In a study published in Frontiers in Forests and Global Change, Yusheng Hu and colleagues at Beijing Information Science and Technology University, the Beijing Academy of Science and Technology, and China University of Mining and Technology-Beijing introduce a fire risk assessment method based on what they call gray linguistic 2-tuple relational analysis. The approach is designed to fuse crisp numbers, interval values, and fuzzy linguistic judgments, such as an expert describing a fuel condition as high or moderate, into a single mathematical representation. Crucially, the method also encodes how incomplete or uncertain each piece of information is, a quantity the authors call the information gray degree.

The physical stakes are laid out starkly in the paper. Interface fires burn through a double-layered fuel bed: natural vegetation such as shrubs, herbs, and litter below, and man-made structures including wooden buildings, furniture, and fuel storage above. When flames jump from vegetation to buildings, the heat release rate climbs sharply, pushing temperatures past the fire resistance limits of standard construction materials. Wind-driven spotting, in which burning embers travel up to 20 kilometers and ignite new fires, has become the dominant spread mechanism, rendering traditional linear firebreaks ineffective. Under extreme conditions, a single burning residence can ignite 30 percent of neighboring buildings within a 500-meter radius.

The costs of these fires are multidimensional and long-lasting. The 2019-2020 Australian bushfire season consumed over 10 million hectares, killed 33 people directly, destroyed more than 3,000 homes, and generated economic losses estimated by Westpac Bank at AUD 5 billion. The fires released 430 million tons of carbon dioxide as carbon sink function declined, and weakened water conservation affected drinking water safety for 12 million people. Biodiversity recovery may take 50 to 100 years, and post-disaster reconstruction typically costs 2.5 to 3.5 times the direct losses.

Against this backdrop, the researchers built a multidimensional indicator system of 15 indicators across four categories: fuel conditions, surrounding environment, meteorological conditions, and topographic characteristics. The fuel dimension includes fuel load measured in tons per hectare, moisture content, and the spatial continuity of fuels, which determines whether fires can ladder vertically from surface vegetation into canopies and structures. The environmental dimension captures uncontrolled agricultural and forestry burning, domestic fire use, population density, and road network density, the last treated as a cost-type indicator because denser roads improve firefighting access and evacuation routes.

The meteorological and topographic indicators follow standard fire science logic. Precipitation governs longer-term moisture in deep fuel layers, while relative humidity controls the immediate ignitability of fine surface fuels like dead grass. Temperature accelerates fuel desiccation, and wind velocity tilts flames, boosts convective oxygen supply, and carries firebrands across defensive barriers. Slope gradient intensifies radiative and convective preheating of uphill fuels, slope aspect regulates solar drying, topographic position identifies canyon chimney effects and ridge-top ember launch points, and elevation shapes vegetation type and fire season length. Notably, the team deliberately retained correlated indicators, such as precipitation and humidity, because they represent distinct physical mechanisms, and the gray relational framework they use does not require strict statistical independence among variables.

The core mathematical innovation lies in the gray linguistic 2-tuple. A conventional linguistic 2-tuple pairs a linguistic label from a seven-term risk scale, ranging from extremely low to extremely high, with a symbolic translation value capturing how far an evaluation deviates from the nearest label. The authors extend this pairing with a gray degree between 0 and 1, quantified across seven levels of information sufficiency from extremely sufficient to extremely incomplete. Crisp measurements, interval-valued data, and fuzzy linguistic variables are all transformed into this unified representation, so an expert who says fuel spatial distribution is high risk but admits the supporting information is incomplete produces an entry that carries both the judgment and its uncertainty explicitly.

Multi-expert inputs are then merged using a gray linguistic 2-tuple weighted averaging operator, which the authors prove satisfies the standard algebraic properties of permutation invariance, idempotency, monotonicity, and boundedness. Each evaluated area is subsequently compared against a positive ideal reference sequence, constructed as the state with the highest assessed risk and the lowest information gray degree, using a gray 2-tuple relational degree that generalizes classical gray relational analysis. A larger relational degree means an area sits closer to the worst-case reference and therefore carries higher risk. To translate the output into operational terms, the framework maps relational degrees onto the seven-level risk classification and adopts a deliberately conservative rule: values falling between two adjacent levels are always assigned to the higher risk category.

