These impacts are unevenly distributed and are expected to escalate. Socioeconomic position, age, sex/gender, ethnicity, pre-existing conditions, occupation and place intersect to shape exposure, sensitivity and adaptive capacity. Marginalised groups, including older adults, children, people with chronic conditions, outdoor workers and residents of low-income or geographically exposed areas, bear a disproportionate burden. An intersectional, climate-justice lens is therefore essential across public health, early warning, urban planning and health-system adaptation.
This session, organised in collaboration with the Swedish Centre for Impacts of Climate Extremes (CLIMES), invites contributions that investigate the complex and uneven impacts of climate extremes on population health. We particularly welcome studies that (i) characterise past impacts and future risks from single and compound extremes; (ii) map intersecting socioeconomic, demographic and spatial vulnerabilities; (iii) evaluate adaptation and early-warning interventions (including occupational heat); and (iv) integrate health, climate and social data to inform equitable climate adaptation and public health responses.
Temperature extremes and air pollution are major environmental drivers of mortality. Although several studies have examined the joint health effects of heat and air pollution, the evidence remains largely confined to the summer season, and synergistic effects throughout the year are poorly understood. In particular, the combined effects of air pollution with cold temperatures, as well as how these interactions vary across population subgroups and over time, have received little attention. This study provides a comprehensive continental-scale assessment of the synergistic effects of temperature and air pollution on mortality across both warm and cold seasons in Europe during 2003-2019.
We analyzed daily temperature and mortality data from the EARLY-ADAPT project, covering 654 contiguous regions across 32 European countries and a population of 539 million people, combined with daily estimates of PM2.5, PM10, NO2, and O3 at 10 km spatial resolution. Region-specific analyses were conducted using over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. Joint associations were modelled using product terms between non-linear functions of temperature and linear functions of air pollutants. Relative risks and attributable numbers were estimated, with stratified analyses by sex, age group, cause of death, and time period.
Our findings provide robust evidence of substantial synergistic effects between temperature extremes and air pollution, with pronounced heterogeneity across demographic groups, causes of death, and over time. These results highlight the importance of accounting for compound climate-air pollution risks in public health surveillance. Integrating temperature and air quality information into early warning systems and climate adaptation strategies is essential to reduce preventable mortality and protect vulnerable populations in a changing climate.
How to cite: Borisova, E., Chen, Z.-Y., Stafoggia, M., De’ Donato, F., Urban, A., and Ballester, J.: Synergistic health effects of temperature and air pollution: a continental-scale European study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10593, https://doi.org/10.5194/egusphere-egu26-10593, 2026.
Although the impact of temperature on mortality is well documented, the global burden of temperature-related hospitalization remains underexplored. Additionally, the epidemiological literature contains contradictory evidence regarding the role of humidity in heat-related mortality. We aim to provide novel insights into vulnerability to heat and contribute to clarifying the role of humidity using a large multi-location hospitalization dataset.
We collected daily data on all-cause and cause-specific emergency hospital admissions from more than 209 locations in 33 countries in the Multi-Country Multi-City (MCC) network. We assess the risk of hospitalization associated with heat using multiple heat stress indicators, including daily air temperature, wet-bulb temperature, and apparent temperature. We calculate daily time series of heat-stress indices for each location using hourly climate variables from the ERA5-Land reanalysis dataset. We estimate city-specific associations using time-series regression with distributed lag non-linear models (DLNM) and pool the results using multivariate meta-regression. We then employ a generalized random forest to identify vulnerability profiles based on area-level factors (e.g., poverty, green space) and individual-level factors.
Preliminary results from Switzerland revealed distinct risk patterns by heat-stress indices and cause-specific admissions. We reported the relative risk (RR) at the 99th percentile of the temperature distribution compared to the minimum hospitalization temperature, along with 95% confidence intervals (95% CI). Regarding daily air temperature (T2m), we observed a protective association with cardiovascular hospitalization across all cities, particularly in Basel (RR 0.71; 95% CI 0.53-0.96) and Zurich (0.78; 0.61-0.99). However, when assessing wet-bulb temperature (Twb), this pattern reversed in Lausanne (1.13; 0.8-1.6) and Lugano (1.01; 0.68-1.5), suggesting a potential increased risk. For genitourinary causes, both metrics indicated increased risks in Lugano and Geneva. However, in Geneva, the risk decreased from 1.73 (1.04-2.88) with T2m to 1.64 (0.99-2.73) with Twb. In the next steps, we will replicate the analysis across more than 209 locations and examine how factors such as green space and individual characteristics modify the association between hospitalization risk and heat, humidity, and heat stress.
