increasingly apparent, as climate change poses growing threats to public welfare
worldwide. Rising temperatures, more frequent and intense extreme weather events
(e.g., heatwaves, floods, droughts), and environmental stressors such as air
pollution, degraded ecosystems, or shifting land use patterns have direct and indirect
impacts on population health. Climate-related changes also affect the distribution of
vector- and waterborne diseases, contribute to the severity of wildfires, and influence
mental and physical health outcomes.
Addressing these multifaceted challenges requires close collaboration between
disciplines, bringing together climate scientists, epidemiologists, environmental and
public health researchers, as well as social scientists. This session provides a
platform for presenting recent advances in understanding and quantifying
environment- and climate-related health risks through the integration of diverse data
sources, including remote sensing, environmental monitoring, climatological
measurements, health records, and socio-demographic information.
We welcome contributions that explore methods to assess climate-sensitive
exposures (e.g., heat, air pollution, allergens), model health impacts across different
temporal and spatial scales, develop risk maps, and evaluate adaptation or mitigation
strategies. Approaches using machine learning, statistical modelling, scenario
analysis, and other innovative tools are encouraged. Both empirical studies and
methodological advances, whether focused on local, regional, or global scales, are
invited. The session aims to foster cross-sectoral exchange and support the
development of data-driven strategies for climate-resilient and equitable public
health.
Orals: Tue, 5 May, 14:00–17:55 | Room 2.24
Early warning systems (EWS) for environmental hazards are increasingly implemented across regions and play an important role in protecting population health. However, most existing systems remain predominantly hazard-based rather than health impact-based, relying on simple threshold exceedances and colour-coded alerts. While such warnings provide a useful first indication of elevated risk, they often lack direct relevance for health-protective decision-making, action and behaviour by, e.g., local authorities, health services, care facilities, employers, and the public. This limitation is particularly important for heat and air pollution, which are typically addressed through separate warning systems despite strong epidemiological evidence that their health effects interact. Numerous studies show synergistic effects on death and disease from concurrent exposure to high temperatures and air pollution (PM₂.₅ and ozone), especially from cardiovascular and respiratory causes, implying that health risks during compound events exceed the sum of individual hazards. Failing to consider these interactions may therefore result in underestimation of risk during such events. Moreover, most available evidence on joint heat and air pollution health risks comes from temperate, high-income settings, while many of the world’s hottest regions are those also experiencing the highest air pollution levels.
Beyond improving risk detection, joint consideration of heat and air pollution offers a major opportunity for health co-benefits. Analyses from the Horizon 2020 RIA project EXHAUSTION show that accelerated air-pollution reduction can function as a powerful adaptation strategy to extreme heat. Integrating heat–air pollution interaction effects into regional mortality and welfare-cost projections revealed that achieving WHO’s annual PM₂.₅ guideline level could reduce heat-related cardiopulmonary mortality by nearly 40% in Europe over the coming decades. Particularly large benefits were found for Balkan and Mediterranean regions where high heat exposure and air pollution coincide and the annual welfare economic costs from heat-related mortality reach billions of Euros.
In the ongoing COPE project in India – a country experiencing increasingly frequent and intense heatwaves and home to many of the world’s most polluted cities – we directly respond to the evidence on joint heat and air pollution effects. Working with local partners, we aim to develop an Early Warning and Decision-support System for heat and air pollution in Delhi and Kolkata. We investigate whether alert thresholds should be dynamically adjusted when heat and air pollution co-occur, and whether vulnerability varies by season, warranting differential season-specific alert thresholds. We draw upon insights from the Horizon CSA project ENBEL, which highlight key technical (data and modelling constraints), structural (institutional capacity and funding), and societal (risk communication and equity) barriers to effective heat-health warning systems, as these lessons are directly applicable to the development of integrated heat–air pollution warnings. In COPE, the research is co-designed and conducted in collaboration with user groups from vulnerable populations and stakeholder partners from the health and governmental sector to ensure that alerts reach all relevant users and include meaningful and tailored actionable guidance for different users. We suggest that integrated, impact-based and action-oriented early warning systems are essential for effective, equitable climate-health adaptation in a warming and, in many places, increasingly polluted, world.
How to cite: Aunan, K. and Chowdhury, S.: Beyond single-hazard alerts: Rethinking heat and air pollution Early Warnings Systems in high-exposure settings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6928, https://doi.org/10.5194/egusphere-egu26-6928, 2026.
Extreme event attribution is a branch of climate science that aims to quantify the extent to which the frequency and intensity of extreme weather events such as heatwaves, cold spells, droughts and floods can be said to have been influenced by human-caused climate change. Extreme heat is the deadliest type of weather, although heat-related illnesses and deaths are not directly captured in death certificates or hospital records, and the risks are rarely appreciated by the public. In this talk we introduce a recent collaboration between scientists at Imperial College London and the London School of Hygiene and Tropical Medicine that brought together established methods from attribution and epidemiology to estimate in near real time the expected number of heat-related deaths in cities across Europe during the summer of 2025, and the proportion of those deaths that can be attributed to human-caused climate change. Across 854 cities in Europe we found an estimated 24,404 (95% interval: 21,968 - 26,806) excess deaths during the summer months, with almost 70% of those attributable to human-caused climate change, although vulnerability to heat varies across the continent. This work received widespread media attention, showing the importance of timely information for public awareness of both the risks to health and the contribution of climate change as the extreme weather unfolded.
How to cite: Barnes, C., Konstantinoudis, G., Masselot, P., Mistry, M., Gasparrini, A., Vicedo-Cabrera, A. M., Clarke, B., Theokritoff, E., and Otto, F.: Near-real-time attribution of mortality to extreme heat, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12610, https://doi.org/10.5194/egusphere-egu26-12610, 2026.
Compound heatwave–high ozone pollution events (CHOPs) represent an emerging climate–health challenge, yet their drivers and long-term population impacts remain insufficiently quantified. Using 2000–2022 high-resolution climate and environmental datasets, together with updated epidemiological evidence for compound heat–ozone risks and machine-learning diagnostics, we show that CHOP occurrences in Eastern–Northern China (ENC) have risen by nearly 3.7‐fold since 2013—far exceeding the increases in isolated heatwaves (1.85-fold) and ozone events (2.66-fold). We identify Western Pacific Warm Pool (WPWP) warming as a dominant climatic precursor that strengthens tropical–midlatitude ocean–atmosphere coupling and reinforces a persistent barotropic high-pressure ridge over ENC. This circulation pattern produces simultaneous heat accumulation, stagnant ventilation, and enhanced photochemical ozone formation, thereby amplifying compound extremes beyond the sum of their individual components. The intensified CHOPs have markedly elevated health burdens. Among older adults, CHOP-related mortality risks have nearly quadrupled, while the associated economic losses now exceed 14.3 billion CNY annually—an increase of more than threefold compared to the early 2000s. These disproportionate impacts highlight the vulnerability of aging populations to compounding climate and air-quality stressors. By revealing the teleconnection pathways that modulate CHOP variability and quantifying their escalating human and economic costs, this study provides a scientific foundation for climate-informed seasonal forecasts, targeted early-warning systems, and equitable adaptation strategies. Our findings underscore the necessity of integrating large-scale climate precursors into compound-risk assessments to safeguard public health under a warming climate.
How to cite: Zhu, S.: Western Pacific Ocean Warming Intensifies Heat–Ozone Compound Extremes and Population Health Risks in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-705, https://doi.org/10.5194/egusphere-egu26-705, 2026.
Heat wave (HW) is a hazardous climate extreme that can lead to serious impacts on human health, posing challenges to the UN Sustainable Development Goals #3, #11, and #13. This study examines the heat wave characteristics across South Asia and surrounding regions during a 45-year period (1980-2024) with a particular focus on recent intensification and increasing population exposure. Daily 2-m air temperature data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) was used for identification of hot days and resultant heat waves. The annual count of hot days (DH) is the number of days the daily maximum temperature surpasses 90th percentile daily maximum temperature, whereas hot nights (NH) refers to exceedance of 90th percentile daily minimum temperature, calculated over a 15-day moving window representing long term climatology of the time of the year being considered. A minimum of three consecutive compound hot day and nights was identified as a HW event. Three HW indices were computed annually: the number of HW events (HWn), the number of HW participating days (HWp), and HW magnitude (HWm), which accounts for combined daytime and nighttime temperature departures. For each grid location, these indices were aggregated at decadal scales from the 1980s to the 2020s to examine the evolution of the HW characteristics across the study domain.
The study revealed that globally, NH has increased substantially (266.19%) from 1980s to 2020s leading to more frequent HWs (44,907 events/year in 1980s to 333,424 events/year in 2020s) during the study period. In Peninsular India, HWn was found to be as high as 98 events and HWp was as high as 533 days in the 2020s. HWm was even more than 500 °C² in some locations in Eastern Asia during the same decade, indicating that both day-time and night-time temperatures showed large anomalies with respect to the long-term climatology.
