wildfire smoke, artificial intelligence, climate modeling, causal inference, generative AI, cloud physics, National Science Foundation, uncertainty quantification, climate prediction, Washington University in St. Louis

CONIOPOL, whose name blends ‘coniology’ (the study of atmospheric dust) with ‘polarization’, was developed by Quentin Laffineur, Alexander Mangold, and Andy W. Delcloo and is described in the journal Atmospheric Measurement Techniques. The algorithm runs on the Vaisala CL61, a single-wavelength automatic lidar ceilometer that fires a 910.55-nanometer infrared laser straight up and records the light scattered back by particles in the air. What sets the CL61 apart from ordinary ceilometers is its polarization capability: it measures both the parallel and cross-polarized components of the returning signal. Spherical particles such as liquid droplets preserve the laser’s polarization, while irregular particles like mineral dust, volcanic ash, and ice crystals scramble it. That difference, quantified as the volume linear depolarization ratio, becomes the key that unlocks particle identification.

The algorithm weaves together three strands of information: the attenuated backscatter profile, the depolarization ratio, and the cloud-base height determined by the instrument itself. After careful signal conditioning, including noise filtering based on the signal-to-noise ratio and a two-dimensional median filter that suppresses isolated noisy pixels without blurring real atmospheric layers, a decision tree sorts every measurement into categories. A threshold on backscatter, determined statistically from a full year of data at about 3 x 10^-6 per meter per steradian, separates clouds and precipitation from aerosols. Depolarization values then split the targets further: values above 0.2 point to mineral dust, values above 0.37 suggest volcanic ash, and values below 0.1 indicate spherical particles such as smoke, marine salt, or liquid droplets.

One of the algorithm’s most distinctive features is its honesty about ambiguity. Rather than forcing every observation into a single pure category, CONIOPOL includes transitional classes that capture mixtures, such as dust blended with boundary-layer pollution or pollen mixed with continental aerosols. A two-dimensional sliding-window consistency filter then examines neighborhoods of five consecutive profiles and five adjacent range gates, spanning roughly 150 seconds and 24 meters, and suppresses isolated detections that do not form coherent structures. This approach acknowledges that the atmosphere is a continuum, not a set of neat boxes, and it makes the resulting classification maps far more robust against noise and instrument artifacts.

Calibration was a central challenge. The CL61 has no internal depolarization calibrator, so the team adopted a clever workaround borrowed from previous work on Doppler lidars: monitoring the depolarization signal at the base of liquid water clouds, whose spherical droplets provide a natural reference with a known, near-zero depolarization. Over the full year of observations, from February 2024 to January 2025, the median depolarization at liquid cloud bases stayed stable between 0.01 and 0.02, confirming that the instrument’s polarization channel did not drift. The backscatter calibration, anchored to a factory-calibrated reference using the well-established liquid-cloud calibration method, also remained stable throughout the study period.

To test whether the classifications made physical sense, the researchers compared them against the Copernicus Atmosphere Monitoring Service (CAMS) global forecast, which models aerosol mixing ratios for dust, sea salt, soot, organic carbon, and sulfate. Because the two datasets differ fundamentally in resolution and nature, the comparison was conditional rather than point-by-point. The results were striking: whenever CONIOPOL detected dust, CAMS dust mixing ratios were significantly elevated below about 3 kilometers, and the intermediate ‘aerosol mix’ class showed intermediate dust levels, exactly as expected for diluted or partially mixed dust intrusions. Similarly, smoke and marine detections coincided with enhanced modeled concentrations of soot, organic carbon, sulfate, and sea salt, particularly during summer when wildfire plumes crossed Europe at higher altitudes.

The year-long record also revealed clear seasonal rhythms. Continental aerosols dominated, detected in more than half of all hours and peaking near 85 percent in July and August, when photochemical production of secondary particles intensifies. Smoke and marine aerosols appeared 10 to 27 percent of the time, mostly confined below one kilometer, consistent with sea-salt aerosols trapped in the marine boundary layer. Dust appeared mainly in spring and autumn, with winter dust layers reaching above 3 kilometers. Ice crystals showed a pronounced seasonal cycle with maxima in late winter and autumn, while liquid cloud droplets were by far the most common hydrometeor, occurring 20 to 70 percent of the time depending on the month.