This research will provide a comprehensive global evaluation of the risk of hospitalizations associated with heat stress and assess the role of humidity. Our study can help improve understanding of how humidity affects temperature-related health risks and identify vulnerability profiles across different countries.
How to cite: Lee, S., Temple, L., Collaborative Research Network, M.-C. M.-C. (., and Vicedo-Cabrera, A. M.: Impact of humidity on heat-related hospitalization risk on a global scale: a multicounty time-series study , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8042, https://doi.org/10.5194/egusphere-egu26-8042, 2026.
Recent evidence shows that anthropogenic climate change is responsible for a large share of heat-related mortality and morbidity globally. Over long time scales, these impacts are modified by demographic and socioeconomic trends, such as population ageing and increasing life expectancy. To better evaluate the societal burden of climate change over time, attribution of impacts beyond mortality counts and risks is needed, including metrics that capture both the quality and length of life.
In this study, we quantify the loss of lifetime attributable to climate change resulting from deaths related to heat and cold. We combine life tables with individual-level mortality data from Mexico, Spain, and Switzerland. We apply state-of-the-art health-impact attribution methods to estimate the association between temperature and years of life lost based on the age at death of each individual. We stratify our analysis by sex and age groups, and aggregate our results to the state level. We obtain observed exposure temperature data from ERA5Land and derive yearly and country-specific counterfactual temperatures by linearly regressing local warming from reanalysis and simulated datasets on attributable global mean surface temperature change.
We show that climate change-attributable heat-related loss of lifetime has increased globally in recent decades. This burden is consistently shifting towards younger individuals. At least 50% is shouldered by individuals who lost more than 20 years of their expected lifetime (i.e., younger than approximately 67 years old in 2024) in Switzerland and Mexico, while in Spain, this share reached 77% already. This increasing heat-related burden is leading to more frequent net losses of lifetime in younger individuals in recent years, accounting for both heat and cold-related deaths. Nevertheless, the attributable net effects of climate change on the entire population are generally negative, driven by a larger reduction in cold exposure in the older population. For older individuals, the net effect shows a decreasing trend with ongoing climate change, which leads to an extension of lifetimes. Only extreme years, like 2003 in Spain and Switzerland, show a net shortening of lifetime across the entire population.
These findings suggest increasing pressure from climate change on heat-vulnerable individuals, reducing their expected lifetime disproportionately. Importantly, this is not exclusive to individuals close to their life expectancy, as individuals with more than 20 years yet to live are the main contributors to attributable years of life lost. These younger individuals are already experiencing climate change as a pressure on their life expectancy across the whole temperature range. In the future, exposure to more frequent and extreme heat could lead to a net loss in lifetime in the overall population, therefore decreasing life expectancy. Our results provide a more nuanced view of which group carries the disproportional burden of climate change health impacts.
How to cite: Schneidewind, T., Lüthi, S., Fischer, E. M., and Vicedo-Cabrera, A. M.: The loss of lifetime related to heat exposure attributable to human-induced climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13472, https://doi.org/10.5194/egusphere-egu26-13472, 2026.
Heat has emerged as a major public health concern. Over 62,000 heat-related deaths were estimated to have occurred during the European summer of 2024, exemplifying the pressing need to develop effective early warning systems. Such systems depend critically on the quality of the underlying forecasts, and recent work has focused on developing impact-based forecasts for heat-related mortality, which provide explicitly impact-oriented information. To date, heat-related mortality forecasts have been based on the output of numerical weather prediction models, or physics-based forecasts. The field of weather forecasting is undergoing a rapid transformation with the advent of skillful data-driven forecasts. This case study compares European heat-related mortality forecasts for 2024 based on physics-based weather forecasts with those based on data-driven weather forecasts. Our results highlight the non-linear relationship between temperature and mortality, and the sensitivity of forecasts to errors at high temperatures, although the generalisability of our results is hampered by the small sample size. The targeted improvement of forecast models for high temperatures would be particularly beneficial for heat-related mortality forecasting, and we suggest the application of this approach to both data-driven and physics-based forecast ensembles as an important next step in the continued development of informative, explicitly impact oriented forecasts.