To quantify the impact of rising heatwaves on rising population, gridded population data from WorldPop of University of Southampton was used to determine the change in population exposure to the HW indices over the recent decade (2015–2024). The major cities with marked increase in population exposure to HW occurrences (i.e., HWn) were identified as Zhengzhou (China), Chengdu (China), Dhaka (Bangladesh), Faridabad (India) and Lahore (Pakistan) with exposure changes ranging from 2.92 × 107 person-events to 6.34 × 107 person-events. The maximum change in population exposure to DH is in Istanbul, Turkey (1.57 × 108 person-days) whereas the same for NH is in Ho Chi Minh City, Vietnam (5.61 × 108 person-days). The rising exposure to NH indicates that many cities are losing the ability of natural night-time cooling and require targeted intervention. Thus, this study offers valuable insights on the spatial and temporal evolution of heat wave characteristics across the most densely populated regions of the world and is expected to be useful for developing policies on climate-resilient urban infrastructure planning.
How to cite: Mandal, N. S., Mandal, N., Das, P., and Chanda, K.: Evolution of Heat wave characteristics across South Asia and identification of the most affected cities in the recent decade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16288, https://doi.org/10.5194/egusphere-egu26-16288, 2026.
Extreme temperatures are the leading cause of climate-related mortality worldwide. To inform mitigation and adaptation strategies, it is crucial to have accurate feels-like temperature measures that quantify thermal stress on human physiology. The Universal Thermal Climate Index (UTCI) is among the most widely used feels-like temperature metrics in climate-health research, applicable in a large range of weather conditions. UTCI is also used by several national and international weather forecasting services to predict thermal stress and issue warnings. However, because of the high complexity of the full UTCI model and associated computational cost, it is operationally approximated by a high-order polynomial to increase computational efficiency.
Here, we demonstrate that a carefully trained and robustly tested neural network model calculates UTCI with significantly greater accuracy compared to the polynomial approximation used in the literature. The neural network model substantially outperforms the polynomial model with a similar computational cost, reducing the approximation error by 86%— from 2.78°C to 0.38°C— and thermal stress misclassification by 76%. We eliminate the need to exclude wind speeds above 17m/s from UTCI calculation, which currently limits the global application of the polynomial approximation. When applied to ERA5 reanalysis data, our model reveals a 25% operational difference in daily heat stress categorization between the two methods in Rome, Italy during the 2003 European heatwave. We provide our UTCI model as openly accessible software, as a more accurate way to calculate UTCI in operational procedures. The neural network UTCI model has the potential to enhance climate-health risk research and improve the accuracy of public weather warning systems.
How to cite: Pastine, B., Klöwer, M., Tang, T., Wilson Kemsley, S., and Slater, L.: An upgraded neural network-based operational procedure for the Universal Thermal Climate Index (UTCI) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5838, https://doi.org/10.5194/egusphere-egu26-5838, 2026.
The impacts of weather, weather changes, climate and climate change do not only affect the physical but also the mental health of humans. It ranges from (post traumatic) stress disorders, depression and anxiety to cognitive and behavioural maladaptation and disorders. Form and characteristics of the impact depend on personal and social factors. Personal predispositions like psychological disorders, gender, age and genetics can influence psychical resilience against environmental impacts.
In PsyCourse x Weather we conduct a cross-sectional study. We compare the impact of weather and weather changes on the quality of life (QOL) of people with affective and psychotic disorders, like schizophrenia and bipolar disorder, with the QOL of a healthy control group. The objective is to find out whether there are differences in the impact of weather and climate on the QOL between patients and a control group and if gender and genetic factors influence the impacts. Health data was gathered from the 17 locations in Germany and Austria of the PsyCourse study (PsyCourse 2015), like the WHOQOL, age, gender and the polygenic risk score. As predictors we use meteorological and air hygienic reanalysis data from ERA5 and CAMS. We include parameters like precipitation, air pressure, ozone, particulate matter, wet bulb globe temperature (WBGT), heat wave and cold stress wave indices, summarised to periods of 14 days and 28 days to reflect the time span of the WHO quality of life questionnaire (WHOQOL) and to include longer-term weather conditions. As a result of regression analysis using generalized additive models, we find that meteorological and air hygienic variables have a rather marginal impact. The fact of having a psychiatric disease has in general a strong influence on QOL compared to weather. The mean QOL (scale ranging from 4 to 20, the higher the number the higher the QOL) of the groups is 17 for the control group and 13.1 for the patients. Nevertheless, we find connections between atmospheric changes and the QOL. For our control group we identify heatwave and WBGT as relevant parameters. While for the patients we find ozone, precipitation and particulate matter as influencing factors. During the 14-day periods there are two significant parameters for the control group with reduced influence in the 28-day periods. In contrast, patients are impacted by more parameters with increasing impacts from the 14-day to the 28-day periods. We also identify differences between male and female. In the control group, heatwaves have negative impacts on the group, while males are more affected compared to females. Males in the patient group are also negatively impacted by heatwaves, yet not significantly, females however have increased QOL during heatwaves.
PsyCourse (2015): Home. Available online at http://www.psycourse.de/, updated on 1/13/2015, checked on 8/25/2025.
How to cite: Rückle, K., Greiner, S.-K., Senner, F., and Hertig, E.: PsyCourse x Weather: the impact of weather changes on mental health, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7274, https://doi.org/10.5194/egusphere-egu26-7274, 2026.
Climate change is intensifying summertime heat exposure across Europe, with growing implications not only for physical human health but also for population mental well-being. However, heat-health research has focused mainly on the physical outcomes of heat exposure; mental health impacts remain underexplored. In particular, spatially and temporally explicit analyses that capture variation in heat exposure across diverse regions are scarce, limiting the systematic identification and monitoring of vulnerable populations. Switzerland serves as a suitable case study for addressing this gap, given its pronounced warming trends and environmental heterogeneity, while the underlying analytical approach is transferable to other countries and regions.
This study investigates the relationship between heat and mental health outcomes in Switzerland by integrating population health survey data with satellite data-based heat metrics in a spatially and temporally explicit framework. The study is grounded in a heat-mental health risk framework linking thermal hazard, spatiotemporal exposure, and demographic vulnerability. Individual-level mental health data from the Swiss Health Survey (a comprehensive national health survey conducted in 2007, 2012, 2017, and 2022) are combined with high-resolution land surface temperature (LST) derived from MODIS Aqua as the primary heat exposure indicator, alongside gridded near-surface air temperature for comparison and benchmarking. The temperature metrics are designed to represent environmental heat load rather than single-day extremes. Mental health is expressed through multiple standardised indices capturing psychological burden, vitality, depressive symptoms, and anxiety. To account for spatiotemporal dependencies, we apply hierarchical Bayesian ordinal regression models that also serve as predictive models for scenario-analysis.
Results indicate that higher LST is generally associated with poorer mental health outcomes across Switzerland, with the strongest and most credible associations observed during the exceptionally hot summer of 2022. We also found that LST-based models outperform air-temperature-based models, which indicates the added value of thermal remote sensing in heat-health studies across spatially heterogenous areas. Spatial analyses reveal pronounced regional and urban-rural gradients in both heat exposure and baseline mental health, while demographic factors such as age and biological sex exhibit substantial variation in mental health vulnerability but do not significantly modify the heat-mental health relationship itself.
By integrating remote sensing, climatological data, and population health records, this study demonstrates a scalable interdisciplinary approach for assessing climate-sensitive mental health risks across space and time. It provides a foundation for integrating mental health into climate adaption, heat warning systems, and spatially targeted public health planning.
How to cite: Schubiger, E., Adams, J. S., Santos, M. J., Fischer, S., and Naegeli, K.: Remote Sensing of Mental Health: The Burden of Heat Exposure in Switzerland. An Interdisciplinary Study Combining Earth Observation and Epidemiology., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11659, https://doi.org/10.5194/egusphere-egu26-11659, 2026.
Heat conditions pose a substantial threat to population health, with certain groups being particularly vulnerable. People with mental health disorders may be especially at risk due to structural and social stressors (e.g., living environment, limited access to cooling), physiological factors (e.g., medication effects on thermoregulation), and psychological factors (e.g., reduced self-care). This study is part of the Austrian climate–health project Parahsohl, in which we develop a data-driven workflow to assess health-relevant heat indicators, focusing on individuals with mental health disorders in the federal state of Tyrol. The objective is to develop regression models linking hospitalizations to weather conditions while accounting for relevant confounders, enabling interpretation in an impact-based weather-warning context and providing a basis for subsequent climate risk assessments.
The analytical workflow comprises three stages: (i) data preparation, (ii) model development, and (iii) model evaluation. Daily hospital admissions (n = 83,673) between May and September from 2007–2023 were used as the response variable for the nine administrative districts of Tyrol, focusing on mental and behavioural disorders (ICD-10 diagnosis codes: F00–F99). Weather predictors were derived from high-resolution (1×1 km) gridded observation data (SPARTACUS) for the same time period and aggregated using a population-weighted approach. Thus, we could account for differences in exposure between densely and sparsely populated areas. Lag variables over multiple temporal windows were generated for key meteorological metrics to capture delayed health effects. Hospitalization counts were modelled using generalized additive mixed models (GAMMs) with a negative binomial distribution. Weather variables were included as fixed effects, while day-of-week, year, and district were treated as random effects. Population offsets allowed incidence-based interpretation. Model performance was evaluated using standard statistical criteria for fit and predictive accuracy, and predictive skill was further assessed through temporal cross-validation across years and months. Initial results indicate that higher daily mean temperatures are significantly associated with increased hospitalization counts, with lagged temperature effects further enhancing model performance. Partial-effect plots and relative risk estimates provide interpretable quantitative measures of heat-related impacts on mental health outcomes. For instance, the hottest day in the study period was associated with an estimated increase in hospitalization risk exceeding 10% compared with average summer conditions.