To demonstrate the method, the team ran a scenario-based numerical experiment with a controlled synthetic dataset and simulated expert profiles, covering five contrasting interface prototypes: an urban edge zone with dense development and low fuel load, an interlaced suburban transition zone, a continuous mountainous forest zone with heavy fuels, steep terrain, and strong winds, a scenic recreational area, and an agriculture-forest edge where seasonal straw burning is common. The ranking placed the mountainous forest zone at highest risk, followed by the agriculture-forest edge, the interlaced transition zone, the recreational area, and finally the urban edge zone. Under the conservative classification rule, the mountainous area rated high risk, the second and third areas slightly high risk, and the remaining two moderate risk.

The robustness tests delivered the study’s most encouraging results. When information gray degrees were perturbed upward across three scenarios, the numerical relational degrees shifted, with the mountainous area’s value ranging from 0.7107 to 0.7909, but the ranking never changed, and Spearman rank correlation with the baseline was a perfect 1.00 in every case. Five weight-perturbation scenarios, each shifting 10 percent of one indicator’s weight to another, likewise left the order intact. The authors also report that the algorithm’s asymptotic time complexity is O(r times m times n plus m log m), meaning that when the numbers of indicators and experts are fixed, computational cost scales approximately linearly with the number of areas assessed, and the method requires no iterative model training or large labeled datasets. The team is candid about limitations: the experiment is synthetic, some information loss is unavoidable when intervals and linguistic terms are transformed, and weight and gray degree initialization retain subjective elements. Empirical validation against real interface landscapes, using remote sensing, high-resolution geographic information systems, meteorological observations, and historical fire records, remains the necessary next step before the framework can guide resource allocation in the communities where homes and wildfire meet.

Subject of Research: A gray linguistic 2-tuple decision framework for assessing and ranking wildfire risk in wildland-urban interface areas under incomplete information

Article Title: Wildland-urban interface fire risk assessment based on gray linguistic 2-tuple relational analysis

Article References: Hu, Y., Pan, H., Zhong, S., & Zhang, L. (2026). Wildland-urban interface fire risk assessment based on gray linguistic 2-tuple relational analysis. Frontiers in Forests and Global Change, 9, Article 1794027. https://doi.org/10.3389/ffgc.2026.1794027

Image Credits: AI Generated

DOI: 10.3389/ffgc.2026.1794027

Keywords: wildland-urban interface, wildfire risk assessment, gray relational analysis, linguistic 2-tuple, gray system theory, multi-criteria decision making, fuzzy linguistic variables, information uncertainty, fire behavior, sensitivity analysis, computational complexity, disaster risk

The research, published in Frontiers in Sustainability by Parastoo Farajpoor and Mohammadreza Narimani of the University of California, Davis, links 12,081 CAL FIRE damage inspection records to an entirely open stack of satellite and map data: Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuel models, 10-meter terrain data, and a dated OpenStreetMap snapshot of buildings and roads captured six days before ignition. The workflow was designed as a data-to-decision system in the spirit of urban informatics, with strict temporal separation so that no post-fire information could leak into the pre-fire predictors. Structures outside the inspected set were treated as unknown rather than undamaged, and building-envelope characteristics observed only after the fire were deliberately excluded because their observability depended on the outcome itself, a subtle form of data leakage that could let a model infer destruction from the absence of inspectable material.

The most consequential technical result concerns validation. Under conventional random cross-validation, the integrated gradient-boosting model appeared excellent, achieving a receiver operating characteristic area under the curve (ROC-AUC) of 0.92. But when the researchers evaluated the same model on spatially held-out 1-kilometer blocks of the fire footprint, performance fell to 0.75. That 0.17 AUC gap is not a statistical footnote; it is the difference between an apparently superb forecasting tool and a moderately informative neighborhood screening tool. Nearby structures share landscape, exposure history, and fire progression, so random splits allow a model to memorize local signatures rather than learn transferable relationships. The interpretable logistic model, notably, matched the more flexible gradient-boosting model under spatial blocking and showed a calibration slope closer to the ideal value of one, meaning its stated probabilities were less overconfident when applied to unfamiliar parts of the city.

