Search Results (59)

In 2024, the annual global surface temperature rose more than 1.5 °C above pre-industrial levels for the first time. This single-year figure is not the same as the long-term warming limit set by the Paris Agreement, but it signals a rising heat-related risk. Extreme heat is already the deadliest weather hazard in the United States, yet most outpatient clinics have no structured protocol for vulnerable patients. This narrative review does three things: it explains how heat stress injures compromised cardiovascular and renal systems, catalogs the primary care medications that impair thermoregulation, and offers a Heat Action Plan (HAP) toolkit for family medicine clinics. We searched PubMed, EMBASE, Google Scholar, and Web of Science for work published from 2000 to May 2026, supplemented by WHO, CDC, and national heat-health guidelines. Patients with cardiovascular disease, chronic kidney disease, diabetes, obesity, and serious mental illness face disproportionate risk during heat events. More than 20 drug classes, among them diuretics, beta-blockers, anticholinergics, and renin–angiotensin–aldosterone system inhibitors, weaken heat defense by blunting sweating, reducing cardiac output, or suppressing thirst. We present a three-tier risk classification and a four-stage toolkit that, when built into routine chronic disease care, may help reduce preventable heat-related illness and death. Full article

Climate change is increasing the frequency, intensity, and duration of heatwaves, making extreme heat a recurrent public health challenge with important nutritional implications. High ambient temperatures affect human health through thermoregulatory strain, sweating, dehydration, electrolyte imbalance, cardiovascular and renal stress, impaired cognitive and physical performance, and exacerbation of chronic diseases. Diet and hydration may therefore represent modifiable, although still underinvestigated, components of heat-health prevention. This narrative review synthesizes evidence on hydration, electrolyte balance, meal composition, dietary quality, vulnerable populations, Mediterranean dietary patterns, seasonality, environmental sustainability, and public health strategies for reducing heat-related health risks. Evidence from climate-health research, physiology, nutrition science, occupational and sports medicine, geriatrics, pediatrics, and public health guidance was integrated. Particular attention was given to distinguishing direct heat-health evidence from recommendations based on indirect evidence, physiological plausibility, or expert consensus. Regular water intake, water-rich foods, avoidance of alcohol, moderation of sugar-sweetened beverages, and individualized electrolyte replacement during prolonged or intense sweating are supported by physiological rationale and public health guidance, although direct heatwave-specific intervention studies remain limited. Dietary recommendations should prioritize smaller, digestible, nutrient-dense meals that preserve nutritional adequacy while reducing unnecessary digestive and metabolic burden. However, evidence on meal size, macronutrient composition, and heat-related outcomes remains largely indirect. Vulnerable groups, including older adults, children, pregnant women, individuals with chronic diseases, outdoor workers, athletes, and socioeconomically disadvantaged populations, require tailored strategies. The Mediterranean diet may provide a useful regional model because it emphasizes seasonal plant foods, fruits and vegetables with high water content, legumes, whole grains, olive oil, culinary simplicity, and environmental sustainability; however, its specific role in heat adaptation requires further study. Integrating nutrition and hydration into heat-health action plans may improve preparedness and support climate-resilient food systems. Future studies should evaluate hydration protocols, meal patterns, Mediterranean diet adherence, biomarkers of nutritional heat resilience, and nutrition-sensitive interventions in real-world heat exposure settings. Full article