A showcase example from February 2024 illustrates the algorithm’s power. During a single night, CONIOPOL tracked an elevated dust plume near the top of the boundary layer, distinguished an upper cloud layer of ice crystals from a lower layer of supercooled liquid water, and even captured a snow-to-rain transition near 2,200 meters altitude. Precipitation classification relies on depolarization thresholds derived from the data themselves: values below 0.01 indicate fog, drizzle, or mist; between 0.01 and 0.23, liquid rain; and above 0.41, heavily rimed graupel. Compared with a conventional rain gauge, the algorithm detected precipitation 5 to 13 percent more often, a positive bias partly attributed to the difficulty of separating drizzle and fog from true precipitation.

Sensitivity tests confirmed the algorithm’s resilience. Perturbing the backscatter calibration by up to 10 percent left most classifications unchanged, while depolarization variability proved to be the dominant source of uncertainty, mainly shifting observations between neighboring categories near fixed thresholds rather than producing arbitrary reassignments. The authors caution that the thresholds were developed for regional background conditions and long-range transported aerosols, and that fresh plumes near strong sources, such as erupting volcanoes or intense wildfires, may behave differently. They also note that a single site and a single year cannot fully validate the approach.

Even with those caveats, the implications are considerable. Polarization-enabled ceilometers are cheap, rugged, and already deployed in operational weather networks across Europe and beyond. CONIOPOL demonstrates that they can deliver scientifically meaningful aerosol typing around the clock, bridging the gaps between sparse satellite overpasses, campaign-based research lidars, and surface air-quality stations. The team envisions extending the algorithm to other CL61 sites and combining its output with the ACTRIS research infrastructure and the new EarthCARE satellite mission. If that happens, the humble ceilometer on the roof of a weather station may become one of the most valuable eyes humanity has on the ever-changing chemistry of the sky.

Subject of Research: Real-time aerosol and hydrometeor classification using single-wavelength polarization lidar

Article Title: New operational perspective to identify aerosols and hydrometeors in real-time with a pioneering algorithm (CONIOPOL) based on single-wavelength polarization lidar (CL61)

Article References: Laffineur, Q., Mangold, A., & Delcloo, A. W. (2026). New operational perspective to identify aerosols and hydrometeors in real-time with a pioneering algorithm (CONIOPOL) based on single-wavelength polarization lidar (CL61). Atmospheric Measurement Techniques, 19(19), 6415-6446. https://doi.org/10.5194/amt-19-6415-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6415-2026

Keywords: aerosols, lidar, ceilometer, depolarization, CONIOPOL, cloud phase, precipitation, Saharan dust, wildfire smoke, air quality, CAMS, atmospheric monitoring

The challenge stems from the nature of wildfire emissions themselves. Unlike the steady biogenic emissions from forests or the well-documented output of industrial sources, wildfires are rare, explosive and deeply unpredictable events. The location, size and intensity of new fires can only be estimated probabilistically, and the height at which smoke is injected into the atmosphere remains highly uncertain. Because air quality forecasts depend so heavily on emissions, these uncertainties cascade through the entire dispersion calculation, producing errors that vary enormously in space and time. The 2023 Canadian fire season, which consumed more than 15 million hectares of forest, made these limitations painfully visible as smoke crossed the Atlantic and degraded air quality in European cities.

Operational air quality assimilation systems, which blend model forecasts with surface observations to produce analyses, rely on accurate estimates of background forecast errors. Yet the error formulations used in most operational systems are highly simplified, often assuming uniform statistical properties that cannot capture the anisotropic, case-dependent error structure of a wildfire plume. Ensemble forecasting, the gold standard in numerical weather prediction, offers a way around this, but it comes at a steep price. Building a meaningful ensemble for air quality requires perturbing emissions, deposition rates and boundary conditions whose error characteristics are poorly known, and even ensembles of 50 to 100 members are orders of magnitude too small to sample the enormous dimensionality of the atmospheric system, leading to sampling errors that demand additional fixes like localization and inflation.

Vogel and her colleagues propose a fundamentally different approach rooted in ideas that trace back to stochastic dynamic prediction in the 1960s. Instead of running dozens of parallel forecasts, their method, called a parametric uncertainty forecast, evolves the error statistics themselves as prognostic variables within the forecast model. The key insight is that for a Gaussian error distribution, a handful of parameters, principally the error standard deviation and its spatial correlation length, are enough to describe the leading structure of the uncertainty. By deriving explicit equations for how these parameters change under advection, diffusion and emissions, the researchers can propagate uncertainty through the model at a tiny fraction of the computational cost of an ensemble.

