The WBGT is the workhorse of heat stress assessment worldwide. Unlike a simple thermometer reading, it is an ISO-standardised index that integrates four environmental factors at once: air temperature, humidity, wind speed, and radiation, covering both shortwave radiation from the sun and longwave radiation emitted by surrounding surfaces. Because it blends these variables into a single number, WBGT is widely used to trigger heat management plans at sporting events, military training exercises and industrial sites. When the index crosses agreed thresholds, organisers can activate shade stations, adjust pacing requirements, or postpone starts. The problem, until now, has been that WBGT is usually measured at a handful of fixed weather stations, which tells organisers almost nothing about what a walker experiences deep in a sun-baked forest clearing or on a shaded stretch of canal path two hours into the course.

That gap matters because heat stress at the human scale is intensely local. A runner moving through alternating sun and shade, over asphalt and through woodland, encounters rapidly shifting microclimates that a regional weather model simply cannot resolve. To capture this variability, the research team—Kobe Vandelanotte, Bram Du Moulin, Siebe Puynen, Thomas Vergauwen, Sara Top and Steven Caluwaerts—deployed a mobile measurement campaign during the 2024 Dodentocht. Instruments were carried along the route to collect spatio-temporal WBGT data as the event unfolded, producing a moving picture of heat stress conditions that matched the actual experience of participants rather than the conditions at a distant airport weather station.

These field observations served a second, crucial purpose: they allowed the team to evaluate a modelling system designed to simulate heat stress at metre-scale resolution. The system couples a numerical weather prediction model, which handles the broader atmospheric state, with a micro-scale radiation model that resolves how buildings, trees and terrain shape the radiation environment street by street and field by field. When the modelled WBGT values were compared against the mobile measurements along the 100-kilometre route, the system delivered what the authors describe as a reasonable approximation of observed conditions. Importantly, it was able to capture some local shade-induced cooling effects—the dips in heat stress that occur when a route passes under tree cover—demonstrating that the micro-scale radiation component was doing real work rather than smoothing over the fine-grained detail.

With the model validated, the researchers turned it from a diagnostic instrument into a decision-making tool. They simulated a series of alternative event-management scenarios for the 2024 Dodentocht, asking a question that every endurance organiser now confronts: what could we have done differently? The scenarios included shifting the start time, rerouting the course, and adjusting assumed walking speeds, each of which changes when and where participants encounter the hottest part of the day and how much heat they accumulate over the event.

The headline finding is striking. For the 2024 edition, starting the event three hours earlier—at 18:00 local time instead of the official 21:00 start—would have reduced cumulative heat-stress exposure by 35 percent, measured as degree hours above a WBGT threshold of 20 degrees Celsius. That single scheduling decision, achievable with no change to the route and no additional infrastructure, would have removed roughly a third of the dangerous heat load carried by participants. In practical terms, an earlier start shifts more of the course into the cooler evening and overnight hours, so that walkers reach the most punishing midday stretch further along, or in some cases past, the peak of the day’s heat.

Route choice proved equally consequential. Under identical meteorological conditions and the official 21:00 start time, the 2023 route accumulated 16.5 percent more degree hours above 20 degrees Celsius WBGT than the 2024 route. The difference illustrates how sensitive endurance events are to seemingly small course design choices: the amount of shade, the exposure of open stretches, and the timing with which the route delivers participants into sun-exposed sections all feed directly into cumulative exposure. A course that is a few kilometres longer but better shaded, or one that reorders its segments so that open terrain is crossed at night, can meaningfully lower the health risk for every participant on the field.

The metric the team uses—degree hours above a WBGT threshold—deserves attention because it captures what a single snapshot cannot: the accumulation of heat stress over time. Physiologically, heat illness risk depends not just on how hot it is, but on how long the body is working hard while hot, and whether it gets a chance to recover. A walker crossing a hot, sunlit section at kilometre 60 after twenty hours of exertion faces a very different risk profile from one crossing the same section fresh. Cumulative exposure metrics of this kind therefore align better with the way heat accumulates in the human body than peak-temperature warnings alone, and they give organisers a quantifiable target to optimise against rather than a binary go/no-go decision.