Background: Although extreme heat is associated with adverse cardiovascular outcomes, the relationship between heat-alert-threshold days, acute myocardial infarction (AMI) admissions, and post-AMI mortality remains uncertain. We aimed to evaluate the association between temperature-defined heat-alert-threshold days and (i) daily AMI admissions and (ii) cumulative all-cause mortality after hospitalized AMI in Hungary. Methods: We conducted a nationwide registry-based study using data from the Hungarian Myocardial Infarction Registry (HMR). All AMI admissions between 1 January 2018 and 31 December 2019 were eligible, with mortality follow-up through 16 June 2021. Heat-alert-threshold days were defined using the temperature criterion applied in the Hungarian national heat-health action plan (daily mean temperature ≥25 °C). AMI admissions were analyzed using adjusted quasi-Poisson regression models and cumulative mortality using stratified Cox proportional hazards models. This operational temperature threshold was used as the exposure definition and does not represent linkage to administrative heat-alert declarations. Results: The cohort included 30,883 AMI events from 29,596 unique patients (mean age, 67.2 [SD 12.8] years; 60.3% male). Patients admitted on heat-alert-threshold days had baseline clinical characteristics similar to those admitted on non-alert days (all standardized mean differences <0.10). Heat-alert-threshold days were associated with fewer recorded AMI admissions during summer (adjusted incidence rate ratio [aIRR] 0.93, 95% CI 0.90–0.97). In contrast, no association was observed between heat-alert-threshold exposure and subsequent all-cause mortality after AMI hospitalization (adjusted hazard ratio [aHR] 0.96, 95% CI 0.87–1.05). These findings remained consistent across sensitivity analyses. Conclusions: In this nationwide Hungarian registry-based study, temperature-defined heat-alert-threshold days were associated with fewer recorded AMI admissions during summer but not with differences in post-AMI mortality among hospitalized patients. The observed inverse association should not be interpreted as evidence of a protective effect of heat exposure. Alternative explanations include behavioral adaptation, delayed care-seeking, exposure misclassification, residual confounding, and the possibility of unmeasured out-of-hospital cardiovascular events, which were not captured by the registry. Because administrative heat-alert declarations were not linked to the analytic dataset, the findings should be interpreted as associations with temperature-defined heat-alert-threshold days rather than evaluations of the effectiveness of the Hungarian heat-alert system. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a densely populated, highly connected coastal urban region that is exposed to typhoons, extreme rainfall, flooding, storm surges, heat-related risks, and public health emergencies. Existing studies have clarified many aspects of hazard exposure and spatial vulnerability, but evidence on how residents receive warnings, interpret risks, prepare, evacuate, and contribute to community resilience remains dispersed across disciplines and jurisdictions. This study maps the intellectual structure and thematic evolution of research on residents’ responses to urban disaster risks in the GBA. Following a PRISMA 2020-aligned identification and screening process, 144 records from the Web of Science Core Collection were manually screened. Bibliometric analysis, keyword co-occurrence analysis, a region–hazard matrix, and a BERTopic-inspired topic modelling procedure were then used to identify publication trends, disciplinary sources, spatial hazard patterns, and latent themes. The results show accelerated growth after 2020 and identify six interrelated themes: risk perception and preparedness; evacuation and shelter accessibility; urban flooding vulnerability; typhoon, storm surge, and coastal community risk; community resilience and climate adaptation governance; and health emergency response and psychosocial resilience. The review synthesises these findings into a heuristic framework linking risk information, cognitive appraisal, response action, and community resilience. The GBA is treated as an analytically informative, rather than statistically representative, case of a high-density, coastal, and multi-jurisdictional urban region. The findings suggest that resident-centred resilience planning should connect trusted and actionable warnings with inclusive digital communication, accessible protective resources, and cross-boundary coordination for compound hazards. Full article

Green and Blue Infrastructure (GBI) is increasingly addressed within climate adaptation and mitigation policies as a strategic operational measure for reducing climate-related impacts in urban environments. However, GBI effectiveness relies strictly on the biophysical condition of natural assets and on the capacity to monitor and interpret ecosystem processes over time, specifically vegetation quality and its physiological response to climatic stressors within complex urban fabrics. This variability emphasizes the need to integrate ecosystem performance into decision-making through digital frameworks capable of quantifying and accounting for ecological resources across temporal scales. In this context, Remote Sensing (RS) technologies provide a structured informational basis for assessing vegetation health and surface thermal patterns in relation to climatic stress thresholds and human exposure. This paper presents a policy-aligned geospatial evidence framework to bridge the gap between environmental monitoring and urban climate action. By integrating high-resolution multispectral remote sensing with heterogeneous spatial datasets and climate models, the framework enables the multitemporal assessment of ecological conditions to inform where GBI measures can support SECAP implementation, project refinement, and monitoring activities. Developed within the Horizon Europe KNOWING project and applied to the Naples East district, Italy, the framework was operationalized within the city’s Sustainable Energy and Climate Action Plan (SECAP). The application produces scenario-oriented outputs for interpreting the potential contribution of GBI and NbS measures to outdoor heat-stress reduction under SECAP conditions. Its practical value lies in translating biophysical data into reusable GIS/WMS layers that connect ecological performance, climate exposure, socio-energetic vulnerability, and planned urban transformations, thereby supporting SECAP implementation, project refinement, and monitoring. Full article