How to cite: Holmberg, E. and Olivetti, L.: Forecasting European heat-related mortality in 2024: data-driven vs physics-based forecast approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-781, https://doi.org/10.5194/egusphere-egu26-781, 2026.
How to cite: Royé, D., Chiminazzo, V., Tobías, A., and Iñiguez, C. and the MCC Collaborative Research Network: Daytime and Nighttime Heat Exposure and Mortality: A Multicountry Analysis Using Hourly Temperature Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9170, https://doi.org/10.5194/egusphere-egu26-9170, 2026.
While increasing heat is a direct impact of climate change on health, the contribution of climate change to temperature-related neonatal deaths in Low- and Middle-income Countries (LMICs), including Kenya, is unknown. We aim to estimate the temperature-related burden of neonatal deaths (children less than 28 days of age) in Kenya between 2022 and 2024 that is attributable to climate change. We use daily neonatal mortality counts for the period ranging from January 1, 2022, to December 31, 2024, from the Kenya Health Information System (KHIS) tracker database. For heat exposure, we use daily reanalysis mean temperature data from the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), including both the obsclim (factual) and counterclim (counterfactual) scenarios at a 0.5° x 0.5° spatial scale. We perform an extended two-stage design for small geographical areas to estimate temperature-neonatal mortality associations and temperature-related burden of neonatal deaths in Kenya between 2022 and 2024 that is attributable to climate change. The KHIS data includes all hospital-based neonatal deaths (~ 29,000) recorded across all the 47 counties in Kenya between 2022 and 2024. We find that across all counties in Kenya, exposure to extreme heat (99th percentile temperature) relative to the minimum mortality temperature for a period of seven days increases the relative risk of neonatal mortality by 1.517 (95% C.I 1.129 - 2.037), although with important geographical differences. Moreover, we found a larger effect in regions with a smaller ratio of health workers per 100,000 population than in those with a higher ratio, and in areas with poor access to insurance compared to those with higher access. Overall, climate change was responsible for 3.6% (95% C.I. 0.9% – 6.5%) of heat-related neonatal deaths in Kenya between 2022-2024. This study underscores the negative impacts of extreme temperatures on neonatal health. Future increases in global mean temperature will likely amplify heat-related health risks, highlighting the urgent need for climate-informed neonatal health mitigation and adaptation measures to protect newborns' health in the face of a changing climate.
How to cite: Sunguti, E., Thiery, W., Vicedo-Cabrera, A., Vanderkelen, I., Chersich, M., Ochuodho, D., and van Lipzig, N.: Temperature-related neonatal deaths attributable to climate change in Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-995, https://doi.org/10.5194/egusphere-egu26-995, 2026.
The escalating threat of global warming has intensified pervasive concerns over its profound health impacts. Even under ambitious climate mitigation pathways, a substantial ‘cooling gap’ persists, leaving billions of people without access to thermal protection due to socioeconomic constraints. This deficit exacerbates heat-related mortality, undermines labor productivity, and erodes global economic output. Therefore, closing this cooling gap through robust adaptation policies is of paramount importance. Air conditioning (AC) represents one of the most mature and effective interventions for health adaptation. However, the projected scale of future demand and the global health and economic benefits accruing from varying AC adaptation policies remain insufficiently quantified. This knowledge gap obstructs the optimized allocation of climate funds and the design of actionable adaptation polices.
To address this, we introduce a novel framework that quantifies global AC demand and its associated health-economic benefits under various mitigation and adaptation scenarios. We begin by employing a process-based approach to project future cooling demand across 170 countries under 3 SSP-RCPs. We further develop a method to assess how different AC access patterns—defined by operational thresholds and access rates—mitigate global heat-related mortality and labor productivity losses. Second, we construct a series of statistical emulators that efficiently characterize the response relationships between temperature increase, AC access patterns, cooling demand, and health outcomes. These emulators allow us to circumvent the limitations of fixed scenarios, and we use them to evaluate impacts under 27 combined mitigation and adaptation policy scenarios. The mitigation scenarios comprise current NDCs and the 1.5°C and 2°C targets, while adaptation scenarios are centered on Decent Living Standards (DLS), incorporating three AC operational thresholds (no_DLS, lax_DLS, strict_DLS) and three access rates (self_adaptation, 2050_DLS, and 2030_DLS). Finally, these health impacts are integrated into a global CGE model (C3IAM/GEEPA) to assess the consequent effects on the global macroeconomy and inequality.