As a next step, the analysis will be extended to a more detailed examination of diagnostic subgroups to better identify particularly vulnerable populations. These results will be presented at EGU2026. This study provides a quantitative assessment of heat-related impacts on mental health hospitalizations and contributes to the development of evidence-based indicators applicable for short-term applications (e.g., user specific impact-based weather warnings) as well as long-term climate risk assessments.
How to cite: Steger, S., Wittholm, J., Baier, K., Schneider, M., Bügelmayer-Blaschek, M., Hofer, L., Kienberger, S., and Brugger, K.: Linking Heat Conditions to Mental Health Hospitalizations: A Data-Driven Analysis for Tyrol, Austria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5303, https://doi.org/10.5194/egusphere-egu26-5303, 2026.
Environmental variables such as air pollution, noise, floods, green space and conflict , shape human health and disease. An important concept is the human exposome, the totality of an individual’s exposure to the environment over their lifetime. The exposome can explain a large proportion of our health, yet quantification across the world population remains surprisingly limited. As we are facing global climate and population change, it becomes increasingly important to understand and quantify the exposome. However, existing studies are often small-scale, do not integrate human mobility affecting personal exposure or focus on narrow sets of variables, thereby failing to capture the full range of socioeconomic and physical variables and values. Harmonized global scale quantitative assessments of the entire exposomes of the world population remain limited. We address this by collecting data sets on environmental variables for a wide range of geo-domains at high resolution (<= 1-10 km2) with global coverage. Seven geo-domains characterizing the external exposome were defined: meteorological and hydrological, biological, geological, air, soil, technological (built environment), and societal. Human mobility in tandem with differing exposure pathways (e.g. passive, through inhalation vs. active, by selecting food stores) are represented globally by aggregating exposures within the spatial context of individuals. The relevant spatial context is an area surrounding the residential locations. Exposure values are first calculated in distance rings centred at the residential location and then aggregated using distance dependent weights. The function determining the weight and maximum distance depends on the exposure pathway and, following this, the relevant human mobility characterization for exposure. To this harmonized dataset, population density is attributed, producing a characterization of the global exposome that will be catalogued in the Green Deal Data Space (GDDS). Initial harmonized processing is executed for global datasets on tropical cyclones, earthquakes, and riverine and coastal flooding, showing hazard intensity and human exposure have different spatial patterns. For example, populated coastal and riverine regions are substantially exposed to flooding relative to their physical hazard extent, whereas other hazards leave populations unaffected. These emerging contrasts illustrate the importance of harmonized global exposome characterization. The assessment framework lays a foundation for analyses on co-exposures, spatial patterns and equitable public health strategies.
How to cite: Kuipers, E., Schmitz, O., Griffioen, R., Bood, R. J., Oonk, R., Loffredo, L., and Karssenberg, D.: Characterization of the world population’s exposomes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9715, https://doi.org/10.5194/egusphere-egu26-9715, 2026.
Indoor air quality (IAQ) is a critical determinant of human health, particularly in environments where occupants spend prolonged periods of time, such as higher education classrooms. These spaces are characterised by high occupancy densities, frequent indoor activities, and continuous exchange of air with the outdoor environment through ventilation systems, window opening, and building leakage. As a result, indoor air quality in university buildings reflects a complex interaction between indoor emission sources, outdoor air pollution, occupant behaviour, and indoor–outdoor transport processes.
Classroom environments are influenced by multiple indoor sources, including occupant-related emissions, resuspension of particulate matter due to movement, cleaning activities, and emissions from building materials and furnishings. Also, outdoor-origin pollutants such as fine particulate matter and gaseous contaminants infiltrate indoor spaces, with their impact depending on ventilation strategies, building envelope characteristics, and user behaviour. Once indoors, pollutants may undergo physical and chemical transformations, further modifying exposure profiles and contributing to cumulative health burdens.
This study investigates indoor air quality in higher education classrooms using an integrated approach that combines field measurements with occupant perception and health-related information. Environmental monitoring focuses on key parameters relevant to indoor–outdoor pollutant exchange, including carbon dioxide as a proxy for ventilation adequacy, particulate matter concentrations, air temperature, and relative humidity. Objective measurements are complemented by surveys assessing perceived air quality, comfort, ventilation, and the presence or aggravation of existing health conditions. This combined methodology enables the evaluation of both exposure conditions and the human factors that influence pollutant dynamics.
Results indicate that elevated indoor pollutant levels often arise from the combined influence of indoor emissions and outdoor infiltration, particularly in naturally ventilated classrooms located in urban environments. Occupant behaviour, such as window opening practices and classroom occupancy patterns, plays a decisive role in shaping indoor pollutant concentrations and perceived air quality. Perceptions of stale or polluted air frequently coincide with conditions of inadequate ventilation or increased outdoor pollution ingress, underscoring the importance of behavioural and building-related factors in exposure assessment.
The findings highlight that perceptions of indoor air quality act as valuable indicators of the cumulative effects of multiple environmental stressors and can provide signals of exposure-related health risks, especially for individuals with pre-existing respiratory conditions. The interconnected nature of indoor and outdoor air quality, where interventions targeting ventilation, building operation, or user behaviour may simultaneously influence indoor exposures and outdoor emissions.
Understanding indoor air quality in higher education institutions requires a holistic perspective that integrates indoor emission sources, indoor–outdoor transport processes, occupant behaviour, and health outcomes. This approach contributes to advancing knowledge of the indoor–outdoor air pollution interface and supports the development of effective interventions and evidence-based policies aimed at improving air quality and protecting human health.
Keywords: Indoor Air Quality; Higher Education; Environmental Perception; Health and Well-being; Classroom environments
Acknowledgements: This work is supported by National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).
This research was supported by the European Union under the Breath IN Erasmus+ project 2023-1-PT01-KA220_HED-00153118.
How to cite: Carvalho, F. and Andrade, C.: Indoor–Outdoor Air Quality Interactions in University Classrooms: Exposure, Perception, and Health Implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22002, https://doi.org/10.5194/egusphere-egu26-22002, 2026.
Rapid urbanization has led to increased population density and more impermeable paving and buildings, causing heat to accumulate on the ground and building shells, resulting in a continuous rise in urban temperatures. Studies have shown that environmental meteorological parameters and air pollutants interact, and air pollution concentrations also have a cumulative effect with environmental factors such as temperature, wind field, and rainfall, impacting human health.
This study aimed to investigate the immediate effects of wet-bulb black bulb concentration (WBGT) and temperature on heart rate variability (HRV) and heart rate in participants. A simple, portable device was used to monitor PM2.5 and other environmental factors. The correlation between temperature and residents' health status was analyzed, with separate analyses conducted for summer and winter-spring seasons to examine the seasonal health impacts.
How to cite: Yu, S.-Y. and Lung, S.-C. C.: Assessment of Immediate health impacts of temperature and PM2.5 in urban residential areas of southern Taiwan., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17098, https://doi.org/10.5194/egusphere-egu26-17098, 2026.
Assessing the efficacy of malaria interventions is increasingly complicated by a changing climate, which can mask or mimic the impacts of public health policies. To robustly attribute changes in disease burden, it is essential to isolate the non-linear impacts of climate trends and variability from intervention effects.
This study introduces the scientific framework of the ACCLIMATISE project (funded by the Wellcome Trust in the ATTRIVERSE program) utilizing the VECTRI dynamical malaria model to simulate transmission under a range of climate counterfactuals. Using latrge ensembles, Our approach filters driving temperature and precipitation data to selectively remove specific modes of variability—ranging from climate change through decadal and multi-year cycles to interannual variability. This experimental setup allows us to disentangle the distinct roles of warming and hydrological variability in driving transmission dynamics across Africa.
We present preliminary results demonstrating how these filtered climate drivers alter simulated malaria baselines, highlighting the sensitivity of the model to specific timescales of climate forcing through temperature and rainfall separately as well as their nonlinear interaction. These simulations establish a "climate-only" reference frame. The ACCLIMATISE project will confront these counterfactual baselines with health observations to attribute the role of climate in this health outcome and separate the signal of malaria interventions from the influence of climate variability and change.
How to cite: Tompkins, A., DiSera, L., Zornoza, M., Caminade, C., Thiam, M., and Munoz, A.: Multiple timescale climate drivers of malaria: Counterfactual ensembles for climate attribution in health from the ACCLIMATISE Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15078, https://doi.org/10.5194/egusphere-egu26-15078, 2026.