The theoretical core of the paper lies in deriving these prognostic equations for the three processes that matter most for near-surface smoke forecasting. Advection turns out to be the simplest case: error standard deviation is transported by exactly the same wind field as the concentration itself, so the model’s existing advection scheme can handle it without modification. Vertical diffusion is more subtle. When a spatially correlated concentration field diffuses, its errors shrink, and the researchers show that this reduction depends on the vertical error correlation length, a new parameter that must be supplied to the model. Emissions act as a source of uncertainty, increasing concentration error in proportion to the correlated emission error standard deviation, a quantity that combines how uncertain the emissions are with how those errors relate to existing concentration errors.

A particularly important theoretical result concerns the choice between error variance and error standard deviation as the prognostic variable. Although the two are mathematically equivalent, the standard deviation form yields simpler equations with fewer terms, avoids an extra spatial derivative that introduces discretization errors, and shares the same units as concentration itself, making implementation and interpretation far easier. In the diffusion equation, the variance form requires an additional gradient term that the standard deviation form does not, and idealized experiments showed the standard deviation formulation tracks a full Kalman filter more accurately. The team therefore selected standard deviation as the working variable throughout their implementation.

The researchers embedded this parametric uncertainty forecast into GEM-MACH, Canada’s operational coupled meteorology and air quality model, which runs at 10-kilometer resolution over North America with 84 vertical layers and hourly wildfire emissions from the Canadian Forest Fire Emissions Prediction System. They chose fine particulate matter, PM2.5, as the target species and added its error standard deviation as a new prognostic field. For initial and boundary conditions, they replaced the operational bounded statistical fit between concentration and error variance with a simple linear relation applied to the instantaneous forecast concentrations, ensuring that uncertainties from emissions inside the simulation period remain consistent with those entering from outside it.

To demonstrate the approach, the team applied it to the June 6, 2023 smoke episode, when large fires in central Quebec sent plumes toward populated areas while older smoke lingered over Ottawa and Montreal. The parametric forecast produced dramatically different error fields from the operational setup: large, plume-shaped uncertainty structures grew directly from the remote fire hotspots and were carried southward by the winds, while the operational error fields, derived from two-month running statistics, remained nearly flat across the smoke regions. When these error estimates were fed into the hourly surface analysis system, the differences were striking. In well-observed urban areas, the two experiments produced similar corrections, but in the sparsely monitored north, the parametric experiment generated analysis increments exceeding 80 micrograms per cubic meter in plumes located roughly 400 kilometers from the nearest observation stations.

That last result may be the most consequential. Because the analysis spreads observation information according to the background error covariance, and because the parametric forecast assigns very large error standard deviations within the plumes, even weakly correlated distant observations can now exert meaningful corrections on unobserved smoke. The researchers emphasize that these far-reaching increments arise purely from the improved variance fields, not from exaggerated error correlations, meaning the sparse observing network is used far more efficiently in a highly case-dependent and anisotropic way. The method can even produce opposite-signed corrections within a single plume when nearby observations disagree, something the operational setup cannot achieve.

The authors are careful to note that their formulation remains deliberately simplified. Emission errors are assumed proportional to local emissions, the vertical error correlation length is prescribed rather than forecast, and processes like area emissions, chemistry, deposition and wind uncertainty are excluded for now. Sensitivity tests showed the results depend on the chosen correlation length, with shorter lengths producing shorter uncertainty plumes. Still, the study marks the first implementation of parametric diffusion and emission error dynamics in a state-of-the-science atmospheric model and leads the way toward a full parametric Kalman filter for operational smoke assimilation. As fire seasons intensify and public demand for trustworthy air quality warnings grows, this efficient new mathematics may soon help forecasters tell not just where the smoke will go, but how sure they are when they say so.