Our findings indicate that while the global cooling gap will contract as socioeconomic development outpaces warming, developing regions in low-to-mid latitudes will continue to face a "cooling dilemma" characterized by high demand and low adaptive capacity. We find that compared to NDCs, more ambitious mitigation like the Paris Agreement (1.5°C and 2°C) yields relatively modest reductions in health-economic losses (0.03–0.07% of the GDP). In contrast, ensuring universal access to decent cooling by 2050 could halve global GDP losses. Accelerating this goal to 2030 would provide an additional cumulative economic gain of approximately 200 trillion USD. Closing the cooling gap offers robust protection for developing regions—particularly India, Asia, the Middle East and Africa- but it remains insufficient to bridge the deep-seated disparity in losses between developing and developed economies. Operational thresholds significantly dictate both cooling demand and realized benefits, necessitating a strategic trade-off between intervention efficacy and population coverage to ensure global climate equity.
How to cite: Yang, B., Yuan, X.-C., Byers, E., Falchetta, G., Andrijevic, M., and Wei, Y.-M.: Closing the Cooling Gap Could Halve Global Heat-related Health and Economic Losses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5959, https://doi.org/10.5194/egusphere-egu26-5959, 2026.
Dengue incidence and drought severity are rapidly rising globally. It has been shown that measures adopted to cope with drought may unintentionally increase mosquito breeding habitats. Empirical work has linked domestic rainwater harvesting tanks to increased Aedes aegypti presence in urban settings. Yet, conventional epidemiological models rarely represent household behaviour and water-use decisions, while socio-hydrological models typically do not account for how hydro-climatic extremes shape the vector-borne diseases.
Here we present a system dynamics model that, for the first time, explicitly couples climate variability, water shortages, dengue, adaptation options, and social behaviour. We integrate a dengue epidemiological framework with a socio-hydrological representation of human–water interactions, and we examine three adaptive pathways: (i) a dengue-focused response emphasising mosquito control; (ii) a drought-focused response prioritising rainwater tanks for household supply and considering migration as an adaptation to drought; and (iii) a co-adaptation strategy that combines drought and dengue measures, guided by evolving social awareness.
Our results indicate that adaptation choices strongly shape awareness dynamics, water scarcity, the number of infected mosquitoes, and ultimately dengue incidence. Drought-focused strategies reduce average water shortages, but lead to prolonged standing water in rainwater tanks that amplify mosquito proliferation and increase infections. Co-adaptation, through responsive diversification of measures and timely management of tank storage, can preserve drought buffering benefits while limiting suitable habitat for vectors. The proposed model can be used to (i) better predict dengue outbreaks to prioritise surveillance and resource allocation, and (ii) test the effectiveness of combined adaptation portfolios under climate-change scenarios.
How to cite: Mazzoleni, M., DeFilippo, F., Torti, C., Quiros-Roldan, E., and Raffetti, E.: A new Socio-Hydro-Epidemiological model for simulating adaptation dynamics between drought and dengue, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14838, https://doi.org/10.5194/egusphere-egu26-14838, 2026.
Understanding how hydroclimatic extremes translate into human vulnerability is essential for designing effective adaptation strategies in drought-prone regions. This study aims to investigate the relationship between drought conditions and malnutrition outcomes across multiple regions using a combination of climate diagnostics, statistical modelling, and machine learning approaches.
We start with a global assessment linking historical drought events to malnutrition indicators using open-source public-health. To support this analysis, we assemble a multi-source dataset integrating meteorological drought indices, vegetation and soil-moisture indicators, and subnational malnutrition metrics. Our methodological framework first characterizes drought variability across temporal scales to identify dominant spatial and temporal patterns of moisture deficits. We then explore the sensitivity of malnutrition indicators to drought stress using nonlinear and lag-aware statistical techniques, complemented by machine learning models to capture potential complex relationships. This approach enables us to begin isolating the pathways through which hydroclimatic anomalies may influence nutritional outcomes, while accounting for confounding socioeconomic factors. The long-term objective is to translate these insights into a prediction tool for improving anticipatory action.