Previous modelling studies of mosquitoes in Europe have primarily focused on local and regional climate drivers, while the influence of large-scale atmospheric teleconnection patterns on mosquito populations remains poorly understood. This study examines how major European–North Atlantic (EUNA) teleconnection patterns—including the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), East Atlantic (EA), East Atlantic/Western Russia (EAWR), Scandinavian (SCAND), and Summer East Atlantic (SEA) patterns—influence mosquito abundance across Germany. Using nationwide mosquito surveillance data (2016–2024), we combined rotated temporal-mode principal component analysis (T-mode PCA) of mean sea level pressure fields with spatiotemporal generalized linear mixed models (GLMMs) to quantify regional- and seasonal-specific relationships among circulation modes, local weather anomalies, and mosquito abundance. Results reveal pronounced regional and seasonal variability in the climate-mediated associations between circulation patterns and mosquito abundance. Effects were strongest and predominantly positive in the Continental Dry, Northwest Cool, Warmest, and Coastal regions, particularly from summer to early autumn, whereas responses in Alpine and other mountainous regions were weaker or negative due to cooler, wetter and windier conditions that constrain mosquito activity. Local temperature and humidity anomalies associated with EUNA circulation patterns were consistently linked to increases in mosquito abundance while precipitation and windspeed anomalies showed negative effects. Positive temperature and negative humidity anomalies during EA⁺, EAWR⁺, SCAND⁻, and SEA⁺ phases exhibited the most consistent positive relationships with mosquito abundance. These findings demonstrate that large-scale climate variability plays a significant role in shaping mosquito population dynamics in central Europe and highlight the value of incorporating teleconnection indices into early-warning and forecasting systems of mosquito-borne diseases.
How to cite: Adeleke, E., Merkenschlager, C., Schäfer, M., Lühken, R., Gutjahr, P., Voll, C., and Hertig, E.: Regional and Seasonal Variability in the Impacts of the North Atlantic Oscillation and Other European North Atlantic Teleconnections on Mosquito Populations in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15200, https://doi.org/10.5194/egusphere-egu26-15200, 2026.
How to cite: Raffetti, E., Martellini O Nocentini, M., and Wybrant, M.: Hydroclimatic Extremes and Climate Variability as Drivers of Malaria Risk in Sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1635, https://doi.org/10.5194/egusphere-egu26-1635, 2026.
Research at the intersection of climate, weather, and health is rapidly expanding and inherently interdisciplinary, requiring integration of information across multiple disciplines. This includes comprehensive, accessible, reliable, and harmonized datasets that combine high-quality observational data with bias-corrected and downscaled climate projections from Global Climate Models (GCMs), such as from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). However, despite the availability of numerous gridded observational datasets and pre-processed projections, individual products vary in strengths, limitations, and representations of fine-scale spatiotemporal patterns, which can substantially affect downstream modelling and projection of current and future health outcomes. Moreover, the operational scale of epidemiological analysis is typically defined by administrative units, rather than by regular grids, and therefore often relies on the inclusion of area-level estimates that are additionally weighted by indicators such as human population. Hence, spatially resolved weather and climate data, typically provided in specialized formats (e.g., NetCDF), generally require substantial preprocessing before they can be used for respective analysis.
To address these challenges, we developed a tailored, quasi-global weather and climate dataset designed to support high-resolution infectious disease transmission modelling in tropical settings. Our dataset comprises (1) high-resolution (0.1°) daily climate projections between 60°N and 60°S, and (2) corresponding spatially averaged (population-weighted) area-level estimates at administrative unit levels 0-2 for over 100 countries. We therefore selected and evaluated multiple global observational datasets, including model- and satellite-based products such as ERA5 and CHIRPS, across heterogeneous, disease-relevant tropical study domains. The observational datasets showing the highest performance in our comparative analysis served as reference climatologies for generating high-resolution, bias-corrected climate projections downscaled from six CMIP6 GCMs, focusing on two scenarios from the Shared Socioeconomic Pathways-Representative Concentration Pathway (SSP-RCP) framework: SSP2-4.5 and SSP5-8.5.
For the first time, we hence provide a robust, open-access resource that combines observational datasets and bias-corrected, downscaled climate projections in a coherent manner and translates them into harmonized, spatially aggregated variables suitable for easy use by non-specialists from various disciplines. As an example application, we present the impact of climate change and the sensitivity of administrative-level vector-borne disease transmission risk in South America to the choice of global climate model and emissions scenario. We focus on yellow fever, a vaccine-preventable zoonotic arbovirus endemic to tropical regions of South America and Africa. We anticipate that our unified weather and climate database will be particularly valuable to infectious disease modelers, epidemiologists, and practitioners conducting climate-sensitive health impact assessments.
How to cite: Jahn, S., Fraser, K., Gaythorpe, K. A. M., Dorigatti, I., Winskill, P., Hinsley, W., Wainwright, C. M., Toumi, R., and Ferguson, N. M.: Developing and Applying a Unified Weather and Climate Database to Assess Climate Change Impacts on Tropical Infectious Disease Transmission and Burden, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17736, https://doi.org/10.5194/egusphere-egu26-17736, 2026.
Mosquito-transmitted diseases, particularly dengue, chikungunya, and Zika pose an increasing public health challenge in Southeast Asia where climate change and rapid land-use change are altering transmission dynamics and associated risks. As primary vectors of these diseases, Aedes aegypti predominates in densely urbanized and peri‑urban environments, exploiting artificial containers for oviposition, while Ae. albopictus, historically found in rural and suburban areas, tends to expand its ecological range worldwide and occupies a broader range of landscapes, including forested and peri‑urban areas. Temperature, rainfall, and humidity influence their survival and reproduction, shaping where each species can thrive under different climatic conditions. These contrasting preferences reflect specific climatic tolerances and landscape associations observed along gradients throughout the region.
Climatic factors such as temperature, precipitation, and relative humidity are identified as determinants for distribution and abundance of Ae. aegypti and Ae. albopictus, although their effects vary seasonally and geographically. Remote sensing and GIS-based studies have further highlighted the role of vegetation indices and urban land cover in shaping vector suitability. Geographic gaps exist in Southeast Asia, where most species distribution modeling studies are limited to local or national scales. This is largely due to the lack of standardized and comprehensive mosquito occurrence data, the concentration of studies in more easily accessible areas, and the challenges of harmonizing data across countries. As a result, regional models remain limited, impeding comprehensive assessment of the environmental drivers of Aedes at broader scales. Many existing models lack justification for variable selection and rarely address multicollinearity among predictors, limiting interpretability and robustness. To fill these gaps, standardized methodologies must be put in place that rigorously test correlations between environmental determinants in order to improve the predictive capacity of distribution models relevant to public health planning and vector control.
Here, we develop a species distribution modeling (SDM) framework that combines statistical and machine learning approaches to quantify the environmental drivers of Ae. aegypti and Ae. albopictus across mainland Southeast Asia. By combining entomological data from Global Biodiversity Information Facility (GBIF) and local datasets provided by project partners, and integrating satellite-derived land-cover classifications, landscape metrics, and high-resolution bioclimatic variables, we evaluate the importance of climatic and landscape predictors while considering collinearity and scale effects. Model performance is evaluated using spatial cross-validation to ensure transferability across countries. Our results provide spatially explicit maps of Aedes mosquitoes habitat suitability and identify key environmental determinants driving current distributions across Southeast Asian countries. We discuss how these determinants may evolve under ongoing and future climate change and on the potential consequences for Aedes suitability patterns and implication for climate-sensitive disease risk. This perspective highlights the relevance of our findings for surveillance prioritization, targeted vector control strategies, and the development of data-driven early warning systems supporting climate-resilient public health planning in Southeast Asia.
How to cite: Teillet, C., Hoeun, S., Huynh, T. T. T., Boyer, S., and Herbreteau, V.: Climatic and landscape drivers of Aedes aegypti and Aedes albopictus mosquito distributions in mainland Southeast Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4826, https://doi.org/10.5194/egusphere-egu26-4826, 2026.
Dengue continues to expand across Brazil under increasingly variable climatic conditions, and anticipating where infections may spread is essential for effective public health preparedness. However, most existing early warning systems focus on local case trajectories alone and overlook the spatial redistribution of infection risk driven by human mobility. This gap leaves planners without the ability to foresee where cases are likely to be imported before local transmission accelerates.
In this study, we develop a generalizable forecasting framework that couples climate-informed dengue incidence predictions with a multimodal mobility network covering all 5,570 Brazilian municipalities. Weekly dengue cases are forecasted using a long short-term memory (LSTM) model that incorporates temperature and humidity dynamics. These forecasts are combined with a composite mobility matrix spanning road, river, and air flows, allowing us to estimate the expected volume of imported infections between cities for every epidemiological week of 2024.
The resulting importation-risk surfaces reveal well-defined corridors of movement-mediated dengue spread, including strong directional asymmetries between major source regions (e.g., large urban hubs with intense outbound flows) and peripheral sink municipalities that depend heavily on external seeding. We find that high importation risk often precedes subsequent local increases in incidence, highlighting the added value of capturing human mobility in early warning systems.
This framework advances dengue surveillance by integrating climate variability, human mobility, and short-term predictive modeling into a unified pipeline. Beyond dengue and Brazil, the approach is modular and transferable to other climate-sensitive infectious diseases and mobility-rich settings. By quantifying how infections may spread through movement pathways before they emerge locally, this work provides a scalable tool for proactive, spatially targeted public health response in an era of intensifying climate-health risks.
How to cite: Chen, X. and Moraga, P.: Forecasting Dengue Importation Risk in Brazil Using Deep Learning and Multimodal Mobility Networks , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-843, https://doi.org/10.5194/egusphere-egu26-843, 2026.
Climate change is a key determinant of public health, influencing disease patterns and human and environmental well-being. Mosquito-borne diseases such as dengue and West-Nile virus continue to pose significant public health challenges worldwide, particularly in regions where environmental conditions favour mosquito production and spread. In recent years, there has been a resurgence of several vector-borne diseases in Europe, driven by climate change, altered water management, and the expanding distribution of invasive mosquito species. Spain has been increasingly affected by this trend, with repeated outbreaks of West-Nile virus—especially in southern regions—and sporadic locally acquired dengue cases reported since 2018.