Subject of Research: Parametric uncertainty forecasting for wildfire smoke data assimilation in operational air quality models

Article Title: Formulation of parametric uncertainty forecasts towards operational wildfire smoke assimilation

Article References: Vogel, A., Ménard, R., Abu, J., & Chen, J. (2026). Formulation of parametric uncertainty forecasts towards operational wildfire smoke assimilation. Nonlinear Processes in Geophysics, 33(3), 347-371. https://doi.org/10.5194/npg-33-347-2026

Image Credits: AI Generated

DOI: 10.5194/npg-33-347-2026

Keywords: wildfire smoke, air quality forecasting, data assimilation, parametric Kalman filter, uncertainty quantification, GEM-MACH, PM2.5, error covariance, ensemble forecasting, emissions uncertainty, Canada wildfires, atmospheric modeling

The device, called a smoke logger, is a small, solar-powered air quality monitor that can be attached to the same collars wildlife biologists already use to track animal movements and physiology. Its design and programming have been released as an open-source package, published alongside the team’s initial results in the journal Methods in Ecology and Evolution, so that researchers anywhere in the world can build their own units. The cost of materials is roughly $100 per logger, a figure that matters enormously in a field where wildlife studies, particularly those involving capturing, collaring, and recapturing animals, are notoriously resource intensive. By making the tool cheap and freely available, the Colorado State team hopes to turn smoke exposure from an unmeasured unknown into a standard data stream in wildlife research, one that can be layered onto the GPS coordinates and heart rate readings that many existing collar studies already collect.

At the technical heart of the device is a low-cost, commercially available particulate sensor of the same type used by the PurpleAir citizen-science monitoring network. These sensors use a laser to count and size airborne particles, focusing on what scientists call particulate matter 2.5, or PM2.5: particles with a diameter of 2.5 micrometers or less. That size threshold is not arbitrary. Particles this small are fine enough to penetrate deep into the lungs and cross into the bloodstream, and they are the components of smoke most strongly linked to cardiovascular and respiratory harm in humans. Measuring PM2.5 concentrations at the level of an individual animal therefore provides a direct, biologically meaningful index of the pollution that animal is actually inhaling, rather than an estimate interpolated from a station many kilometers away.

The engineering choices behind the smoke logger reflect the harsh realities of field deployment. Transmitting data from a collar in remote terrain would require costly satellite or cellular connectivity that simply does not exist in most wildlands, and the energy demands of radio transmission would quickly drain a small battery. The team’s solution is to record and store air quality readings locally every 30 minutes, powered by a small solar panel, and to recover the data later when the collar is retrieved. This makes the device suitable for medium and large animals that can carry the added weight, and it fits naturally into the workflow of long-term wildlife studies in which collars are routinely recovered after months or years. The trade-off is that the data arrives on the timescale of the study rather than in real time, but for questions about chronic exposure and behavioral response, that is often exactly what researchers need.

The first deployments on mule deer produced one of the most striking findings of the project. When the researchers compared readings from the deer-borne sensors with data from the closest official monitoring stations, they found that air quality differed by an order of magnitude, meaning the animals were experiencing conditions either ten times better or ten times worse than the nearest station recorded. Air quality, in other words, varies enormously at the microscale, shaped by local terrain, vegetation, wind patterns, and the animal’s own movements through the landscape. George Wittemyer, the CSU professor who led the project, described that discovery as eye-opening, and it carries a broader implication: without sensors on the animals themselves, any estimate of wildlife smoke exposure drawn from regional monitoring networks is likely to be badly wrong.

That variability cuts both ways, and it hints at one of the most intriguing possibilities raised by the research. If air quality shifts dramatically over short distances, then some parts of a landscape may function as refugia during smoke events, places where animals can find measurably cleaner air while the rest of their range chokes under haze. Adam Parlin, who designed the smoke logger as a postdoctoral researcher in CSU’s Department of Fish, Wildlife and Conservation Biology, pointed to exactly this kind of insight as the tool’s promise. Identifying where animals go and how they adjust their behavior during severe smoke events, from the wildlife’s own perspective, could give conservation managers actionable information about which habitats matter most during fire season and how to protect them.

Not every test animal encountered dangerous smoke during the study period. Deer monitored in Colorado and Utah this year were not exposed to unhealthy levels of wildfire smoke, a reminder that exposure is highly episodic and that multi-year deployments will be needed to capture the worst events. But the team demonstrated that the sensors are sensitive enough to register even minimal pollution spikes from traffic for deer living in urban environments, a sign that the devices can resolve subtle gradients in air quality rather than only extreme events. That sensitivity matters because chronic, low-level exposure may carry health costs that accumulate over time, and because behavioral responses to mild smoke may differ from responses to severe events in ways that only fine-grained data can reveal.