This initial research effort seeks to contribute to the broader understanding of how climate extremes interact with public-health vulnerability. By developing an analytical framework and openly accessible datasets, this work aims to support disaster-risk management and health preparedness in the face of increasingly complex and escalating climate-related risks in developing more timely and targeted responses.
How to cite: Krouma, M., Galfi, V. M., Poblete Cazenave, M., and de Ruiter, M.: Assessment of the Relationship Between Drought and Malnutrition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2754, https://doi.org/10.5194/egusphere-egu26-2754, 2026.
Climate change is reshaping the environmental conditions that govern vector-borne disease transmission, yet many large-scale assessments continue to rely on simplified climate-based indicators that overlook key biological processes regulating transmission persistence and spatial heterogeneity. Here, we employ a dynamical model, VECTRI, a framework developed by the International Center for Theoretical Physics (ICTP) that integrates climatic and entomological factors to examine how climate change alters vector-borne disease transmission patterns across India. Our results indicate a widespread intensification and spatial redistribution of transmission, with notable expansion into regions that have historically experienced limited exposure, suggesting increasing vulnerability in areas with lower population immunity and limited preparedness. By contrasting dynamical simulations with climate-only metrics, we show that simplified indicators can misrepresent both the location and persistence of future transmission risk, highlighting the importance of integrating climate and entomological processes for improving climate-sensitive disease risk assessments and informing more robust public health planning in a warming world.
How to cite: Baraik, S. K., Parihar, R. S., and Mishra, S. K.: Emerging transmission regimes of vector-borne diseases under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7529, https://doi.org/10.5194/egusphere-egu26-7529, 2026.
Extreme heat has increasingly affected population health over recent decades, with rising occurrences of heat-related mortality and morbidity across different climate zones. The severity of these impacts, however, is not solely determined by ambient temperature; it is profoundly shaped by environmental and social factors such as demographic composition, living and labor conditions, income and education levels. These factors jointly determine vulnerability and adaptive capacity, translating social inequalities into disproportionate health impacts among specific population groups. This study aims to quantitatively characterize the interplay of these social and environmental factors in shaping differences in heat-related hospitalizations in the Netherlands. We focus on admissions due to cardiovascular, respiratory, and direct heat-exposure conditions such as dehydration, renal failure, and heat stroke. Using municipal-level data from Statistics Netherlands (CBS) and climate indicators over a five-year period, we applied Random Forest regressor and classifier algorithms to explore the relationships between heat-related morbidity and a wide set of socioeconomic and demographic variables. Through SHapley Additive exPlanations (SHAP), we interpret the relative importance and interaction effects of predictors while accounting for multicollinearity and nonlinear relationships, advancing over conventional linear models commonly used in vulnerability assessments. The results highlight dominant vulnerability patterns associated with age structure, marital status, labor participation, income, and social assistance, and differentiate linear, nonlinear and threshold effects across variables. The spatial character of the analysis allows the identification of municipalities where multiple vulnerability drivers converge, indicating local “hotspots” of heat-related risk. Our results demonstrate the value of machine learning approaches for uncovering complex, intersectional patterns of vulnerability to extreme heat. Beyond methodological advancement, this work provides actionable insights for spatially targeted adaptation planning and public health interventions. It underscores the urgency of integrating health, social, and climate data in national adaptation strategies to protect populations disproportionately affected by intensifying heat extremes.
How to cite: Sestito, B., Mazzoleni, M., Botzen, W., and Aerts, J.: Unraveling social and environmental drivers of heat-related hospitalizations in the Netherlands through Random Forest analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13467, https://doi.org/10.5194/egusphere-egu26-13467, 2026.