Within the inspected residential population, a one-standard-deviation increase in building count within a 100-meter-radius disk was associated with a 3.74-fold increase in the conditional odds of destruction, the largest association in the model. The gradient-boosting model’s SHAP attributions told the same story, and the response was markedly non-linear: destruction odds rose sharply once roughly 50 to 60 buildings fell within the 100-meter disk. This inflection is compatible with a change in propagation opportunity as repeated ember exchange and radiant-heat exposure accumulate across dense urban fabric, echoing prior California findings that housing arrangement and structure density rival or exceed vegetation cover as correlates of loss. The researchers are careful to note that the threshold is a model-derived value within Palisades data, not a universal zoning cutoff, and that building concentration may also encode where the fire traveled and when structures were exposed.

The vegetation results complicate any simple narrative about green space being protective or hazardous. Higher neighborhood values of the Normalized Difference Moisture Index (NDMI), a shortwave-infrared measure of canopy moisture, were consistently associated with lower destruction odds across both joint and single-index model specifications, with the strongest effect at the 100 to 300 meter neighborhood scale. The Normalized Difference Vegetation Index (NDVI), a greenness measure, told a specification-dependent story: its positive coefficient in the joint model reversed when entered separately, so the data do not support an independent harmful effect of greenness. The actionable distinction, the authors argue, is among vegetation amount, moisture condition, arrangement, and continuity, and NDVI alone cannot make it. A greenness-only risk map risks conflating actively transpiring vegetation with desiccated fuel.

Smaller but coherent effects emerged for terrain, access, and construction era. Destruction odds increased at higher local topographic positions and on east-facing aspects, and decreased with elevation, distance from wildland fuels, and distance from dead-end road nodes. Newer construction year and greater assessed value were protective conditional on other predictors, a pattern compatible with progressive improvements in building practice, though year built also encodes neighborhood age and redevelopment history. Discrimination also tended to increase with spatial scale, from 0.72 for immediate 0 to 30 meter surroundings to 0.75 for the 100 to 300 meter neighborhood, suggesting that open urban data are most useful at the scale where community morphology operates.

A separate impact track, analytically isolated from the pre-fire models, characterized spectral change and post-fire recovery without contaminating the susceptibility analysis. Mean differenced Normalized Burn Ratio (dNBR) in the 30 to 100 meter ring was 159 for undamaged structures, 218 for affected structures, and 236 for destroyed structures, with broad class overlap underscoring that neighborhood spectral change differs from an individual structure’s fire exposure. Monthly tracking of relative greenness through July 2026 showed strongly seasonal recovery ordered by spectral-impact class, with NDVI reaching 64 to 77 percent of the month-matched three-year baseline by mid-2026. Because these summaries combine developed and wildland surfaces, they reflect demolition, debris clearance, and landscaping as much as ecological recovery.

The community-context analysis revealed its own boundary: the 12 census tracts containing inspected structures occupied a narrow, low range of the national Social Vulnerability Index distribution, near the least-vulnerable quarter, with tract destruction fractions ranging from 0 to 0.90 but no reliable statistical gradient. The authors stress that this restricted-range comparison shows only that a single affluent study area contains too little social variation to estimate the vulnerability relationship, not that social factors are unimportant to wildfire resilience.