The broader context makes tools like this increasingly urgent. Climate change is driving more frequent and more intense extreme heat episodes across Europe and beyond, and participants in outdoor endurance events are among the most exposed populations, combining prolonged outdoor duration with high metabolic heat production from sustained physical exertion. Mass-participation events—from marathons and ultramarathons to long-distance walking events and cycling sportives—have already seen cancellations, medical surges and, in tragic cases, fatalities linked to heat. Organisers traditionally rely on historical averages and fixed station data to plan, but a warming climate is eroding the reliability of the past as a guide. High-resolution heat stress modelling offers a way to plan for the conditions that will actually occur, and to stress-test contingency plans before race day rather than improvising during a medical emergency.

What this study demonstrates, ultimately, is a workflow: measure, validate, simulate, decide. Mobile observations ground-truth the model; the validated model becomes a virtual laboratory in which organisers can compare start times, routes and pacing scenarios; and the resulting exposure estimates feed directly into heat management plans, from shade and hydration placement to medical staffing and cancellation thresholds. The authors present the system as a reliable and actionable tool for developing and implementing effective heat management strategies for outdoor endurance events, and the Dodentocht results show that the payoff can be large—an earlier start alone cutting exposure by more than a third. As summers grow hotter, the events that adapt fastest will be those that treat heat stress not as an act of nature to be endured, but as a design variable to be engineered, modelled and minimised long before the first participant takes a step.

Subject of Research: High-resolution wet bulb globe temperature modelling to assess and reduce heat stress exposure in ultra-endurance events

Article Title: Too hot to race? Demonstrating heat stress modelling as an adaptation tool for endurance events

Article References: Too hot to race? Demonstrating heat stress modelling as an adaptation tool for endurance events. (n.d.). https://doi.org/10.1371/journal.pclm.0001058

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0001058

Keywords: heat stress, wet bulb globe temperature, endurance events, numerical weather prediction, micro-scale radiation modelling, climate change adaptation, ultra-endurance, Dodentocht, heat management, WBGT, event planning, Belgium

The stakes are far from academic. Global mean air temperature has already risen by roughly 1.1 degrees Celsius, and urban areas suffer additional warming from the urban heat island effect, in which asphalt, concrete, and dense building fabric trap heat long after sunset. Epidemiological investigations in Paris, Chicago, and Shanghai have documented substantial spikes in heat-related mortality during extreme heat events, and global assessments estimate that around three billion people now experience heat stress conditions exceeding safe physiological thresholds. Against this backdrop, dozens of indices have been developed to translate raw meteorological variables such as air temperature, humidity, wind speed, and radiation into a single integrated value of human thermal load. They fall into four broad families: empirical indices like the Heat Index and Humidex, bioclimatic indices such as the Wet Bulb Globe Temperature, rational heat-balance indices like the Physiological Equivalent Temperature and the Universal Thermal Climate Index, and physiological strain indices such as Predicted Heat Strain.

The trouble, as comparative studies have repeatedly shown, is that these indices disagree. Work in humid Hong Kong, Singapore, and Guangzhou found that humidity-sensitive indices such as UTCI and the Heat Index best captured perceived thermal stress, while studies in arid Tehran, Phoenix, and Riyadh concluded that solar radiation and wind speed dominate thermal strain, favoring PET and WBGT. The same weather conditions can therefore land in different heat stress categories depending on which index is applied. Yet most urban climate and occupational health studies never justify their choice of index at all, defaulting to whatever is conventional in their region or discipline. Middle Eastern researchers tend to lean on WBGT because it is embedded in occupational guidelines, while European investigators often champion PET and UTCI. The new study set out to replace this habit with a transparent, reproducible method.

The framework unfolded in four stages across three carefully chosen cities: Abadan, a hot-humid city near the Persian Gulf; Isfahan, a hot-dry city at 1,570 meters altitude; and Gorgan, a temperate-humid city near the Caspian Sea. First, the team compiled a pool of twenty candidate indices through a systematic search of Scopus, Web of Science, PubMed, and Google Scholar. Second, they applied the Fuzzy Delphi Method, a structured consensus technique in which twenty-six experts with at least five years of experience in heat stress, thermal comfort, occupational health, or climatology rated each index on a five-point linguistic scale. The verbal judgments were converted into triangular fuzzy numbers, aggregated, and defuzzified using the center-of-gravity method. Only indices achieving a defuzzified score of at least 0.70 and sufficient expert consensus survived; exactly ten did, led by WBGT with 96.2 percent consensus, followed by PHS, UTCI, and PET, all above 88 percent. Simpler empirical indices clustered near the bottom of the accepted list.