Understanding how extreme temperatures impact mortality across population groups is critical for assessing vulnerability to climate change and designing effective public health interventions. This study builds on previous work analyzing temperature-attributable mortality fractions in Madrid from 1890 to 2019 by converting these into age-specific mortality rates. Using daily temperature and all-cause mortality data, combined with annual population estimates by age group, we estimated time series of temperature-specific mortality rates to capture long term and period changes in mortality through time. These were analyzed over time to assess changes in risk exposure and adaptation patterns. We applied generalized linear models to investigate long-term trends in heat- and cold-attributable mortality rates, accounting for demographic shifts, population aging, and historical public health interventions, including the introduction of heat prevention plans in the mid-2000s. The results show that while the overall mortality rate in Madrid declined substantially over the study period, temperature-specific mortality rates decreased even more sharply. Cold-related mortality showed the strongest declines, while heat-related mortality reductions were more modest. These trends varied by age group and time period, with older adults consistently exhibiting higher vulnerability. By linking historical mortality surveillance with temperature exposure and population data, this study offers a rare long-term perspective on how age-specific vulnerability to temperature extremes has evolved. It contributes methodologically by translating attributable fractions into dynamic mortality rates, enabling direct comparison across time and demographic strata. Our findings underscore the need for sustained climate-health adaptation policies and highlight persistent age-based inequalities.
How to cite: Ordanovich, D., Ramiro, D., and Tobías, A.: Reconstructing mortality burden from temperature extremes using age-specific mortality rates over 130 years in the city of Madrid, Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4887, https://doi.org/10.5194/egusphere-egu26-4887, 2026.
Europe is currently the fastest warming continent and even in temperate countries such as Germany the number of extreme temperature days has been rising. These pose increasing risks to human health and wellbeing. Yet, while dry heat and urban heat islands have received substantial scientific attention, humid heat episodes have historically been rare in Central Europe. Recent observations, however, indicate that their frequency and intensity are growing, and projections suggest that regions once considered climatically temperate may increasingly encounter conditions previously confined to the tropics. In this study, we examine humid heat stress in terms of days with exceptionally high vapor pressure exceeding a critical threshold of 18.8 hPa. With a particular focus on elderly populations (65+), we quantify humid heat stress in Germany for a historical reference period (1961-1990) calculated from ERA5 re-analysis, and for the future under a global warming level of +2 °C using an ensemble of convection permitting RCM simulations at 3 km resolution. An integration with official population counts and projections yields humid heat exposure estimates. Beyond spatio-temporal trends, the analysis decomposes the drivers of change into three components: (i) climate change, (ii) population growth or decline, and (iii) demographic ageing.
Data analysis for the reference period revealed pronounced spatial disparities: average annual humid heat days peak in Berlin and Brandenburg, whereas humid heat is most seldom in Thuringia and Schleswig-Holstein. While population densities are the highest in the three German city states and lowest in the eastern part of Germany, this pattern is also reflected in the proportion of senior citizens. By combining humid heat frequency, population, and elderly share, we derive the number of “senior citizen humid heat events.” In the reference period, this indicator is dominated by population distribution resulting in maximum exposure in Berlin, Hamburg and Bremen. Preliminary results for +2 °C global warming suggest significant changes in climatic hotspots. Ongoing work will assess how these and other spatial patterns are expected to propagate in detail, before quantifying the relative contributions of climate, population, and demographic change to future humid heat exposure in Germany.
How to cite: Scheiber, L., Samakinwa, E., Cohrs, J.-C., Weber, T., Pfeifer, S., and Rechid, D.: Changing humid heat exposure in Germany – where senior citizens will be most affected and why, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18375, https://doi.org/10.5194/egusphere-egu26-18375, 2026.
Short-term exposure to ambient fine particulate matter (PM2.5) is a well-recognized driver of cardiovascular morbidity and mortality. However, current air pollution alert systems are often suboptimal in protecting public health, largely because they do not fully account for the complex, nonlinear exposure-response relationship between PM2.5 levels and cardiovascular outcomes. To address this limitation, this study establishes a globally representative nonlinear exposure-response function and determines an optimal public health alert threshold that effectively balances health benefits with the reduction of societal disruption.
We performed a systematic review and meta-analysis covering 100 epidemiological studies, yielding 123 effect estimates published up to May 2025. To estimate the nonlinear curve, we applied a novel three-stage meta-regression model that integrates spline functions with structural causal modeling theory. Furthermore, by utilizing global gridded datasets regarding PM2.5 concentrations, population distribution, and baseline mortality from 2000 to 2023, we quantified the cardiovascular mortality burden and employed a ROC-like analysis to identify the optimal alert value.