The research team is now scaling up through partnerships. They are collaborating with researchers across the western United States to attach smoke loggers to existing studies tracking cougars, deer, and elk, effectively piggybacking on fieldwork that is already underway rather than launching costly new capture programs. They have also stationed stationary sensors in the habitat of the Sierra Nevada fisher, an endangered small mammal related to weasels, to characterize the air quality within the species’ range. Those results will appear in future publications, but the strategy is clear: by combining animal-borne and stationary sensors, researchers can map both the exposure of individual animals and the broader atmospheric conditions of the landscapes they inhabit.

The implications extend beyond wildlife. The U.S. Environmental Protection Agency monitors air quality in populated areas through its AirNow network, leaving vast rural and wilderness regions effectively unmonitored. Sensors carried by animals roaming those unmonitored spaces could fill gaps in the national picture of smoke exposure, offering health-relevant data for rural communities and people who work outdoors as well as for the animals themselves. In a year when massive smoke plumes have degraded air quality for weeks at a time across North America, a chronic problem that Wittemyer notes now affects everyone, that dual benefit gives the technology an unusually broad appeal for a device built to hang around a deer’s neck.

For land managers, the most practical payoff may come in the planning of prescribed burns. Deliberate, controlled fire is one of the most important tools for reducing fuel loads and limiting the severity of future wildfires, but it generates its own smoke, and managers currently have little way of knowing when or where that smoke actually reaches wildlife. Mark Ditmer, the U.S. Forest Service wildlife ecologist who co-led the project, emphasized that a stronger understanding of smoke exposure could allow burns to be timed and located to meet forest-management goals while avoiding sensitive periods and important habitats. As wildfires grow in frequency, size, and severity, and as smoke becomes a recurring seasonal stressor rather than a rare event, tools like the smoke logger offer something conservation biology has never had before: a way to see the air through an animal’s lungs, and to act on what that view reveals.

Subject of Research: Animal-borne air quality sensors for measuring wildfire smoke exposure in wildlife

Article Title: Novel device will help show how wildfire smoke impacts wildlife

Article References: Novel device will help show how wildfire smoke impacts wildlife. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: wildfire smoke, wildlife, PM2.5, air quality, smoke logger, Colorado State University, mule deer, animal-borne sensors, conservation, prescribed burns, Methods in Ecology and Evolution, open-source technology

Chemical Insights’ footprint at the meeting is unusually broad for a single research organization. According to the institute, its scientists are hosting one symposium, delivering five presentations, leading one workshop and presenting six poster sessions. The topics span some of the most pressing exposure questions of the moment: the chemical emissions produced when electric vehicles catch fire, the smoke that wildfires carry into communities, the volatile organic compounds released by resins used in dental 3D printing, and the hidden chemical ingredients in everyday consumer products ranging from cosmetics to mobility aids. Together, the presentations illustrate how modern exposure science connects laboratory measurements of emissions to real-world estimates of the doses people receive.

One of the most technically ambitious threads in the Chemical Insights program concerns electric vehicle fires. Lithium-ion battery failures, known as thermal runaway events, can release dense clouds of gases and fine particles containing metals and other hazardous species. Three separate contributions address this emerging risk from complementary angles. A poster presentation documents exposure to particulate matter and metals during controlled burns of electric vehicles, while another characterizes gas and particle-phase emissions from full-scale electric vehicle fires and discusses the implications for emerging exposure risks. A third presentation describes the physicochemical and toxicological characterization of particle emissions collected from those fires, linking the physical and chemical properties of the particles to their biological activity. A related poster examines the emissions profile of thermal runaway in mobility-aid batteries and evaluates how fire suppression systems influence those emissions.

The significance of this work lies in the gap it addresses. As electric vehicles, e-bikes, scooters and other battery-powered devices proliferate, first responders, occupants of nearby buildings and communities downwind of fire incidents face exposure scenarios that traditional fire-safety research never fully characterized. Battery fires can burn hotter and release different chemical species than conventional hydrocarbon fires, including metal-containing particles that may penetrate deep into the respiratory tract. By conducting controlled burns under laboratory conditions and collecting emissions for detailed analysis, researchers can begin to quantify what is released, in what quantities, and with what toxicological signatures — the raw material needed for exposure and risk assessments that protect firefighters, investigators and the public.