Mosquito population dynamics are largely determined by climatic factors, including temperature and water availability. Therefore, understanding the linkage between climate, local water resources, and mosquito dynamics is crucial for better predicting current and future health risks and informing effective disease control and health management. We investigated how the temporal and spatial distribution of water availability and climatic conditions influenced mosquito populations in the Aiguamolls de l’Empordà, a natural wetland area connected to La Muga and El-Fluvia river basins (Catalonia, Northeast Spain), under current and projected climatic scenarios. To do so, we developed a machine learning based Random Forest (RF) model fed withCulex mosquito abundance data (weekly data from 12 traps), climate (rainfall and temperature), and hydrological simulated data (discharge, actual and potential evapotranspiration, and aridity) from 2001 to 2021. We use projected daily climate from ensemble projections of climate scenarios of the REMO2015 regional climate model under the RCP2.6 and RCP8.5 scenarios (2031-2060) to project the future abundance of mosquito populations in the study area. Our model comprised 48 environmental predictors and the Culex population as the predictand.
The Culex mosquito population showed a strong positive correlation with temperature-related variables and a negative relationship with discharge and aridity. The RF model showed reasonably good performance in training (R2 = 0.90) and testing (R2 = 0.61), showing a well-matched temporal pattern of average condition per trap with observed data. Based on Mean Decrease in Impurity analysis, potential evaporation and temperature were found to be highly important predictors. According to the climate projection under RCP 8.5, in general, mean annual rainfall over the study area will decrease, while minimum and maximum temperatures will increase in the future (2031-2060) compared to the baseline (1981-2010). Thus, these changes could create more favourable conditions for mosquitoes, resulting in substantial additional risk to public health. These results underscore the mounting risk of mosquito-borne diseases in Europe and the necessity for enhanced surveillance and preventive management. Our results contribute to the project “Infectious Disease Decision-support Tools and Alert systems to build climate Resilience to emerging health Threats (IDAlert)” funded by the European Union.
Keywords: Wetlands, Machine Learning, Health Risk, Climate change, Mosquito-borne diseases
How to cite: Sirisena, J., Rodriguez, J., Bernal, S., Bartumeus, F., Costa, M. M., and Bouwer, L. M.: Simulating Mosquito Populations through the Integration of Climate and Water Resource Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13289, https://doi.org/10.5194/egusphere-egu26-13289, 2026.
Introduction
Climate change is increasingly associated with extreme weather events that disrupt healthcare delivery, yet the system-wide health consequences of these disruptions remain poorly quantified. While damage to health facilities following extreme events is well documented, far less is known about how climate-related disruptions to service accessibility propagate through health systems and affect population health. In Malawi, for example, Cyclone Freddy in 2023 led to the closure of at least 79 healthcare facilities, in some cases for several months, substantially reducing access to care in already resource-constrained settings.
Methods
We use Malawi, one of the world’s most climate-vulnerable countries, as a case study to investigate the interactions between extreme precipitation, health system functioning, and population health. We integrate empirically-estimated damage functions for the impact of precipitation on healthcare service delivery into Thanzi La Onse, an all-disease, health-system model calibrated to Malawi. Using two climate and socioeconomic futures (SSP2-4.5 and SSP5-8.5), we project the impacts of climate-related disruptions on healthcare access between 2025 and 2040, accounting for heterogeneous healthcare-seeking behaviour and changes in service accessibility.
Results and Discussion
We estimated, for the first time, the population health impact of precipitation-mediated disruptions to healthcare services. We estimate that up to 4% of healthcare appointments may be disrupted by precipitation-related events over the study period. Disruptions disproportionately affect conditions requiring continuous or long-term engagement with care, such as chronic pain, mental health conditions, and contraceptive services (Figure), where interruptions increase the likelihood of individuals falling out of care entirely. Additionally, acute care such ante- and postnatal care were disrupted. Despite these effects, we project only modest changes in aggregate DALYs, reflecting both pre-existing barriers to healthcare access and conservative assumptions regarding the scope of service disruption. Notably, our analysis does not yet capture complete precipitation-driven changes in disease prevalence, suggesting that our estimates likely represent a lower bound of true impacts. Nonetheless, the projected scale of disruption highlights a substantial and growing strain on healthcare systems under climate change, particularly in rural and infrastructure-poor areas. Future work will extend this framework to explicitly model facility closures, transport disruptions, and climate-sensitive diseases, providing a more comprehensive assessment of health system vulnerability and resilience.
Figure: Disruption to appointments due to precipitation-mediated disruptions under the SSP2.45 scenario (compared with the "no dispruption" Baseline) between 2025 and 2040. Those services that either required a long-term engagement with care, or were acute, were most affetced.
How to cite: Murray-Watson, R. and the TLO Modelling Team: Healthcare Disruptions and Health System Resilience under Climate Change in Malawi, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6736, https://doi.org/10.5194/egusphere-egu26-6736, 2026.
Posters on site: Tue, 5 May, 08:30–10:15 | Hall X5
Healthcare systems across Europe face growing challenges from climate-related hazards such as heatwaves, extreme precipitation, poor air quality, allergen exposure, and vector-borne diseases. To support outpatient medical practices in adapting to these risks, the AdaptNet project developed the AdaptNet Climate-Health Toolbox, a comprehensive, practice-oriented suite of tools designed to build climate resilience within primary and specialist care. Developed jointly with ambulatory physicians, the toolbox integrates scientific evidence with pragmatic operational guidance and is freely accessible online (https://www.gesundheitsnetznuernberg.de/adaptnet-klima-toolbox/).
The toolbox consists of several complementary modules. An interactive nationwide risk map enables users to assess present and future climate-related health risks for any German region, covering hazards such as heat, floods, air pollution, allergens, wildfires, and vectors. Downloadable checklists provide actionable recommendations for extreme weather events, power outages, and heat preparedness, supporting structured team-based adaptation planning. A basic online training introduces essential climate-health knowledge, while advanced training modules deepen practical implementation through case-based learning and support for quality circles and workshops.
To enhance clinical management, the toolbox includes a heat-focused medication review tool, helping practitioners identify and adjust risk-relevant drugs during heat periods. For patient communication, customizable “info-prescriptions” on heat and pollen, posters, flyers, and waiting room videos convey clear behavioural guidance and increase awareness during high-risk periods. All components are designed for simple integration into routine workflows and can be adapted to local needs. Collectively, the toolbox provides a structured pathway for practices, from risk assessment to team coordination, patient counselling, and medical decision support, to strengthen resilience to climate change impacts.
AdaptNet is funded by the G-BA Innovation Fund (01VSF22044).
How to cite: Kaspar-Ott, I., Álvarez, F., Köhn, P., Gäbel, P., Herrmann, A., Hueber, S., Klanke, M., Lindenthal, J., Nieder, J., Shimada, D., Stark, S., Quitmann, C., Wambach, V., and Hertig, E.: The AdaptNet Climate-Health Toolbox: A Comprehensive Multi-Component Framework to Strengthen Climate Resilience in Outpatient Healthcare in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1560, https://doi.org/10.5194/egusphere-egu26-1560, 2026.
Air pollution is an increasing public health concern in Sri Lanka, driven by rapid urbanization, regional pollutant transport, and continued reliance on solid fuels in rural areas. Exposure to fine particulate matter (PM2.5) is associated with elevated risks of cardiovascular and respiratory diseases, stroke, and premature mortality, contributing to over 20% of total disability-adjusted life years (DALYs) and deaths nationally, according to the most recent iteration of the Global Burden of Diseases Study. However, high-resolution exposure data and short-term health impact assessments remain limited.
In this study, we develop the first high-resolution (1 × 1 km, daily) PM2.5 dataset for Sri Lanka by combining in-situ measurements, satellite retrievals, and reanalysis products using a hybrid modeling framework. We then quantify the acute effects of PM2.5 exposure on respiratory health using daily hospital admission data (eIMMR) for 2020–2023, focusing on acute respiratory infections (ICD-10 codes J00–J06, J09–J18, J20–J22). We apply a Distributed Lag Non-Linear Model (DLNM) to capture non-linear exposure–response relationships and delayed effects, considering lags up to 50 days. Models control relative humidity, temperature, precipitation, carbon monoxide, day of week, and month. Confounding was assessed using a leave-one-out approach, while effect modification was examined through tertile-based stratification and pairwise statistical tests.
Population-weighted PM2.5 concentrations show a rapidly increasing trend, particularly in and around the national capital. We find that PM2.5 effects are strongest on the day of exposure (lag 0) and decrease with increasing lag. A 10-µg m-3 increase in PM2.5 is associated with a 16.6% (10–22%) increase in hospitalizations for acute respiratory diseases. Relative humidity emerges as a key confounder, while precipitation significantly modifies the PM2.5–hospital admission relationship, with substantially stronger effects on low-precipitation days (RR ≈ 1.40). Children under 15 years’ experience higher risks compared to adults and the elderly.
These findings highlight the growing respiratory health burden of air pollution in Sri Lanka and underscore the need for integrated air quality management and health-informed policy. Future work will incorporate additional pollutants (NO2, O3), socioeconomic factors, and extend analyses to cardiovascular outcomes and joint PM2.5–temperature effects.