Our meta-analysis indicates a pooled risk ratio of 1.009 (95% CI: 1.0074–1.011) for cardiovascular mortality per 10 μg/m3 increment in short-term PM2.5. The derived exposure-response curve reveals a distinct supralinear shape: marginal risks are elevated at lower concentrations, plateau at moderate levels (~75-150 μg/m3), and surge sharply again beyond 150 μg/m3. In 2023, pollution episodes exceeding the WHO first-stage interim target (75 μg/m3) were associated with an estimated 59,399 (95% CI: 38,126–82,413) attributable cardiovascular deaths globally. The analysis identifies 136 μg/m3 (95% CI: 129–148) as the optimal alert threshold. Implementing warnings at this specific level could potentially prevent 73.2% (95% CI: 71.8%–76.6%) of attributable deaths while impacting only 32% of at-risk person-days.
In conclusion, a significant nonlinear relationship governs short-term PM2.5 exposure and cardiovascular mortality. The optimal alert value identified in this study provides critical evidence for designing more scientific, efficient, and health-oriented air pollution warning systems, thereby maximizing public health protection while minimizing social costs.
How to cite: Deng, J., Yang, Y., and Xue, T.: Optimizing Air Pollution Warning Systems: A Global Assessment of PM2.5-Mortality Nonlinearity and Alert Thresholds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12683, https://doi.org/10.5194/egusphere-egu26-12683, 2026.
Farmworkers are highly vulnerable to heat stress. We describe the results of an interdisciplinary approach to mapping, measuring, anticipating and mitigating farmworker heat stress in the Imperial Valley, California. We combine climate modeling, remote sensing, in situ physiological measurements, farmworker-evaluated apps, and farmworker and stakeholder interviews on structural vulnerability to heat. Several heat guidelines (State, Federal) are evaluated for their impact on mandated rest break minutes. Key findings include: a) air temperature, land surface temperature, and wet bulb globe temperature have all increased over a 20 year period, with increased rates of health threshold excedance; b) crops harvested during the daytime in spring and summer, including orchards and grapes, have the greatest heat exposure and high metabolic expenditure; c) labor-intensive activities other than harvesting continue throughout the summer, with consequent risk of heat exposure; d) guidelines that use air temperature result in significantly fewer rest minutes than heat indices such as the wet bulb globe temperature; e) farmworkers are subject to structural vulnerability due to lack of political power and socioeconomic status, resulting in persistent heat exposure with weak government oversight or enforcement; f) web-based apps can be developed and evaluated in collaboration with the farmworker community to provide early warning systems and real-time guidance on adaptive and protective behaviors. We conclude with recommendations for policy, management, interventions, and adaptation measures, including plans to evaluate in-field cooling structures.
How to cite: Biggs, T. W., Ciborowski, H., Parajuli, S., Lopez-Galvez, N., Thompson, C., Monteverde, C., Roberts, D., de Sales, F., McMahon, C., Quintana, V., Hurtado-Gonzalez, S., Toji-Ruiz, B., Toji-Ruiz, B., Valencia, D., Bravo Martinzez del Valle, M., Rutan, R., Lafler, R., Portillo, F., Villalobos Ayala, A., and Madonia, S. and the Additional team members: Rural Heat Islands: Interdisciplinary mapping, prediction, and mitigation of farmworker heat stress, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13367, https://doi.org/10.5194/egusphere-egu26-13367, 2026.
The Paris Agreement marked a historic step toward a safer and more equitable world, establishing a shared legal and political framework for addressing climate change. Yet, a decade on, current nationally determined contributions (NDCs) and pledges— even if fully implemented—are projected to lead to around 2.6°C of global warming above pre-industrial levels, leaving the planet dangerously hot.
Since 2015, heat early warning systems and action plans have increased across the globe, demonstrating growing recognition of extreme heat as a major climate risk. However, progress remains uneven and slow, particularly due to limited financing for heat adaptation at the local level, mainly in rapidly urbanizing cities of the Global South. The costs of inaction are escalating faster than the pace of adaptation: health systems are being overwhelmed, productivity and labour capacity are declining, infrastructure is under stress, and the world’s most vulnerable populations risk being left unprepared for intensifying heat extremes..
Here, we show how six recent, highly impactful extreme heat events across the globe have changed in both likelihood and intensity under historical warming levels (since the signing of the Paris Agreement; ~1.0°C and 1.3°C) and under future warming conditions (2.6°C and 4°C), alongside the distribution of impacts and progress in heat action plans.