Wildfire smoke, another major theme at the meeting, receives dedicated attention in a scientific session on estimating and monitoring wildfire smoke exposures. Wildfire events have grown in frequency and intensity across many regions, pushing smoke plumes hundreds or thousands of kilometers from the fire line and exposing populations far removed from the flames. Estimating what people actually inhale during these events is a formidable measurement challenge: smoke composition changes as it ages in the atmosphere, monitoring networks are unevenly distributed, and individuals move through microenvironments with very different concentrations. The session reflects a growing effort across the exposure science community to combine ground sensors, satellite data, models and personal monitoring to produce exposure estimates that are accurate enough to support public health decisions such as school closures, clean-air shelter recommendations and targeted advisories for vulnerable groups.

Indoor and occupational exposures receive their share of the spotlight as well. One presentation examines the toxicity of volatile organic compound emissions from a dental 3D printing resin using a human alveolar tissue model. Dental laboratories and clinics increasingly rely on resin-based additive manufacturing, which can emit volatile organic compounds during printing and post-processing. Testing those emissions against human lung tissue models in the laboratory offers a way to assess potential respiratory toxicity without relying solely on animal studies or chemical-by-chemical literature reviews. It is a concrete example of a broader methodological trend in toxicology: the use of advanced in vitro systems derived from human cells to evaluate the hazards of complex chemical mixtures rather than single compounds in isolation.

Consumer products present a different kind of exposure puzzle, and two Chemical Insights posters tackle it directly. One, titled “The Ingredients You Can’t Read: Expanding Access to Consumer Product Chemical Data,” addresses a persistent transparency problem — many products contain chemical ingredients that are not disclosed on labels, leaving consumers, researchers and regulators without the information needed to assess exposure. A companion poster describes the integration of chemical profiling with Monte Carlo modeling for multi-route exposure risk assessment of cosmetic spray products. Monte Carlo methods allow researchers to propagate uncertainty across the many variables that shape real exposures, including how often a product is used, how much is applied, and how much of each chemical is inhaled, absorbed through the skin or ingested. Combining analytical chemical profiling of the products themselves with probabilistic modeling produces risk estimates that reflect the full range of real-world use patterns rather than a single worst-case scenario.

Several contributions push the methodological frontier of the field. A presentation on a systematic framework for exposure reconstruction from biomarker data addresses one of exposure science’s hardest inverse problems: working backward from chemicals measured in blood, urine or other biological samples to infer the magnitude, timing and source of the original exposure. Biomarkers integrate exposure across all routes and sources, but interpreting them requires careful modeling of how chemicals are absorbed, distributed, metabolized and eliminated. Another presentation leverages non-targeted analysis of human biomonitoring data, an analytical strategy that screens samples for a vast array of chemical features without deciding in advance which compounds to look for, allowing previously unrecognized exposures to surface. Chemical Insights is also participating in a scientific session on artificial intelligence and machine learning in exposure science, a rapidly growing area where algorithms are being applied to everything from sensor networks to large biomonitoring datasets.

The workshop on advancing exposure forensics for environmental health protection highlights another emerging discipline. Exposure forensics applies the investigative tools of exposure science to determine the source, pathway and timing of chemical exposures after the fact — whether in litigation, in outbreak investigations, or in community health studies where residents suspect an environmental cause of illness. Building capacity in this area requires shared methods for chemical fingerprinting, source attribution and reconstruction of past exposures, along with standards for how such evidence is communicated to regulators, courts and affected communities. A presentation titled “Beyond the Logo: Advancing the Exposome Through Collaborative Exposure Science at UL Research Institutes” situates the institute’s portfolio within the exposome concept, the ambitious framework that seeks to capture the totality of environmental exposures a person experiences across a lifetime, from chemicals and pollutants to psychosocial stressors.

Finally, a poster on the CIExpo Portfolio describes an effort to integrate exposure data from source to intake — that is, to trace chemicals from the moment they are emitted by a product, material or process through the environmental pathways they travel to the point where they enter a human body. That source-to-intake chain is the conceptual backbone of exposure science, and integrating data across it remains one of the field’s central technical challenges, requiring alignment of emission measurements, environmental fate modeling, microenvironmental monitoring and human activity patterns. Taken together, the Chemical Insights program at the ISES Annual Meeting offers a snapshot of where exposure science is heading: toward complex real-world scenarios such as battery fires and wildfire smoke, toward human-relevant toxicological models and non-targeted chemistry, and toward computational tools — from Monte Carlo simulation to machine learning — capable of turning fragmented measurements into actionable knowledge for protecting public health.