The practical translation is a neighborhood-scale screening framework rather than a parcel-level prediction system. The proposed pathway runs from computational screening to professional assessment, field verification, and planning action, with every indicator coupled to its valid scale and uncertainty. Dense-fabric areas flagged by the model would trigger coordinated assessment of the adjacent-building interface, including ember-resistant vents and roofs, ignition-resistant fencing, and reduced combustible continuity, while vegetation programs would replace greenness-only screening with moisture-aware assessments that preserve urban canopy’s cooling value. Because the entire pipeline is scripted and archived with public replication code, it can be rerun as inspection data are released for future fires, building toward a multi-event benchmark. Until coefficients and density thresholds are tested across additional fires and urban forms, the authors conclude, they should not be treated as operationally transferable, but as an auditable single-event baseline for comparative urban wildfire resilience science.

Subject of Research: Urban wildfire resilience and explainable GeoAI modeling of structure loss in the 2025 Palisades Fire

Article Title: Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 palisades fire

Article References: Farajpoor, P., & Narimani, M. (2026). Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 palisades fire. Frontiers in Sustainability, 7, Article 1975724. https://doi.org/10.3389/frsus.2026.1975724

Image Credits: AI Generated

DOI: 10.3389/frsus.2026.1975724

Keywords: Palisades Fire, GeoAI, urban wildfire, spatial cross-validation, building density, vegetation moisture, NDMI, NDVI, wildland-urban interface, urban informatics, CAL FIRE, Sentinel-2

The award, part of NSF’s Fire Science Innovations through Research and Education program, runs from September 2026 through August 2029 and brings together a multidisciplinary team from the University at Buffalo, the University of California, Berkeley, Northeastern University and the U.S. Department of Energy’s Pacific Northwest National Laboratory. The study, titled Collaborative Research: FIRE-WUI: REKINDLE: Resilient Engineering through Knowledge Integration for NetworkeD Lifelines and Ecosystem, will focus on communities in the wildland-urban interface, the zone where housing, power lines and other human development meet or intermingle with forests, grasslands and other natural vegetation. These are precisely the places where the destructive potential of wildfire and its aftermath is growing fastest.

As wildfires become more frequent and severe across the American West and beyond, wildland-urban interface communities face escalating risks that extend well beyond the immediate destruction of the fire itself. Burned landscapes lose the vegetation and soil structure that once absorbed rainfall, making them dramatically more prone to debris flows and flash floods during subsequent storms. At the same time, fires and their secondary hazards can inflict significant disruptions on lifeline infrastructure, including power grids, water systems and transportation networks. Current approaches to wildfire resilience planning, however, typically consider these interconnected risks in isolation, which makes it difficult to see how they interact with one another and how people actually respond when multiple hazards strike in sequence.

REKINDLE is designed to close that gap by combining artificial intelligence, infrastructure modeling and social science to assess wildfire-related risks together and to identify concrete ways to bolster public safety and community resilience. Sayanti Mukherjee, PhD, the project’s lead principal investigator and an associate professor in UB’s Department of Industrial and Systems Engineering, explained the central problem the team hopes to solve. People are familiar with wildfire risk, she noted, but what is often overlooked are the additional hazards and cascading effects that can follow. A region affected by wildfire can become more vulnerable to subsequent hazards like debris flows and flash floods, and when those events cut off roads or interrupt power or water service, the impacts can quickly compound for communities already struggling to recover.

Mukherjee, who is also an affiliate faculty member with UB’s Department of Electrical and Computer Engineering and the Institute for Artificial Intelligence and Data Science, is joined by co-principal investigator Susan Spierre Clark, PhD, of UB’s Department of Environment and Sustainability. The broader investigative team includes Auroop R. Ganguly, PhD, of Northeastern University; Marta González, PhD, and Anna Serra-Llobet, PhD, of UC Berkeley; and Andre Coleman, PhD, and Sam Chatterjee, PhD, of Pacific Northwest National Laboratory. This mix of expertise in engineering, environmental science, data science and social systems reflects the project’s core premise: that understanding cascading wildfire disasters requires simultaneously modeling the physical hazards, the engineered networks they damage and the human decisions those damage cascades trigger.