How to cite: Kimutai, J., Arrighi, J., Keeping, T., and Otto, F.: A Decade of the Paris Agreement: Unequal Heat Burdens and Urban Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16492, https://doi.org/10.5194/egusphere-egu26-16492, 2026.
European summers are warming rapidly, increasing the frequency and intensity of hazardous heat. Here we quantify how mean-state warming has increased Germany's summer extreme-heat burden and how that risk scales with global warming level (GWL). We introduce OMS (Observation-based Mean-Shift), an observation-based counterfactual framework that preserves observed day-to-day weather variability while shifting only the seasonal mean state. Using OMS, we quantify Germany's sensitivity of June--August mean temperature to global mean surface temperature and translate observed summers to pre-industrial, +1.5 °C, and +2.0 °C climates by shifting the observed daily JJA series. Because only the seasonal mean is adjusted, OMS isolates the thermodynamic signal while leaving circulation statistics unchanged, providing a conservative baseline.
Germany exhibits strong regional amplification, with an estimated sensitivity of +2.63 °C of local summer warming per +1 °C of global warming and a 95% confidence interval of 1.62–3.62. To connect warming to exposure-relevant outcomes, we define the extreme-heat burden, EHD, in °C·days as cumulative degrees above a fixed national summer 95th-percentile threshold for 1991–2020. We evaluate the high-impact summers of 2018, 2019, and 2022, producing warming-level-consistent counterfactual realizations for each event while retaining intra-seasonal variability. Across these events, anthropogenic warming yields a substantial increase in EHD from pre-industrial to present-day conditions, with sharp further escalation toward +1.5 °C and +2.0 °C. Subnational analyses show coherent increases across all federal states but with substantial heterogeneity in magnitude, highlighting where risk intensifies most strongly as warming progresses. We additionally quantify per-capita burden using population data and assess distributional equity using Lorenz curves and Gini coefficients. Gini coefficients show that total extreme-heat burden is distributed fairly evenly across years, whereas per-capita extreme-heat burden is notably more concentrated. This implies that while the overall hazard is broadly spread across federal states, population-normalized exposure is substantially more unequal, with a disproportionate share of per-capita heat burden concentrated in a subset of states.
How to cite: Samakinwa, E., Scheiber, L., Cohrs, J.-C., Pfeifer, S., Tewes, T., and Rechid, D.: Mean-state warming loads Germany’s extreme-summer heat burden, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17267, https://doi.org/10.5194/egusphere-egu26-17267, 2026.
Climate change has amplified health consequences from heatwave exposure, resulting in the exacerbation of existing inequities from structural racism and environmental discrimination. Even so, research has not adequately prioritized the examination of heatwave impacts on morbidity at refined spatial scales alongside the characterization of specific or intersectional community characteristics that relate to these injustices. This study examined the spatio-temporal relationship between the exposure to 27 heatwave definitions and acute care utilizations from 2006 to 2019 across California ZIP code tabulation areas (ZCTAs) and assessed how 145 community characteristics may influence susceptibility. A within-community matched design paired with a spatial Bayesian hierarchical model considered variation in these associations at a fine spatial scale, and a random effects meta-regression was applied to evaluate their modification by community characteristics. Across the state, the 1-day 95th percentile of maximum temperature definition was found to have the greatest population attributable number (29,723; 95% CI: 27,691, 31,722). Predominantly positive relationships were identified at the ZCTA level, where both the Central Valley and Southern California were the most impacted regions. Communities experiencing certain social, cultural, and economic discrimination, particularly those with higher proportions of American Indian/ Alaska Native male residents under 5 years old, residents using the Supplemental Nutrition Assistance Program (SNAP), and Asian male residents, were observed to be the most susceptible to heat-related health impacts. These findings may support future efforts to elucidate underlying mechanisms of heat-related health disparities and inform heat action plans that prioritize the most affected communities to reduce their health burden.
How to cite: Teyton, A., Chen, C., Hansen, K., Brown, H., Hale, M., and Benmarhnia, T.: Fine-Scale Spatio-Temporal Patterns in the Heat-Related Health Burden Within California (2006-2019): The Role of Structural Racism and Environmental Injustice, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18579, https://doi.org/10.5194/egusphere-egu26-18579, 2026.