Subject of Research: Chemical exposure science research on emissions from electric vehicle fires, wildfire smoke, 3D printing and consumer products

Article Title: Chemical Insights’ research to make an impact at The International Society of Exposure Science Annual Meeting

Article References: Chemical Insights’ research to make an impact at The International Society of Exposure Science Annual Meeting. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: exposure science, ISES Annual Meeting, electric vehicle fires, thermal runaway, wildfire smoke, volatile organic compounds, 3D printing resin, biomonitoring, Monte Carlo modeling, consumer product chemicals, particulate matter, UL Research Institutes

The biological case for pediatric vulnerability rests on basic physiology. Children breathe more air per kilogram of body weight than adults, in part because their higher ventilation rates and greater oronasal partitioning toward mouth breathing increase the dose of any airborne pollutant delivered to the lower airways. Their airways are also narrower and still developing, so a given degree of inflammation or mucus production produces proportionally greater obstruction. The review emphasizes that children’s immune responses are immunologically immature and skewed toward the type 2 inflammatory pathways that define allergic asthma, meaning that environmental insults arriving during early life can both provoke acute attacks and shape the trajectory of the disease itself. When these anatomical and immunological factors combine with socioeconomic and structural disparities—substandard housing, proximity to traffic corridors, limited access to care—the burden of climate-amplified exacerbations concentrates among the children least equipped to escape it.

Air pollution remains the most extensively quantified trigger, and the review details the cellular choreography by which it initiates an attack. Particulate matter, ozone, nitrogen dioxide and diesel exhaust particles generate reactive oxygen species in the airway epithelium, damaging the barrier that normally keeps the lung’s immune system calm. Injured epithelial cells release alarmins and inflammatory mediators, and experimental work shows that diesel exhaust particles can upregulate interleukin-17A expression through ROS-dependent NF-κB signaling in airway epithelial cells, feeding neutrophilic and mixed inflammatory responses. Ozone, a powerful oxidant formed photochemically from precursor pollutants, is particularly adept at provoking epithelial injury and airway hyperresponsiveness. Global burden-of-disease analyses cited in the review attribute substantial numbers of new asthma cases and emergency room visits to ambient PM2.5, ozone and nitrogen dioxide, and meta-analyses of outdoor pollution consistently link short-term exposure spikes to exacerbations in both children and adults.

What elevates this familiar story into a climate story is the feedback between warming and pollution chemistry. Ground-level ozone formation accelerates with temperature, so heat waves and ozone episodes increasingly arrive together, compounding respiratory stress. Climate-attribution modeling has begun to disentangle how much of future pollution-related mortality and asthma morbidity will be driven by climate change itself rather than by emissions alone, with projections suggesting that a warming world will partially offset gains from cleaner air in some regions while worsening them in others. The review also highlights wildfire smoke as a rapidly growing climate-linked pollution source: attribution studies indicate that anthropogenic climate change has played a large role in driving smoke concentrations across the western United States, and epidemiological work has directly tied climate-change-induced wildfire smoke to asthma exacerbations in that region. Fine particles in smoke penetrate deep into the lung, and children in smoke-affected communities have shown measurable increases in asthma-related emergency visits during major fire events.

Temperature emerges as a second major axis, operating through mechanisms distinct from pollution. Extreme heat triggers oxidative stress, activates bronchial C-fiber sensory nerves and promotes systemic inflammation; in susceptible patients, simply breathing hot humid air can provoke bronchoconstriction through a cholinergic reflex, a finding demonstrated experimentally in asthmatic subjects. Time-series studies from Atlanta, Maryland, Montreal and elsewhere show that warm-season temperature extremes and heat events are associated with increased asthma-related emergency department visits and hospitalizations in children, and case-crossover analyses in Ontario have extended the pediatric heat signal to extreme heat days more broadly. Cold is not protective: longitudinal data from eighteen Chinese cities identified cold temperatures and sudden temperature drops as novel risk factors for exacerbation, and mouse models show that both high and low temperature extremes aggravate airway inflammation. Gene-expression studies suggest extreme temperatures can even reprogram the airway epithelium in mice and asthma patients, hinting at mechanisms that outlast the weather event itself.