Through REKINDLE, researchers will investigate how wildfires and the hazards they set in motion can compromise key infrastructure, producing disruptions that ripple through the essential services people depend on for daily life and well-being. Clark emphasized that the team wants to understand not only how infrastructure systems are affected, but how those disruptions shape household decisions and community response. Bringing those pieces together, she said, can help communities build resilience, restore critical infrastructure and recover faster from future disasters. The framing matters because infrastructure failures are rarely just technical problems; a closed road can prevent evacuation, a lost power connection can disable water pumping, and a prolonged outage can determine whether a family can remain in or return to a damaged neighborhood.

Artificial intelligence will play a central role in the technical core of the project. The researchers plan to build a physics-informed, geospatial-AI model that fuses Earth observation data with wildfire expertise to generate maps showing where wildfires and related hazards such as debris flows and flash floods are most likely to occur. By anchoring machine learning in physical processes, the team aims to produce hazard predictions that are both data-driven and scientifically interpretable, an increasingly important consideration for tools intended to inform emergency management decisions. In parallel, the researchers will deploy machine learning models to trace how failures could propagate across interconnected power, water and transportation systems, capturing the dependency structures that turn a localized fire damage into a region-wide service disruption.

The human dimension of the cascade will receive equal attention. Using household surveys, focus groups and simulation games, the research team will examine how infrastructure failures affect families’ livelihoods and their ability to recover from cascading disasters. These social science methods are intended to reveal how households weigh risks, make evacuation and recovery decisions, and cope when multiple services fail at once. The team will then use these findings to help determine where investments in stronger infrastructure could deliver the greatest protective impact before a fire occurs, and how recovery efforts should be prioritized in the aftermath. Mukherjee noted that deciding where to invest resources, whether through hardening infrastructure or increasing community engagement and outreach, will be an important part of the project, and that AI and optimization can help assess different strategies and pinpoint where investments would have the most value. That kind of insight, she said, can support emergency managers, infrastructure operators and environmental planners in making more informed decisions for their communities.

To keep the work grounded in real conditions, the researchers will build and test REKINDLE through case studies in the Los Angeles and Montecito regions of California, both of which have experienced major wildfires and devastating post-fire debris flows in recent years. Montecito in particular has become a sobering reference point for cascading wildfire hazards, having seen destructive debris flows sweep through burned terrain after intense rainfall. The research team will collaborate with government agencies, utility providers and community stakeholders throughout the project to help ensure that the tools they develop reflect the practical needs of the people and institutions that will ultimately use them, from county emergency planners deciding where to pre-position resources to utilities weighing which assets to reinforce first.

The project also carries a substantial education and workforce development mission. Researchers will create wildfire-focused educational materials for students ranging from middle school through graduate school, introducing a new generation to the science of wildfire resilience and the related hazards that follow fires. Undergraduate and graduate students will have opportunities to gain hands-on experience through internships with industry partners and national laboratories, building career pathways in a field where demand for expertise continues to grow. The team will make its educational resources and research data publicly available so that other researchers, practitioners and communities can build on the work. If REKINDLE succeeds, its integrated view of fire, flood, infrastructure and human response could reshape how at-risk communities plan for the full arc of a wildfire disaster, not just the day the flames arrive.

Subject of Research: AI-based modeling of cascading wildfire hazards and infrastructure resilience in wildland-urban interface communities

Article Title: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience

Article References: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: wildfire, cascading disasters, artificial intelligence, infrastructure resilience, wildland-urban interface, debris flows, flash floods, National Science Foundation, University at Buffalo, geospatial AI, community resilience, lifeline infrastructure

The study stands apart from most fire research in both scope and focus. Rather than examining wildland blazes in remote forests or dissecting a single catastrophic event, the researchers compiled records of 141 fires that intersected urban areas across the United States, reaching back to 1984. By combining four decades of fire records with population data, development patterns, climate measurements, and demographic information, the team assembled one of the most comprehensive portraits to date of how fire behaves when it crosses into developed landscapes. That long temporal window allowed the researchers to test whether patterns of urban fire exposure have shifted over time, and whether the social and environmental conditions that precede urban fires have remained stable across an era of rapid climate change.