How to cite: Aunan, K., Amarakoon, P. M., Jayawardena, R., Diunugala, A., Sandvik, B., Ferkingstad Sandve, G. K., Fleck Fossen, E. I., and Chowdhury, S.: Assessing the health risk of air pollution exposure in Sri Lanka, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9786, https://doi.org/10.5194/egusphere-egu26-9786, 2026.
Previous studies have shown associations between extreme temperatures and the risk of urgent hospital admissions. However, less is known about the role that air pollutants play in this association in Central and Eastern Europe. This study aimed to distinguish the independent effects of temperature and air pollutants on urgent hospital admissions in the general population of the Czech Republic.
We use data on daily urgent hospital admissions (all-cause, cardiovascular, and respiratory), mean ambient temperature, and air pollutants (PM10, NO2, SO2, O3) from 1998 to 2018. Using a multi-exposure and two-step approach, we applied distributed lagged non-linear models (DLNM) to understand the non-linear and 21-day lagged effects of temperature, as well as the linear effects of air pollutants, on hospitalizations in the 14 Czech regions. Later, we estimated the pooled effects using meta-regression techniques. Additionally, we did a separate analysis by age and sex categories.
Meta-regression pooled estimates showed that for the Czech Republic, the 1st percentile of temperature was associated with increased risk ratio (RR) of respiratory admissions (RR=1.20, CI:1.16-1.25). In contrast, the 99th percentile of temperature was associated with increased risk of all-cause admissions (RR=1.05, CI:1.03-1.07). A 10 µg/m3 increase in NO2 was associated with increased risk of all-cause (RR=1.013, CI:1.010-1.015) and cardiovascular (RR=1.015, CI:1.011-1.019) admissions. Associations were stronger for the age group 5 to 19 years (all-cause RR=1.030, CI:1.025-1.034; cardiovascular RR=1.042, CI:1.012-1.074), including respiratory admissions (RR=1.020, CI:1.009-1.032). Other pollutants did not show statistically significant associations.
Extreme temperatures and rising NO2 concentrations are likely to increase the risk of urgent hospital admissions in the Czech Republic. Children and adolescents seem to be the most vulnerable group to these environmental exposures. Therefore, public health measures must address environmental necessities, while pediatric units prepare for the potential increased hospitalization demand. Exposures measured at the individual level are essential to confirm these findings.
How to cite: Urban, A., Quispe-Haro, C., and Mühlhaus, R.: Effects of Temperature and Air Pollution on Urgent Hospital Admissions in the Czech Republic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7085, https://doi.org/10.5194/egusphere-egu26-7085, 2026.
Air pollution has been investigated as a potential risk factor for breast cancer [1]; however, its quantitative impact on malignancy risk stratification remains uncertain, particularly when integrated with radiological features. In this study, we investigate whether long-term exposure to air pollution — a climate-sensitive environmental stressor — derived from Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data provides complementary information for predicting breast lesion malignancy in a screened population.
We analysed mammographic and clinical data from 906 women undergoing breast cancer screening, classified as benign (BI-RADS B2) or malignant (BI-RADS B5). Individual exposure to NO₂, PM₂.₅, PM₁₀ and O₃ was estimated by linking the zip code of residence to CAMS gridded concentrations, computing both annual mean levels and cumulative exposure over the three years preceding diagnosis. Environmental exposure metrics were integrated with radiological descriptors, including lesion morphology, margins and breast density patterns, together with demographic information.
To reduce model complexity and limit overfitting, univariate feature selection was applied using an ANOVA F-test (p < 0.05) prior to training a feed-forward neural network. Model performance was assessed using independent validation data and compared with models excluding environmental exposure variables.
The integrated model achieved a ROC-AUC of 0.78, with balanced accuracy and a weighted F1-score of 0.73. Radiological features such as spiculated margins and irregular lesion shape remained the strongest predictors of malignancy; however, cumulative NO₂ and PM₂.₅ exposure metrics retained independent statistical significance and contributed to model performance. Limiting partially redundant air-quality metrics decreased apparent predictive power but improved model stability and interpretability, highlighting the potential impact of spatial and exposure-related confounding in observational datasets.
These findings suggest that long-term air-pollution exposure, as quantified using Copernicus atmospheric reanalysis products, provides a modest but consistent contribution to breast lesion malignancy risk stratification when combined with mammographic features [2]. This study demonstrates the feasibility of integrating atmospheric reanalysis data with clinical imaging information for exploratory environmental health applications, while underscoring the need for geographically robust validation and cautious interpretation of causality.
[1] White AJ, Bradshaw PT, Hamra GB. Air pollution and Breast Cancer: A Review. Curr Epidemiol Rep. 2018 Jun;5(2):92-100. doi: 10.1007/s40471-018-0143-2. Epub 2018 Mar 27.
[2] Fiore, M.; Palella, M.; Ferroni, E.; Miligi, L.; Portaluri, M.; Marchese, C.A.; Mensi, C.; Civitelli, S.; Tanturri, G.; Mangia, C. Air Pollution and Breast Cancer Risk: An Umbrella Review. Environments 2025, 12, 289. https://doi.org/10.3390/environments12050153
How to cite: Chiacchiaretta, P., Dotta, F., Staropoli, M. C., Aruffo, E., Mascitelli, A., Sallese, I., Delli Pizzi, A., and Di Carlo, P.: Spatiotemporal Machine Learning Integration of Atmospheric Reanalysis and Mammographic Data for Breast Lesion Malignancy Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2149, https://doi.org/10.5194/egusphere-egu26-2149, 2026.
The effect of environmental parameters on glycaemic trends undoubtedly has clinical relevance, which needs to be managed appropriately by understanding the responses of patients treated with different therapeutic approaches. In general, it is possible to assess how glycaemic trends in diabetic patients respond to external temperatures, humidity and Humidex. This study presents the preliminary results obtained as part of the project "Innovation Ecosystem: innovation, digitalisation and sustainability for the widespread economy in central Italy (VITALITY)", funded by NextGenerationEU, on patients with type 2 diabetes and the findings of analyses carried out on children and young adults (type 1 diabetes) followed at the UOSD Regional Paediatric Diabetes Service Hospital, ‘SS. Annunziata’ Hospital. The study was performed to assess the effect of climate change on diabetic patients; to this end, a correlation analysis between atmospheric temperature, humidity and Humidex trends with respect to blood glucose patterns was carried out both on the entire sample of patients followed at the Lanciano-Vasto-Chieti (Abruzzo, Italy) Local Health Authority (approximately 200,000 subjects) over 5 years (2019-2023), and on precision basis, following a subset of approximately 50 patients with type 2 diabetes, intensively for one week during this year (2025). A parallel analysis was conducted over a period of one year (Autumn 2022 - Summer 2023) on 219 patients with type 1 diabetes, evaluating their glycaemic trends in relation to outdoor temperatures [1,2]. Results showed a close correlation between atmospheric conditions and blood glucose levels at every stage of analysis, highlighting the importance of considering environmental parameters, such as outdoor temperatures and humidity in the study of chronic diseases like diabetes.
[1] Mascitelli, Alessandra, Stefano Tumini, Piero Chiacchiaretta, Eleonora Aruffo, Lorenza Sacrini, Maria Alessandra Saltarelli, and Piero Di Carlo. 2025. "Effect of Atmospheric Temperature Variations on Glycemic Patterns of Patients with Type 1 Diabetes: Analysis as a Function of Different Therapeutic Treatments" International Journal of Environmental Research and Public Health 22, no. 12: 1850. https://doi.org/10.3390/ijerph22121850
[2] Chiacchiaretta, Piero, Stefano Tumini, Alessandra Mascitelli, Lorenza Sacrini, Maria Alessandra Saltarelli, Maura Carabotta, Jacopo Osmelli, Piero Di Carlo, and Eleonora Aruffo. 2024. "The Impact of Atmospheric Temperature Variations on Glycaemic Patterns in Children and Young Adults with Type 1 Diabetes" Climate 12, no. 8: 121. https://doi.org/10.3390/cli12080121
How to cite: Mascitelli, A., Chiacchiaretta, P., Staropoli, M. C., Aruffo, E., Tumini, S., Ferretti, A., Franciotti, R., La Fratta, I., Lucarelli, F., and Di Carlo, P.: Climate change and diabetes: preliminary results on patients with type 1 and type 2 diabetes in the Abruzzo Region (Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2147, https://doi.org/10.5194/egusphere-egu26-2147, 2026.
Heatwaves are among the deadliest climate extremes, with children especially vulnerable due to physiological sensitivity and limited adaptive capacity. Yet cumulative lifetime exposure of children remains poorly quantified, particularly in high-latitude countries such as Canada, where warming is occurring at roughly twice the global average. Here, we present the first national-scale assessment of Lifetime Heatwave Exposure (LHE) for Canadian children under multiple global warming pathways.
We integrate high-resolution temperature observations, downscaled CMIP6 climate projections, and demographic data to estimate the number of severe heatwave events children are expected to experience over their lifetime. Heatwaves are defined using locally relevant thresholds based on exceedance of the 98th percentile of daily maximum temperature, ensuring consistency across Canada’s climate zones. Exposure is evaluated across warming levels from 1 °C to >5 °C at sub-provincial population scales.