The review’s treatment of relative humidity is among its most technically interesting sections. As the atmosphere warms, tropospheric relative humidity stays near constant globally while relative humidity over land declines, a divergence explained by the fact that land warms faster than the ocean surface that supplies its moisture. For the human airway, both directions matter. At low humidity, inspired air dehydrates the respiratory mucosa, and recent work in Communications Earth & Environment argues that global warming risks dehydrating and inflaming human airways directly, compromising the epithelial defenses that depend on a well-hydrated mucus layer. At high humidity, mucus clearance becomes impaired, and humid-heat combinations captured by indices such as the humidex have been linked to childhood asthma hospitalizations in time-series analyses from Hefei, China. Humidity also modifies pollution toxicity: studies in Romania found that the respiratory effects of ambient air pollution varied with air humidity, meaning that climate-driven shifts in moisture regimes can change the dose-response relationship children experience on any given day.

Beyond the three headline exposures, the review maps a web of indirect climate pathways. Lengthening pollen seasons—documented across North America through attribution analyses linking anthropogenic warming to extended and more intense allergen release—prolong the period during which sensitized children hover near the threshold of an attack. Transitional weather in spring and autumn favors the circulation of respiratory viruses, the single most common precipitant of pediatric exacerbations, and shifting season lengths alter when these viral windows overlap with pollution and pollen peaks. Extreme weather events add acute shocks: thunderstorm asthma outbreaks occur when storm outflows rupture pollen grains into respirable fragments; hurricanes and floods damage housing and mold-infest interiors; and dust storms in arid regions have been tied to asthma hospitalizations and respiratory emergency visits in Chinese cities. Each pathway operates on a different timescale, from minutes during a thunderstorm to decades of shifting seasonal norms, but all converge on the same vulnerable airway.

The authors are candid about the limits of current knowledge. Climate attribution—the quantitative assignment of a fraction of observed health outcomes to human-caused warming—has been achieved for only selected exposures, chiefly heat, wildfire smoke and ozone, while the pediatric-specific contribution of humidity shifts, dust storms and viral seasonality remains poorly quantified. Most epidemiological studies measure single pollutants or single meteorological variables, yet children are exposed to mixtures, and emerging work on joint pollutant-mixture associations suggests interactions that single-exposure models miss. Disentangling the independent effect of climate change from concurrent changes in emissions, land use and healthcare access is methodologically demanding, and the review calls for closing these attribution gaps as a research priority, particularly for the low- and middle-income settings where pediatric asthma prevalence is rising fastest.

The practical response the authors propose is adaptation now, not merely mitigation later. Heat-health and air-quality warning systems can give families and clinicians hours of lead time before dangerous conditions arrive; environmental adaptation measures, from improved housing ventilation and filtration to urban greening that cools heat islands, reduce the dose of triggers children actually receive; and equity-focused targeting directs these interventions to the high-risk populations where environmental injustice has already been shown to worsen asthma outcomes among school-age children. Deep-learning models trained on environmental variables are even being tested to forecast asthma patient volumes, offering health systems a way to anticipate surges. The review’s impact statement distills the argument: climate change amplifies the environmental triggers of pediatric asthma exacerbations, children’s physiology heightens their sensitivity to those triggers, and integrating climate-adaptation strategies into routine asthma care could measurably reduce the burden. In a century defined by warming, the authors suggest, the pediatric asthma action plan may need a new first line: check the forecast.

Subject of Research: Climate change effects on pediatric asthma exacerbations through air pollution, temperature and relative humidity

Article Title: Climate change, environmental exposures, and pediatric asthma exacerbations: air pollution, temperature, and relative humidity

Article References: Tran, X. N., Lee, Y.-L., Chang, L.-T., Chang, T.-Y., Chuang, K.-J., Ho, K.-F., Chung, K. F., Chang, J.-H., & Chuang, H.-C. (2026). Climate change, environmental exposures, and pediatric asthma exacerbations: air pollution, temperature, and relative humidity. Pediatric Research. https://doi.org/10.1038/s41390-026-05418-1

Image Credits: AI Generated

DOI: 10.1038/s41390-026-05418-1

Keywords: pediatric asthma, climate change, air pollution, extreme heat, relative humidity, wildfire smoke, ozone, pollen, thunderstorm asthma, respiratory viruses, environmental justice, asthma exacerbations