Third, the researchers used the Fuzzy Analytic Hierarchy Process to rank the ten surviving indices against five evaluation criteria: physiological relevance, climatic sensitivity, outdoor applicability, data requirements, and scientific validation support. The expert panel judged physiological relevance the most important criterion, with a normalized weight of 0.370, followed by scientific validation support at 0.229 and climatic sensitivity at 0.210. Pairwise comparisons were performed separately for each city, yielding climate-specific global weights and rankings. The results were unambiguous. In steamy Abadan, WBGT took first place with a final weight of 0.178, followed by PHS and UTCI. In arid Isfahan, UTCI led at 0.171, ahead of PET and mPET. In temperate Gorgan, PET ranked first at 0.166, with UTCI and SET close behind. Across all three climates, the simplest empirical indices, the Heat Index and Humidex, languished near the bottom of the table.

Crucially, the team did not stop at expert opinion. They subjected the rankings to a one-at-a-time sensitivity analysis, perturbing each criterion weight by plus or minus 10, 20, and 30 percent while rescaling the remaining weights. The top-ranked index in every city survived every perturbation, and Spearman rank correlations between baseline and perturbed rankings ranged from 0.88 to 0.97, indicating that the climate-specific hierarchies are structurally embedded in the decision framework rather than artifacts of particular expert judgments. All pairwise comparison matrices also met the standard consistency threshold, with consistency ratios below 0.10, reinforcing confidence in the aggregated expert judgments.

The fourth and most novel stage brought real human bodies into the equation. Between April and September 2025, the researchers surveyed 300 adults, one hundred per city, recruited through stratified convenience sampling to represent walking, standing, and light work in streets, parks, and commercial areas. Calibrated instruments measured air temperature, humidity, wind speed, black globe temperature, and WBGT at roughly 1.1 meters above ground, the center of gravity of a standing adult, synchronized with each participant’s Thermal Sensation Vote on the standardized ASHRAE seven-point scale from minus three, cold, to plus three, hot. Perceived heat differed dramatically across the cities: the mean TSV was plus 2.10 in Abadan, where 68 percent of respondents reported feeling hot or very hot, compared with plus 1.52 in Isfahan and just plus 0.84 in Gorgan, where only 27 percent reached the hot end of the scale.

When the continuous index values were correlated against these field votes, the climate dependence of index performance became vivid. In Abadan, WBGT showed the strongest agreement with thermal sensation, a Spearman coefficient of 0.79, with PHS close behind at 0.74. In Isfahan, UTCI peaked at 0.76 and PET at 0.73. In Gorgan, PET led at 0.72, followed by UTCI at 0.69 and SET at 0.67. Grouped by category, physiological indices achieved a mean correlation of 0.70 and thermal comfort indices 0.68, while empirical indices trailed at 0.49. Most tellingly, when the expert-derived FAHP rankings were compared with rankings built purely from the perception data, the agreement was strong in all three cities, with Spearman coefficients of 0.82 in Abadan, 0.79 in Isfahan, and 0.81 in Gorgan, and the top three indices were identical in both rankings everywhere. The mean values of the top-ranked indices also rose monotonically across successive thermal sensation categories, showing they can discriminate progressive levels of perceived strain, an essential property for heat-warning systems.

The physiological logic behind these patterns is coherent. In humid climates, high moisture content of the air suppresses evaporative heat loss, so indices that explicitly track humidity-driven heat storage and sweat evaporation, like WBGT and PHS, track real strain best. In arid environments, intense solar radiation and wind dominate the energy balance, which is precisely what the radiation-sensitive, heat-balance indices UTCI and PET capture. In milder temperate-humid settings, severe physiological strain is less dominant and cumulative discomfort and behavioral adaptation matter more, favoring comfort-oriented indices that integrate energy balance with perceptual dimensions. The authors acknowledge limitations, including a predominantly Iranian expert panel, convenience sampling, a warm-season-only data window, and the omission of micro-scale urban morphology variables such as sky-view factor and vegetation shading. They call for extending the framework across seasons, adding urban form parameters, and testing dynamic indices under projected climate-change scenarios.

The practical message for city planners, public health authorities, and occupational safety bodies is direct: stop assuming one standard indicator fits everywhere. A heat-health warning system in a humid coastal city that relies on a dry-climate comfort index may understate danger precisely when evaporative cooling fails, while an arid inland city using a humidity-centric index may miss the dominant role of radiant heat. By fusing fuzzy expert consensus, multi-criteria weighting, sensitivity testing, and ground-truth human perception into a single transferable methodology, the study offers a template any city can adapt. As global warming pushes more urban populations past safe physiological thresholds, knowing which thermometer of human strain to trust may prove as important as the measurements themselves.