Our results demonstrate a clear generational shift in heat exposure. Under 3 °C warming, over 80% of Canadian children are projected to experience unprecedented lifetime heatwave exposure exceeding the historical maximum. Analysis of 45 major historical heat events shows that 70% of reported heat-related deaths occurred during LHE-level events, including the 2021 Pacific Northwest heat dome, when 447 excess deaths were recorded in Vancouver alone. Projections indicate a nationwide transition from rare, once-in-a-lifetime heatwaves to recurrent generational hazards, with western provinces reaching full exposure earliest and eastern and northern regions converging rapidly by mid-century.
By shifting focus from short-term extremes to cumulative lifetime exposure, this study introduces a child-centric, policy-relevant metric for climate risk assessment. The findings highlight growing intergenerational inequity and underscore the urgency of global mitigation alongside targeted local adaptation—such as urban greening, cooling infrastructure, and heat-health early-warning systems—to protect current and future generations of Canadian children.
How to cite: Zaerpour, M., Papalexiou, S. M., and Helldén, D.: Cumulative Lifetime Heatwave Exposure for Canadian Children in a Warming Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12862, https://doi.org/10.5194/egusphere-egu26-12862, 2026.
Rapid deterioration of urban air quality poses severe threats to climate, ecosystems, and human health, particularly in megacities such as Delhi, India. This study presents a comprehensive assessment of aerosol dynamics during the post-monsoon season (PMS; October–November) from 2019 to 2025, a period frequently associated with extreme pollution episodes driven by crop residue burning and unfavorable meteorological conditions. We integrated ground-based PM₂.₅ observations, satellite-derived aerosol optical depth at 550 nm (AOD₅₅₀), active fire counts, and key meteorological parameters to examine the drivers of severe air pollution events. The highest mean AOD₅₅₀ (0.79-0.80) and PM₂.₅ concentration (140-150 μg m⁻³) were observed. Across all years, PM₂.₅ levels peaked between mid-October and mid-November, exceeding the WHO 24-hour guideline (15 μg m⁻³) indicating a persistent public health emergency. A moderate to strong correlation was identified between PM₂.₅ and AOD, highlighting the role of columnar aerosol loading in surface pollution. Fire hotspot analysis revealed that 36–58% of total fire events occurred in identified hotspot regions. A statistically significant non-linear negative relationship was observed between wind speed and both AOD and PM₂.₅, underscoring the influence of stagnant meteorological conditions. HYSPLIT back-trajectory and wind rose analyses indicate dominant air mass transport from the north and north-west during PMS. The findings emphasize the urgent need for integrated mitigation strategies, including sustainable residue management, adoption of cleaner agricultural practices in hotspot regions, and stricter emission controls, to reduce pollution exposure and associated health risks.
How to cite: Kumar, R. P.: Extreme Post-Monsoon Air Pollution in Delhi: Aerosol Dynamics, Fire Emissions, and Meteorological Controls, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21766, https://doi.org/10.5194/egusphere-egu26-21766, 2026.
Urban heatwaves have a significant impact on human health and thermal comfort in cities. The Universal Thermal Climate Index (UTCI) is widely used to evaluate outdoor thermal comfort in cities. UTCI is based on meteorological inputs (air temperature, relative humidity, solar radiation…), clothing characteristics and a human physiological model. Accurate estimation of UTCI requires an accurate assessment of radiative heat exchanges between the human body and the surrounding environment. The mean radiant temperature (Tmrt) is the primary input of UTCI. Tmrt represents a simplified parameterization of the combined shortwave and longwave of radiative exchanges between the human body and its environment, expressed as a single equivalent value corresponding to a hypothetical uniform radiative enclosure. Under outdoor conditions, the estimation of radiative heat exchanges, and thus of Tmrt, remains complex due to the spatial non-uniformity of the surrounding environment and the complexity of human body geometry. In this context, the three-direction radiometer method is commonly used to measure incoming shortwaves and longwaves radiation, and based on assumptions regarding human geometry and emissivity, Tmrt can thus be reliably evaluated. However, because radiometer method is expensive, an alternative cost-effective, smaller, along with associated analytical methods have been developed. These approaches are mainly based on black and grey globes of various diameters and materials and are widely used to characterize the effect of strategies to mitigate the impacts of urban heat waves on the microclimate of cities. The accuracy, response time and representativeness of these probes with respect to human body perception of radiative effects are often questioned. This study focuses on the experimental evaluation of the uncertainties associated with the use of these cost-effective devices for estimating Tmrt. A new cylindrical probe has been designed to better represent human body geometry; its accuracy is evaluated and compared with the classical radiometer method and with black and grey globes commonly used. The experimental campaigns include tests conducted in a controlled environment (wind tunnel) as well as outdoor measurements. The influences of surface emissivity, globe diameter, and globe material on Tmrt estimation are investigated. The wind tunnel setup, combined with a xenon lamp to simulate solar radiation, allows precise control over airflow, radiation, and thermal conditions affecting globe temperature measurements. This setup is used to evaluate the sensitivity of the different probes to the controlled variables. Outdoor experiments investigate real thermal radiation conditions and a wider range of meteorological variables, including cloud cover, wind regimes, and solar angles. Using experimental results obtained from the outdoor campaign, Tmrt values derived from globe measurements are compared with reference values.
How to cite: Wehbi, Z., Essaidi, Z., Chanut, C., Garcia-De-Cezar, M., Cheviron, B., Liron, F., Tomas, S., and Aprin, L.: Assessing Uncertainties in Mean Radiant Temperature Measurements in Controlled or Outdoor Conditions., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22398, https://doi.org/10.5194/egusphere-egu26-22398, 2026.
Hydroclimatic records show an increase in both the duration and intensity of droughts in the Amazon River Basin (ARB) with remarkable events occurring in the past 20 years in the region (e.g., in 2005, 2010, 2015-2016, and 2023-2024). Climate projections indicate overall drier conditions for most of the ARB in the next decades, together with a higher frequency of extremes such as droughts and floods. The co-occurrence of extreme droughts with heatwaves and forest fires have been referred to as compound dry hazards. They pose significant health risks to people in the Amazon. Hydrological droughts, for instance, change river flows in the ARB, directly affecting the most important means of transportation for rural riverine communities: river navigation. Riverine communities, an officially recognised traditional people of Brazil (Federal Decree 8750/2016), depend on navigation to access urban centres, health care facilities, schools, and fishing and hunting sites. Food and fuel supply also depend entirely on navigation in many remote parts of the ARB where roads are scarce. Extended and intense dry periods can lead to the total isolation of entire communities for several months, with food and medicine supply shortages, and reduced access to healthcare facilities. When droughts co-occur with forest fires and heatwaves, there is an increase in healthcare demand due to respiratory diseases, waterborne diseases, and other health issues, while access is disrupted by very low water levels in rivers. Compound dry hazards may pose health impacts that can be felt differently according to gender, with increasing evidence suggesting that women suffer more intensively because of social norms regarding gender roles as well as due to physiological factors related to reproductive health. Gender differentiated impacts of climate change may affect several dimensions of well-being and daily activities in the rural context: distribution of labour, mobility and migration, access to means for hygiene and health care, and exposure to climate-sensitive diseases. This presentation examines the pathways through which compound dry hazards disproportionately affect riverine women in the Amazon, compared to men, due to social norms, geographical conditions, and gender-specific physiological needs.
How to cite: Santos de Lima, L. and Nunes Macedo, M.: Health Risks to Riverine Women in the Amazon Under Compound Dry Hazards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21788, https://doi.org/10.5194/egusphere-egu26-21788, 2026.
Climate change is increasing Europe’s vulnerability to vector-borne diseases. One example are the increasing number of outbreaks caused by the West Nile Virus. Whereas in the early 2010s, the virus was only found in south-eastern Europe, local infections are now also detected in more northern and western countries. To inform health care institutions as well as citizens it is necessary to be able to predict where and when the next outbreak will happen. There have been studies that show that land use, climate and weather influence the risk of human West Nile Virus infections, but it is less clear what the relative contributions of land use, climate change and weather are. In particular, it is not determined yet if the northward expansion of WNV can be better explained by the gradual change in climate, or by the occurrence of specific weather conditions that increase the risk of WNV infections. In this study, a random forest model is used to determine what is the best predictor of WNV infections: weather or climate. It shows that on a spatial scale of NUTS3-regions, the climate mean seasonal cycle of the 2m temperature is the best predictor for human WNV outbreaks, and that including the weather in the model does not improve its performance. Moreover, results indicate that WNV risk is higher in areas in which the climate mean seasonal cycle of temperature is in between 20-26 °C for one or more weeks. This can help explain and predict the emergence of human WNV infections in new regions in Europe.
How to cite: Boonekamp, M., De Roode, S., Siebesma, P., Bogaard, T., Van der Schrier, G., Sikkema, R., Schrama, M., Koopmans, M., and Marsboom, C.: Human West Nile Virus infections in Europe: weather or climate?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18547, https://doi.org/10.5194/egusphere-egu26-18547, 2026.
Black Carbon Exposure as a Risk Factor for Child Health in India
Rajesh Bag1,2, Debajit Sarkar2, Ram Pravesh Kumar1, Sagnik Dey2,3
1School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India.
2Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India.
3Adjunct faculty, Korea University, Seoul, South Korea.
Email: rajeshgeovu@gmail.com
Keywords: Black carbon; stunting; wasting; low birth weight.