The answer to the second question is a decisive no, at least as far as the profile of the neighborhoods that burn is concerned. According to Dion Kucera, a postdoctoral researcher in urban ecology at UC Riverside and an author of the study, urban fires have consistently clustered in suburban, high-income, owner-occupied developments, and that pattern has not budged in more than 40 years. The numbers bear this out with striking clarity: neighborhoods with substantially higher median incomes had roughly 30 percent higher odds of experiencing fire exposure than less affluent areas. The explanation lies partly in geography and partly in the history of American development. Wealthy enclaves are disproportionately built in fire-prone settings, on slopes and ridgelines and along the wildland-urban interface, where flammable vegetation surrounds homes and steep terrain can accelerate flame spread.

One of the most consequential findings for municipal planners concerns what happens in the months before an urban fire ignites or spreads. The study found that the critical precursor is not long-term drought, the factor most often emphasized in seasonal fire outlooks, but rather a short, intense drying of the environment during the six months preceding the fire. This distinction matters because it changes the timescale at which risk should be monitored. As Kucera explained, three- to six-month moisture measures may be far more useful to city managers trying to identify windows of heightened danger than the longer-term drought assessments that currently dominate many planning frameworks. In practical terms, a city could be emerging from a multi-year drought yet still face acute urban fire risk if recent months have brought rapid desiccation of vegetation within the built environment.

The aftermath of urban fire, the study shows, is often measured in years rather than months. The researchers tracked whether burned neighborhoods returned to their pre-fire ranges of vegetation greenness and surface temperature during the five years following each blaze. For most places, they did not. Approximately 78 percent of severely burned areas failed to return to their pre-fire temperature range within five years, and 73 percent failed to recover their previous level of greenness. The general trajectory for burned urban land was toward hotter and less green conditions, with those ecological shifts persisting long after the flames were extinguished. These are not merely aesthetic changes: elevated surface temperatures and reduced vegetation cover in dense residential areas can alter local microclimates, increase cooling demands on surviving homes, and reshape habitat within city boundaries.

Income emerged as a powerful predictor of recovery as well as exposure. Higher-income neighborhoods tended to regain their pre-fire surface temperatures more quickly and suffered smaller losses in vegetation greenness than poorer areas. The study did not formally test the mechanisms behind this disparity, but the pattern is consistent with well-documented differences in the resources available to wealthy and low-income communities after disasters, including faster rebuilding, more extensive landscaping and irrigation, and greater capacity to fund vegetation restoration. Whatever the underlying drivers, the result points to a compounding inequality: the communities least likely to burn are also the ones best positioned to heal when fire does arrive, while lower-income urban areas that do experience fire face slower and less complete ecological recovery.

The social geography of urban fire also varies sharply by region, a nuance the researchers uncovered by analyzing demographic patterns separately across the country. Nationally, neighborhoods with higher proportions of white residents had lower odds of experiencing fire. In California and other dry regions of the American West, however, that relationship reversed: both white and Black populations experienced greater odds of fire exposure in those states. The reversal likely reflects the particular settlement history of the West, where affluent and diverse communities alike have expanded into fire-prone foothills and canyons. For the researchers, the regional divergence underscores a central argument of the paper: understanding urban fire requires attending to the social characteristics of the places that burn, not just the climate and fuel conditions that govern fire in wildlands.

The study also documented that urban fires behave differently from their wildland counterparts in ways that complicate simple extrapolation. While wildland fires in the United States have been growing both larger and more frequent, the number of fires reaching urban areas did not show a clear increase over the study period. There was, however, evidence that the amount of urban land burned is increasing. That trend was heavily influenced by especially destructive fire years, suggesting that a small number of extreme events may be driving growing losses even without any rise in the overall frequency of urban fire. This pattern echoes a broader theme in fire science, in which the tail of the distribution, the rare but catastrophic fire year, increasingly dominates total damage, and it implies that resilience planning should be calibrated to worst-case scenarios rather than average conditions.