Subject of Research: Climate-sensitive prioritization of human heat stress indices in urban environments

Article Title: A climate-sensitive decision-support framework for prioritizing human heat stress indices: Integrating fuzzy Delphi, FAHP, and thermal perception in Abadan, Isfahan, and Gorgan, Iran

Article References: Mousavi, S. M., Dehghan, H., & Yazdanirad, S. (2026). A climate-sensitive decision-support framework for prioritizing human heat stress indices: Integrating fuzzy Delphi, FAHP, and thermal perception in Abadan, Isfahan, and Gorgan, Iran. Environmental and Sustainability Indicators, 32, Article 101529. https://doi.org/10.1016/j.indic.2026.101529

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101529

Keywords: heat stress, thermal comfort, WBGT, UTCI, PET, urban climate, Fuzzy Delphi, FAHP, thermal sensation, climate change, Iran, decision support

The scientific team characterized the event using several complementary indicators. Drought conditions were quantified with the three-month Standardized Precipitation-Evapotranspiration Index, or SPEI-3, which captures anomalies in the surface water balance by comparing precipitation against potential evapotranspiration estimated with the Penman-Monteith formulation. Heat stress was measured with the Wet Bulb Globe Temperature, or WBGT, a metric that integrates temperature, humidity, and evaporative constraints and is widely regarded as one of the most relevant indicators of human thermal strain. During September to November 2020, WBGT values in the region persistently exceeded the 95th percentile of the 1979-2022 climatology, while SPEI values revealed drought conditions stretching back through 2019 and into 2020.

What made the 2020 event particularly unusual was its timing relative to the seasonal cycle. The extreme period was not confined to the climatological dry season but emerged during a transition between anomalously dry rainy seasons, indicating a disruption of the typical pattern in which rainfall recharges the landscape from October through April. Observations from Brazil’s national meteorological service, analyzed with the CTX90pct heatwave index, showed that heatwave frequency peaked in 2020, the hottest year on record for the region. The prolonged dry conditions during 2019 likely enhanced atmospheric evaporative demand, and the delayed onset of the following rainy season further amplified heat accumulation and moisture deficits by prolonging land-atmosphere feedbacks associated with reduced surface moisture.

To quantify the human fingerprint, the researchers turned to two independent modeling frameworks. The first was HadGEM3-A, the Met Office’s high-resolution atmosphere-only attribution model, which runs at roughly 60-kilometer resolution over Brazil and provides large ensembles of up to 525 members. The model was run in two configurations: a realistic world including both anthropogenic and natural forcings, and a counterfactual world driven only by natural factors such as solar variability and volcanic eruptions. When the observed 2020 WBGT anomaly was placed against the probability distributions from these two worlds, the result was stark. The observed event sat in the upper tail of the anthropogenically forced simulations, while the probability density under natural-only forcing was close to zero at that threshold, yielding a Fraction of Attributable Risk of 0.99 with a 95 percent confidence interval of 0.97 to 1.00.

The second framework drew on simulations from the Coupled Model Intercomparison Project Phase 6, using three structurally different models: MIROC6, CanESM5, and ACCESS-ESM1-5. All three showed the same directional pattern, with simulations including anthropogenic forcing systematically shifted toward higher WBGT anomalies relative to natural-only runs. CanESM5 gave event probabilities under natural conditions that were effectively zero, producing a probability ratio greater than 1000. ACCESS-ESM1-5 showed a median probability ratio of 860, and MIROC6 produced a lower but still substantial value of 129. Because the two frameworks capture different sources of uncertainty, with HadGEM3-A constraining internal variability through large ensembles and CMIP6 quantifying structural uncertainty across model physics, the consistency of their results strengthens the conclusion that human-caused warming dramatically reshaped the odds of the event.

The climatic anomalies did not remain an abstract statistical finding; they translated into fire on the ground. Burned area estimates derived from the MODIS MCD64A1 satellite product, intersected with the MapBiomas land cover dataset, revealed a pronounced asymmetry in where the fires burned. Approximately 80 percent of the total burned area, about 1920 square kilometers, occurred in non-forest land cover classes dominated by pasture, while forest formations accounted for roughly 20 percent, about 468 square kilometers. Pasture fires showed a clear seasonal signal, increasing sharply from August to October before declining in November, reflecting the rhythms of fire use in land management. Forest fires were more spatially heterogeneous and less seasonal, with hotspots in municipalities such as Feijó, Tarauacá, and Porto Walter concentrated in September and October.