Introduction
Black Carbon (BC), a short-lived climate pollutant and a major light-absorbing component of fine particulate matter (PM2.5) plays a dual role in driving climate change and adversely impacting human health. In India, persistently high levels of ambient PM2.5 are compounded by household air pollution from biomass combustion, resulting in chronic BC exposure across large sections of the population. Child undernutrition manifested as stunting, wasting, and Low Birth Weight (LBW) continues to be a critical public health challenge in India, contributing to elevated child morbidity, mortality, and long-term developmental deficits. Despite the biological plausibility linking BC exposure to quantifying associated health effects in the Indian context is limited. Addressing this gap, the present study investigates the association between chronic BC exposure and three key indicators of child undernutrition, thereby providing novel insights into the intersection of air pollution and child health.
Methodology
We utilized nationally representative data from the National Family Health Surveys (NFHS-4: 2015-16 and NFHS-5: 2019-21), comprising 437,908 children under five years of age. Among them 10,362 observations had missing mean BC exposure and 35,386 had missing information on fuel type, wealth index, mother Body Mass Index (BMI), mother age, mother education, residence, child sex and mother smoking status. These records were excluded from the analysis. After removing all missing values, the final analytic sample included 402,508 children. Monthly mean BC exposure (2010-2021) at 1 km × 1 km resolution was merged with geocoded DHS cluster coordinates (Dey et al., 2020). For stunting and wasting exposure was averaged from child birth to the month of interview. For in-utero exposure related to LBW, we averaged BC concentrations from 9th months prior to birth through the month of birth. Generalized Linear Model (GLM) and Generalized Linear Mixed Models (GLMM) were used to estimate associations between long-term BC exposure and odds of stunting, wasting, and LBW, adjusting for household fuel type, mother education, mother wealth index, residence, mother age, mother BMI, child gender and mother smoking status. We estimated the exposure response relationship using a Generalized Additive Model (GAM) incorporating a cubic spline for BC. Effect modification by all covariates was evaluated using multiplicative interaction terms. Stratified ORs with 95% uncertainty intervals were reported only for significant interactions. All models were adjusted for the same covariates.
Results & discussions
After adjusting for confounders, the odds of stunting and wasting increased to 1.03 (95% UI 1.026-1.032) and 1.04 (95% UI 1.026-1.032) respectively for each 1 μg/m³ increase in long-term ambient BC exposure . Under the GAM framework the exposure response curves for stunting and wasting showed a monotonic increase with rising BC levels.
How to cite: Bag, R.: Black Carbon Exposure as a Risk Factor for Child Health in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1311, https://doi.org/10.5194/egusphere-egu26-1311, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot A
EGU26-694 | ECS | Posters virtual | VPS31
Adverse Birth Outcomes Attributable to High Heat in NigeriaMon, 04 May, 15:24–15:27 (CEST) vPoster spot A
Exposure to fine particulate matter (PM2.5) has been linked with adverse birth outcomes in Nigeria. Emerging evidence suggests that high temperatures may also be associated with these outcomes. However, this association, as well as whether temperature modifies the effects of PM2.5 on these outcomes, has not been explored in Nigeria.
Using data from the 2018 Nigerian Demographic and Health Survey, we examined the association between maternal exposure to maximum temperature (Tmax) during pregnancy and adverse birth outcomes, including Low Birth Weight (LBW), Preterm Births (PTB), and Stillbirths (SB). A daily maximum near-surface air temperature gridded dataset (2012-2018) at 1-km2 resolution was obtained from Zhang et al. (2022) and linked to birth clusters based on geographic coordinates. Temperature metrics (hot days and heatwave events) were derived from the 90th percentile threshold of the daily Tmax values, based on all pregnancy periods. Logistic regression analysis was used to estimate the association between these metrics and birth outcomes. The intensity, frequency, and duration of these temperature metrics in relation to the birth outcomes were also evaluated. We then estimated the Relative Excess Risk due to Interaction (RERI) using interaction terms for each temperature metric during the corresponding PM2.5 exposure period.
We observed a strong correlation (r=0.93) between the model temperature data and observational data (2012-2018). An increasing positive association was observed between the duration of hot days and PTB, while an increase in heatwave events was positively associated with LBW. Intensity in hot days was positively associated (1.59; 95% CI: 1.28-1.96) with LBW. At the same time, frequency in hot days showed no significant relationship with any of the birth outcomes. Positive additive interaction between high temperature and PM2.5 was observed across exposure categories for LBW and SB. The magnitude of interaction was greater at moderate PM2.5 levels (Q2) for LBW, while the highest levels (Q3) had a greater effect for SB. As global temperatures rise, these findings provide evidence that maximum temperature can intensify the health burden of ambient PM2.5 during pregnancy, underscoring the need for climate-adaptive maternal health interventions.
How to cite: Seyinde, D. and Dey, S.: Adverse Birth Outcomes Attributable to High Heat in Nigeria , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-694, https://doi.org/10.5194/egusphere-egu26-694, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 4
EGU26-9676 | ECS | Posters virtual | VPS32
Deforestation-Driven Surface Warming and Heat Exposure in a Tropical Dry Forest District.Wed, 06 May, 14:30–14:33 (CEST) vPoster spot 4
Deforestation-Driven Surface Warming and Heat Exposure in a Tropical Dry Forest District
Deforestation is widely understood as an important driver of local-scale climate warming in tropical regions, yet its consequences for human heat exposure and associated health risks remain poorly quantified at fine spatial scales. Forest cover regulates land surface temperature through canopy shading and evapotranspiration, suggesting that forest loss may amplify near-surface warming and intensify heat stress beyond background climate change. While global and regional studies have documented warming associated with deforestation, most analyses are conducted at coarse spatial scales and offer limited insight into district-level impacts relevant for human exposure. This gap is particularly evident in tropical dry deciduous forest regions, which experience pronounced seasonal heat stress and support populations heavily dependent on outdoor labor. In India, this type of landscape is widespread, yet fine-resolution assessments linking forest-cover change to heat exposure remain scarce.
This study proposes a district-level investigation of deforestation-driven warming and heat exposure in a district of Jharkhand, which is an ecologically stressed dry tropical forest region characterized by forest degradation and extreme summer temperatures. Forest-cover change since 2000 is quantified using Landsat-based Hansen Global Forest Change data, while land surface temperature patterns are examined using MODIS daytime LST observations. Hourly temperature and humidity fields from ERA5 reanalysis are used to reconstruct diurnal heat exposure and derive heat-stress indicators relevant to outdoor working conditions. Population-weighted exposure metrics and established temperature–health response functions from global burden datasets are employed to explore potential implications for heat-related mortality and losses in safe working hours.
By integrating high-resolution forest, climate, and population datasets, this work aims to isolate the contribution of local forest loss to heat exposure beyond broader regional warming trends. The analysis is expected to provide early evidence of how deforestation can intensify heat risks in vulnerable rural districts, with direct relevance for heat-adaptation planning, forest conservation priorities, and occupational health policies. These insights can inform district-level climate action plans, guide nature-based cooling strategies, and also support targeted interventions to reduce heat exposure among outdoor workers and farmers in tropical dry forest regions.
How to cite: Rani, S. and Sen, S.: Deforestation-Driven Surface Warming and Heat Exposure in a Tropical Dry Forest District., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9676, https://doi.org/10.5194/egusphere-egu26-9676, 2026.
EGU26-16816 | ECS | Posters virtual | VPS32
Heat Stress Impacts on Elite Tennis Performance: Evidence from the Australian OpenWed, 06 May, 14:33–14:36 (CEST) vPoster spot 4
Increasing temperatures create more challenges for outdoor elite sports, particularly high-intensity tournaments such as the Australian Open, where players frequently experience high thermal stress. This study investigates the impact of environmental heat stress on professional tennis performance using high-resolution data from professional tennis matches with environmental performance diagnostics. To quantify these impacts, ATP and WTA singles matches played at various Australian Open tournaments have been analysed in conjunction with ERA5-Land reanalysis data averaged per hour, covering air temperature, relative humidity, global radiation, and wind speed. Heat stress was computed using the Wet Bulb Globe Temperature index and categorised into heat danger levels according to the heat danger classification of Sports Medicine Australia. A hypothesis-driven, uncertainty-aware statistical framework was employed, utilising robust non-parametric tests, trend analyses, and Spearman rank correlations to evaluate the sensitivity of key performance metrics to escalating levels of heat stress. Overall, the results indicate that severe heat stress conditions negatively affect the efficiency of serve and return, the number of unforced errors, the level of performance variability, and the length of a match in ATP and WTA events. More specifically, aggressive serve-related variables, such as aces, demonstrate a partial level of resilience in severe heat, while rally complexity, shot variety, and return length decrease with increased levels of heat stress. When analysed by set status, the results further suggest that while one of the most elite players controls their playstyle in severe heat conditions, the lower-seeded players take more risks and tend to make errors. Taken together, these findings provide large-scale empirical evidence of the impacts of environmental stress during the Australian Open tournament games. In light of these findings, the Australian Open tournament should adjust its schedule to prioritise tennis players’ health, and future tournaments should be scheduled more precisely according to reports from climate scientists and data-informed schedules.
How to cite: Kahraman, G., Turp, M. T., and An, N.: Heat Stress Impacts on Elite Tennis Performance: Evidence from the Australian Open, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16816, https://doi.org/10.5194/egusphere-egu26-16816, 2026.