The implications extend beyond ecology and emergency management into the financial architecture of disaster risk. The findings could inform how communities prepare for fire and how insurers assess exposure, although the study did not examine insurance markets directly, and Kucera noted that additional research should explore those questions. If high-income neighborhoods carry systematically higher odds of burning, while low-income areas face slower recovery when they do burn, then both premium structures and public mitigation spending may need to reflect a risk landscape that looks very different from the one implied by wildland fire models alone. More broadly, Kucera argues that scientists need to stop treating fires that enter developed areas as simple extensions of wildland fire. Cities, he contends, have their own patterns of exposure and recovery, shaped by where people build, who lives there, and the resources they can marshal after disaster strikes.

Ultimately, the study reframes urban fire as a problem of social structure as much as climate. The urban fire problem is not governed solely by temperature, precipitation, and fuel loads, as it is in wildlands, but by the spatial distribution of wealth, the historical geography of development, and the short-term moisture dynamics of built environments. As Kucera put it, adapting to the growing intensity of urban fires requires looking beyond the forest and into the social fabric of our cities. With 141 fires and four decades of evidence now assembled, the researchers have provided planners, insurers, and residents with a clearer map of where urban fire strikes, when it strikes, and who is left waiting longest for their neighborhoods to turn green and cool again.

Subject of Research: Urban fire exposure and unequal ecological recovery across U.S. neighborhoods

Article Title: Rich neighborhoods more likely to burn

Article References: Rich neighborhoods more likely to burn. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: urban fire, wildland-urban interface, fire risk, income inequality, ecological recovery, surface temperature, vegetation greenness, drought, urban planning, California, npj Urban Sustainability, UC Riverside

The study examined 1,400 Maui adults, measuring concentrations of 24 different metals in urine samples while simultaneously assessing pulmonary function through standardized breathing tests conducted at the same visit. When researchers compared the results against national biomonitoring reference data from the United States, the disparities were striking. Participants showed antimony concentrations nearly 40 times higher than U.S. reference values, manganese at 3.8 times, barium at 2.3 times and arsenic at 2.2 times the national benchmarks. These are not trace deviations; they represent a population carrying a metal burden far beyond what typical American residents experience.

The pulmonary findings may prove even more consequential. Across three complementary statistical approaches, higher combined metal exposure was consistently associated with several measures of abnormal lung function. Arsenic, cadmium, copper and antimony emerged as the metals contributing most strongly to those patterns. Each of these substances has well-documented toxicological profiles: arsenic and cadmium are established carcinogens, antimony is a metalloid used in flame retardants and electronics, and copper in excess can damage respiratory tissue. The convergence of elevated body burdens and measurable respiratory impairment in the same individuals points to a public health problem that outlasts the emergency phase of a disaster by many months.

The researchers are careful, however, not to attribute the entire burden to the fires alone. Because no pre-wildfire biomonitoring data exist for these residents, the study cannot determine precisely how much of the measured metal load came directly from the August 2023 disaster versus chronic, persistent or other ongoing exposures. Hawaiʻi’s geology, a long history of agricultural and industrial land use, diet and other environmental factors may all have contributed to exposures that predated the fires. The disaster may then have introduced entirely new contaminants while simultaneously disturbing and remobilizing pollutants already locked in soil, dust and the built environment. Disentangling those pathways, the team says, will require longitudinal biomonitoring and environmental sampling over years.

What makes the Maui situation scientifically distinct is the nature of the fire itself. Much of the existing knowledge about wildfire health effects derives from studies of smoke generated by burning vegetation. But the Lahaina fire belonged to a different and increasingly common category: the wildland–urban interface disaster, in which flames sweep through neighborhoods rather than forests. Such fires consume homes, vehicles, electronics, treated lumber and electrical systems, generating complex chemical mixtures that bear little resemblance to wood smoke. At the same time, they disturb pollutants accumulated in soil from decades of earlier agricultural, industrial or other land uses, redistributing them across the landscape during cleanup and recovery.