Heat exposure is an increasingly important climate-related determinant of mental health, yet the biological mechanisms linking heat to psychiatric outcomes remain insufficiently integrated. This narrative review synthesizes evidence from climate medicine, thermoregulation, neuroscience, psychoneuroimmunology, sleep research, psychopharmacology, epidemiology, and psychiatry to examine how acute and chronic heat exposure may influence mental health. Heat acts as a multi-system biological stressor by challenging thermoregulation, increasing autonomic and cardiovascular strain, disrupting hydration, impairing sleep and circadian rhythms, activating stress-response systems, and contributing to neuroinflammation, oxidative stress, mitochondrial dysfunction, blood–brain barrier disruption, and neurotransmitter alterations. These pathways may interact with psychotropic medication exposure and impaired behavioral thermoregulation, particularly among people with severe mental illness, older adults, individuals receiving lithium, people with cognitive impairment, substance use disorders, outdoor workers, and those living in poorly cooled environments. Heat-related psychiatric vulnerability is therefore transdiagnostic but heterogeneous, with relevance to distress, depression, anxiety, mood instability, psychosis, substance-related crises, cognitive dysfunction, delirium, and suicide-related outcomes. The review also distinguishes pathways supported by stronger human evidence from mechanisms supported mainly by experimental, preclinical, severe-spectrum, or indirect evidence. It proposes an integrative biological model and an evidence-informed translational agenda focused on exposure assessment, biomarkers, sleep and circadian monitoring, medication safety, risk prediction, physiological adaptation, heat-mitigation strategies, heat-health action plans, and implementation-oriented evaluation. Full article

Extreme heat poses health risks during pregnancy, but little is known about how pregnant individuals seek weather information to engage in heat-health protective actions. This study examined associations between routine and event-driven weather information seeking and both routine physiological heat-health protective actions (i.e., limiting sun exposure, staying hydrated, and spending time in air conditioning) and higher-threshold adaptive behaviors (i.e., changing plans due to heat). A cross-sectional survey of 195 pregnant individuals in Bexar County, TX, USA, was conducted during the summer and fall of 2024. Descriptive and nonparametric analyses explored relationships across trimesters. Participants demonstrated high routine weather information seeking and greater weather information needs since becoming pregnant. Over half (51.3%) reported increased weather information seeking during excessive heat, with lower increases during the first trimester. During extreme heat, most respondents increased heat-health protective actions. Increased information needs during pregnancy were significantly related to heat-health protective actions. Routine weather checking showed weak or inverse relationships with changing plans, suggesting that routine weather awareness alone may not prompt changing plans. Trimester patterns indicated heightened information seeking and protective actions later in pregnancy. Findings highlight the importance of pregnancy-specific heat risk communication with trimester-specific guidance provided in clinical counseling, public health messaging, and meteorological communication. Full article

Intensifying heatwaves threaten the reliability of electric distribution systems, yet the quantitative relationship between heatwave characteristics and observed power outage behavior remains poorly understood at multi-year, statewide scales. This study develops an event-based, spatiotemporal framework to quantify heatwave-induced outage risk across 254 Texas counties from 2014–2021 by integrating county-level EAGLE-I outage records with reanalysis-derived heat index measurements. An adaptive percentile-based threshold identifies 3048 heatwave events; logistic regression quantifies the probabilistic relationship between heat intensity and major-outage occurrence under three severity definitions. Across 3048 identified heatwave events, 51% involved at least one outage, a rate significantly above the non-heatwave warm-season baseline and revealing widespread heat-related reliability challenges. Outage severity and duration exhibit heavy-tailed distributions, with a small number of extreme events disproportionately affecting customers. Logistic regression models under three severity definitions (P90, P95, and ≥500 customers) demonstrate that heat intensity is a statistically robust probabilistic predictor of major outages, with each +1 °F increase in mean event heat index raising the odds by approximately 43–52%. The predicted probability of a P90-severity major outage approximately doubles across the interquartile range of event heat intensity (~7% to ~14%), providing actionable guidance for utility pre-staging decisions during forecast heatwave episodes. These findings offer a scalable methodology for climate-related reliability assessment, supporting grid hardening, resource planning, and public health preparedness. Full article

Climate change represents one of the defining global health challenges of the 21st century, with far-reaching implications for population health, health systems, and health equity. The acceleration of environmental change, evidenced by record-breaking global temperatures, extreme weather events, and ecological degradation, poses a direct threat to achieving Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all. This manuscript presents a narrative review and policy analysis of the intersection of climate change and global public health in light of the outcomes of the 2025 United Nations Climate Change Conference (COP30) in Belém, Brazil. Drawing on peer-reviewed literature, major institutional reports, and relevant policy documents, we explore how climate change exacerbates communicable and non-communicable diseases, undermines health system resilience, and disproportionately affects vulnerable populations worldwide. Particular attention is given to heat-related morbidity, infectious disease expansion, air pollution, food and water insecurity, displacement, gender inequities, antimicrobial resistance, and mental health impacts. The paper highlights the significance of the Belém Health Action Plan (BHAP), which is treated here as a COP30-associated action framework that places health more centrally within climate policy discussions. However, major challenges remain, including its voluntary orientation, the absence of dedicated financing mechanisms within the framework itself, and limited clarity on accountability arrangements, as identified through our synthesis of the available policy and evidence base. We argue that achieving SDG 3 is no longer feasible without integrating climate adaptation and mitigation into health systems and policies, and that progress will depend on translating global commitments into context-specific country strategies, governance arrangements, and implementation pathways. Full article