The smoke from these fires, combined with the extreme heat, left a measurable mark on public health. Between September and November 2020, 39,453 respiratory disease notifications were recorded across Acre, corresponding to 5.4 percent of the state’s population. The capital, Rio Branco, concentrated the largest absolute burden with more than 15,000 cases and 159 reported deaths, but the highest proportions of affected residents appeared in smaller, more vulnerable municipalities such as Assis Brasil, Santa Rosa do Purus, Acrelândia, and Porto Walter, where lower population density, geographic isolation, and limited healthcare infrastructure amplified the relative impact.

To understand the environmental drivers behind these health outcomes, the team fitted generalized linear models with a Poisson error distribution for each month of the peak fire season, using predictors including fine particulate matter concentrations, burned area, precipitation, air temperature, wind speed, wind direction, and aerosol optical depth. The models performed well overall, with September showing the strongest fit and most predictors statistically significant. A geographically weighted regression analysis then revealed that the influence of environmental variables was anything but uniform. Particulate matter exhibited by far the strongest positive average association with respiratory notifications, though with substantial local variability, while wind speed also showed a positive average effect. Temperature, precipitation, aerosol optical depth, and burned area showed weaker or mixed average relationships, and their coefficients varied considerably from one municipality to another.

This spatial heterogeneity carries an important lesson: municipalities exposed to similar environmental conditions did not necessarily experience similar health outcomes. Relative risk values from the regression framework ranged from approximately 0.18 to 1.57, indicating localized areas with substantially fewer or more notifications than expected. The authors interpret these patterns as evidence that respiratory outcomes were shaped not only by smoke exposure but also by social and territorial factors, including socioeconomic conditions, healthcare accessibility, demographic composition, and housing quality. The study period also coincided with the COVID-19 pandemic, which may have influenced healthcare-seeking behavior, reporting practices, and clinical overlap of respiratory symptoms, adding an acknowledged layer of uncertainty to the interpretation of the notification counts.

The researchers also examined the policy landscape and found striking gaps. No heatwave-specific records appeared in Brazil’s official disaster registry for the study period, suggesting that heatwaves are not yet consistently classified as a formal disaster category in the country, where drought remains the primary designation for heat-related impacts. In contrast, all municipalities in Acre reported wildfire-related disaster events in 2020, classified as fires affecting air quality. The authors argue that reducing future risks will require integrated adaptation strategies that combine climate-risk management, wildfire prevention, air-quality monitoring, and public health preparedness, including early warning systems, fire-free land management techniques, water resource conservation, community resilience building, and the intersectoral coordination now mandated by recent Brazilian climate adaptation legislation.

Taken together, the findings from Acre illustrate how a single climatic anomaly can propagate through environmental and social systems to produce uneven and cascading impacts. Anthropogenic climate change did not merely raise temperatures; it contributed to the drought conditions, vegetation drying, and fire-weather environment that allowed burning to expand and smoke to accumulate over populated areas. The 2020 event, the researchers conclude, should be understood not as an isolated anomaly but as part of an ongoing shift in the probability distribution of climate extremes driven by human forcing, a shift that is already reshaping the risks faced by vulnerable communities across the western Amazon.

Subject of Research: Attribution of the 2020 compound heatwave, drought, wildfire, and respiratory health event in the western Brazilian Amazon to anthropogenic climate change

Article Title: Western amazon 2020 extreme heatwave

Article References: Dutra, D. J., Veiga, R. Q., Abreu, R. C., Huang, W. T. K., dos Santos, J. C., Cunha-Zeri, G., de Luna Francisco, Q.-H. R., Silva de Lima, Y. M., Leyton, A. O., da Silva, L. A., Quevedo, R. P., Goulart Fiscina, L. F., Sousa, M., Calado, B. N., Cunningham, C., Carneiro da Silva, I. S., Noronha, P., de Meneses, E., Leão, H., … Anderson, L. O. (2026). Western amazon 2020 extreme heatwave. Regional Environmental Change, 26(4), Article 192. https://doi.org/10.1007/s10113-026-02681-0

Image Credits: AI Generated

DOI: 10.1007/s10113-026-02681-0

Keywords: climate change, heatwave, Amazon, drought, wildfires, event attribution, heat stress, WBGT, public health, respiratory disease, Acre, CMIP6