Those materials can remain in ash, settled dust, soil and debris long after the flames are extinguished, creating continued exposure pathways that include resuspended fine particles, direct contact with contaminated dust and debris, and indirect environmental exposure even when the air looks clear. This is the mechanism the MauiWES team believes explains why metal burdens remained elevated in residents’ bodies many months after the disaster. When a fire burns through an entire community, it can fundamentally alter how contaminants move through the environment, transforming a static contamination landscape into a dynamic and prolonged source of human exposure.

The geographic patterns uncovered by the study add another layer of complexity. Exposure was not confined to Lahaina, the town most closely associated with the disaster. Lahaina residents did show a distinct metal profile, including relatively elevated concentrations of arsenic and cadmium, but Wailuku and Kahului residents recorded the highest median concentrations of copper, iron and molybdenum. Kula and Kihei displayed different metal profiles as well, underscoring that no single uniform exposure pattern existed across the island. Pulmonary impairment varied in parallel: among interpretable breathing test results, Wailuku and Kahului exhibited some of the highest prevalence of abnormal lung-function measures, while Lahaina also showed substantial impairment and Kula generally showed the lowest burden.

These spatial differences suggest that the post-disaster exposure landscape is far more complicated than a simple comparison between people inside and outside a burn zone. The researchers believe the measured metal mixtures reflect overlapping contributions from wildfire-generated material, contaminants remobilized by the disaster, historically contaminated soils, local land use, geology and other ongoing environmental sources. Determining the relative contribution of each pathway is now a central focus of the research program, and the answer will shape how communities recovering from urban fires should be monitored and protected.

The implications extend well beyond Hawaiʻi. As destructive fires increasingly reach developed communities across the American West, Australia, the Mediterranean and elsewhere, the Maui findings suggest that disaster-response systems may need to look beyond short-term measurements of smoke and particulate matter. Future responses, the researchers argue, could include longer-term environmental monitoring, human biomonitoring, respiratory surveillance and targeted assessment of soil, ash and settled dust, particularly in places with histories of agricultural or industrial contamination. In September 2026, the National Institute of Environmental Health Sciences awarded the team a five-year, $3.6 million grant to investigate pollutants left behind by decades of agricultural and industrial activity in Hawaiʻi and to examine whether wildfires and other disasters can remobilize those contaminants into new pathways of human exposure.

That new project, led by MauiWES co-directors Alika Maunakea and Ruben Juarez together with Alison Lee of the Icahn School of Medicine at Mount Sinai, will follow approximately 1,000 Hawaiʻi residents over five years, combining environmental measurements, biological samples and cardiopulmonary assessments. Its goal is to distinguish persistent background exposures, legacy contamination and disaster-related exposures while examining how each contributes to heart and lung health over time. Together, the studies shift the scientific question from simply asking what a wildfire released to a broader and more consequential one: how a disaster transforms an existing environmental contamination landscape and changes what people are exposed to in the months and years that follow.

The research team has also emphasized that the study was designed not merely to document harm but to connect participants with information and care. MauiWES participants receive access to their individual health results, opportunities to discuss those findings with the research team, and referrals when screening identifies potential health concerns. The study will continue following families over time to understand how health evolves during Maui’s recovery. Juarez acknowledged that the findings may be difficult for families who have already endured so much, but stressed that the team’s responsibility extends beyond publishing the science to returning information to the community, connecting people with care when they need it, and continuing to learn alongside Maui as the island rebuilds. For now, the central lesson stands: exposure may not end when the smoke clears, and the next scientific challenge is to understand which exposures persist, where they come from and what they mean for long-term health.

Subject of Research: Persistent toxic metal exposure and pulmonary impairment among residents affected by the 2023 Maui wildfires

Article Title: EMBARGOED: Toxic metals found in Maui residents months after 2023 wildfires, UH study finds

Article References: EMBARGOED: Toxic metals found in Maui residents months after 2023 wildfires, UH study finds. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Maui wildfires, Lahaina, toxic metals, biomonitoring, lung function, arsenic, cadmium, antimony, wildland-urban interface, environmental health, University of Hawaii, PNAS