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities. Full article

Winter cities face the dual pressures of climate change and population aging, urgently requiring a shift from a singular focus on winter protection toward a development model adaptable to both winter and summer conditions. This shift is essential to enhance social resilience and safeguard the health of all age groups. This case study investigates how the thermal environment of life-sustaining streets in winter cities correlates with older adults’ daily activities. Employing Spearman correlation analysis, a heat exposure–pedestrian flow coupling matrix, and a comprehensive risk diagnostic model, the research analyzes the spatiotemporal variation patterns and underlying drivers of the street thermal environment. The key findings are: (1) All 15 surveyed streets exhibited Wet Bulb Globe Temperatures (WBGT) exceeding 28 °C during peak activity hours, with afternoon values (17:00–19:00) up to 2.7 °C higher than morning values. (2) On East Chaoyang Road, despite building shade and a high Visible Green Index (39.68%), the WBGT ranked second highest. This condition is attributed to a critically low average wind speed of 0.69 m/s (significantly below the city’s summer average of 2.67 m/s) and the widespread use of low-albedo asphalt, which collectively trap heat and negate the benefits of shading. (3) Using a dual-dimensional diagnostic framework, four streets were identified as dual-pressure streets with their combination of high elderly pedestrian flow (exceeding 126 persons/h) and high thermal risk (WBGT > 29 °C), marking them as priority intervention units. Based on these findings, the study proposes categorized street retrofit strategies that synergistically integrate climate adaptation and aging-friendliness. This provides an actionable, evidence-based foundation for planning decisions to support the sustainable renewal of winter cities amid climate change and population aging. Full article

Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high computational cost or yield statistically accurate but physically inconsistent results. Urban air quality in mountain–valley cities is strongly shaped by thermal inversions and weak nocturnal ventilation that trap pollutants close to the surface. We present a hybrid physics–machine-learning framework that combines a Navier–Stokes surface-layer model with data-driven post-processing to produce short-term forecasts of wind, temperature, and particulate matter while preserving physical consistency. The approach captures diurnal ventilation patterns and the well-known negative linkage between near-surface wind and particulate loadings during wintertime inversions. Compared with purely statistical baselines, the hybrid system improves short-range forecast skill and maintains interpretability through physically grounded diagnostics. Beyond Almaty, the workflow is transferable to other mountain–valley environments and is directly actionable for early warning, traffic and heating-related emission management, and health-risk communication. By uniting physically meaningful fields with lightweight Machine Learning correction, the method offers a practical bridge between computational fluid dynamics and operational decision support for cities facing recurrent stagnation episodes. Aim: Develop and verify a method for the diagnostics and short-term forecasting of surface circulation and particle concentrations in Almaty (2024), ensuring physical consistency of fields, increased forecast accuracy on 6–24 h horizons, and interpretability of risk factors. Compared to purely statistical baselines (R² ≈ 0.55 for PM forecasts), our hybrid framework achieved a 16% gain in explained variance and reduced RMSE by 25%. This improvement was most evident during winter inversion episodes. Methods: This study introduces a hybrid modeling framework that integrates the Navier–Stokes equations with machine-learning algorithms to diagnose and forecast surface air circulation and particulate matter concentrations. The approach ensures both physical consistency and improved predictive accuracy for short-term horizons (6–24 h). The Navier–Stokes equations in the Boussinesq approximation, the energy equation, and K-closure particulate matter transport were used. The numerical solution is based on the projection method (convection—TVD/QUICK, pressure—Poisson equation). The ML module is gradient boosting and decision trees for meteorological parameters, lags, and diagnostic quantities. The 2024 data are cleaned, normalized, and visualized. Results: The hybrid model reproduces the diurnal cycle of ventilation and concentrations, especially during winter inversions. For 6 h: wind RMSE ≈ 1.2 m/s (R² ≈ 0.71), temperature RMSE ≈ 1.8 °C (R² ≈ 0.78), and particles RMSE ≈ 0.012 mg/m³ (R² ≈ 0.64). Errors are higher for 24 h. A negative relationship between wind and concentration was established: +1 m/s reduces the median by 10–15% during winter nights. Conclusions: The approach can be generalized to other mountain–valley cities beyond Almaty. Combining the physical model and ML correction improves short-term predictive ability and maintains physical consistency. The method is applicable for air quality risk assessment and decision support; further clarification of emissions and consideration of urban canyon geometry are required. The results support early-warning systems, health risk communication, and urban planning. Full article

Urban trees play a critical role in mitigating climate change by capturing atmospheric CO₂ and providing multiple co-benefits, including cooling urban environments, reducing building energy demand, and enhancing citizens’ physical and psychological well-being. This study presents the Co Carbon Trees Measurement project, a citizen science initiative implemented in the city of Viladecans, Spain, involving 658 students, local administration, and academia, three components of the EU mission’s quadruple helix governance model. Over one year, 1274 urban trees were measured for trunk diameter and height to quantify annual CO₂ sequestration using a direct measurement approach combining field data collection with a mobile application for a height assessment and a flexible measuring tape for diameter. Results indicate that carbon fixation increases with tree size, displaying a parabolic function with larger trees sequestering significantly more CO₂. A range between 10 and 20 kg of CO₂ is sequestered by the urban trees in the period 2024–2025. The study also highlights the broader benefits of urban trees, including shading, mitigation of the urban heat island effect, and positive impacts on mental health and social cohesion. While the total CO₂ captured in Viladecans (≈810 tons/year) is small relative to city emissions (≈170,000 tons/year), the methodology demonstrates a scalable, replicable approach for monitoring progress toward climate neutrality and integrating urban trees into planning and climate action strategies. This approach positions green infrastructure as a central component of sustainable and resilient urban development. Full article

Outdoor thermal comfort is a critical determinant of urban livability and public health, particularly in the face of the increasing frequency and intensity of extreme weather events. While meteorological variables are well-established drivers of thermal stress, the influence of ambient air pollution on human thermal perception remains poorly understood and largely overlooked in urban climate research. To address this gap, this study investigates the multidimensional effects of six major air pollutants PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO on the Universal Thermal Climate Index (UTCI) across 370 Chinese cities from 2020 to 2024. Using integrated spatiotemporal analysis, we found significant seasonal, diurnal, and climatic heterogeneity in pollutant–UTCI interactions. Our findings reveal that O₃ and PM₁₀ amplify thermal stress during summer daytime through photochemical heating and radiative forcing, whereas PM_2.5 and CO reduce nocturnal heat loss in winter by trapping long-wave radiation, effectively acting as thermal insulators. These effects are further modulated by local climate: arid regions (e.g., Lanzhou) experience exacerbated O₃-driven heat stress, while cold zones (e.g., Harbin) benefit from particulate-induced warming in winter. Meteorological factors serve as dual regulators; temperature and solar radiation directly elevate the UTCI, while wind and humidity govern pollutant dispersion and thus indirectly shape thermal comfort. This study not only advances the scientific understanding of air pollution’s role in urban thermal environments but also provides actionable, data-driven insights for climate-resilient urban planning, public health interventions, and integrated environmental policies that jointly address air quality and thermal comfort in rapidly urbanizing regions. Full article

Climate change is reshaping weather patterns and atmospheric circulation globally, particularly in monsoon-dominated tropical environments. To examine how these changes are unfolding in Bangladesh, we extend the Spatial Synoptic Classification (SSC) using ERA5 reanalysis (1960–2024) at three representative stations (Chittagong, Khulna, and Sylhet) to assess long-term changes in the SSC weather types and their internal meteorological properties. The SSC calendars were constructed and analyzed for seasonal distribution, interannual trends, and decadal anomalies of temperature and dew point. Results reveal that Bangladesh’s climatology is dominated by Moist Tropical (MT), Moist Moderate (MM), and Dry Moderate (DM) weather types with a coherent seasonal cycle. Interannually, MT increased strongly across all stations, while MM and DM declined significantly. Decadal anomalies show consistent warming and moistening since the 2000s, which are most pronounced for Dry Tropical (DT) and MT. These findings indicate that climate change in Bangladesh is expressed not only through shifting frequencies but also through evolving thermodynamic characteristics of daily weather types, underscoring the SSC framework’s value in tropical monsoon regions for generating actionable climate information to support heat-stress planning and climate-health services. Full article