搜索结果：Climatological

This study investigates bioclimatic comfort conditions in Iran by integrating perspectives from traditional Persian Medicine (PM) with modern climatological methodologies. The primary objective is to develop the Persian Medicine (PMI) as a criterion for evaluating comfort zones across Iran and to compare its results with modern climatological indices, specifically the Terjung Index (TI) and the Standard Effective Temperature Index (SETI). Daily meteorological data from 2003 to 2018 were utilized. To construct the PMI, qualitative variables describing temperate regions were extracted from classical PM texts and implemented Python programming, Excel and ArcGIS 10.8 for spatial analysis. The analysis focuses on May, September, and December, months that exhibited the highest degree of similarity among the comfort zones identified by PMI, TI, and SETI. Results indicate that the spatiotemporal distribution of comfort zones derived from SETI corresponds most closely with those identified by PMI, representing the highest levels of climatic and physiological comfort throughout the year. Overall, the findings demonstrate a strong correspondence between modern climatological comfort indices and PMI-based assessments in temperate regions of Iran. This research highlights the potential of integrating traditional medical- environmental knowledge with modern climate science, providing valuable insights for medical geography, environmental health management, and climate adaptation planning under global warming conditions.

Malaria remains a significant public health issue in sub&#x2011;Saharan Africa, influenced by climate variability, population movement and urbanisation. In South Africa, efforts to eliminate malaria operate within a dynamic environment where climatic fluctuations and population movements complicate risk patterns. However, existing approaches to analyzing climate-malaria relationships, often rely on seasonal averages, ignoring the potential influence of rare climatic extremes and migration flows. This study investigates how climatic extremes, climatic variability, and migration flows interact to shape malaria risk and inform adaptive surveillance and control strategies. Climate data (monthly minimum/maximum temperature, rainfall and relative humidity) for Limpopo, Mpumalanga and KwaZulu&#x2011;Natal (2000-2024) were obtained from the South African Weather Service. Laboratory&#x2011;confirmed malaria cases were sourced from provincial surveillance reports, and in&#x2011;migration data into Thembelihle and selected parts of Soweto (2017-2024) were obtained from the Soweto and Thembelihle Health and Demographic Surveillance System (SaT&#x2011;HDSS). Seasonal climate trajectories were evaluated using Ordinary Least Squares regressions. Climatic seasonality was assessed using diagnostic metrics, humidity plateaus, rainfall extremes and thermal season&#x2011;length indices. Shock&#x2011;year dynamics (KwaZulu&#x2011;Natal 2022 floods; Limpopo 2017 Cyclone Dineo) were analysed using monthly anomalies against 2000-2010 climatological envelopes. Pearson correlations examined same&#x2011;year and lag&#x2011;1 climate-malaria associations. Climate-malaria-migration co&#x2011;movement was assessed using min-max&#x2011;normalised rainfall, humidity, temperature, incidence and migration data (0-1). Malaria tail events were defined as years at or above the provincial 90th percentile. All statistical analyses were conducted using IBM Statistical Package for the Social Sciences (SPSS), Version 30; p&#x2009;<&#x2009;0.05 was considered significant. Climate trends across the three endemic provinces showed consistent minimum-temperature warming, declining warm-season humidity, and rainfall patterns increasingly dominated by short-duration extremes. Province-specific seasonality diagnostics revealed that KwaZulu-Natal experienced frequent multi-month humidity plateaus, Limpopo showed pronounced lagged hydrological effects following extreme rainfall events, and Mpumalanga displayed prolonged warm-season durations in recent years. Shock-year analyses (KwaZulu-Natal 2022 floods; Limpopo 2017 Cyclone Dineo) demonstrated compound climatic anomalies, combinations of intense rainfall, sustained humidity, and sub-seasonal cooling that extended environmental suitability beyond expected seasonal boundaries. Same-year humidity was positively associated with malaria incidence in KwaZulu-Natal and Mpumalanga, whereas lag-1 rainfall showed a significant relationship with malaria in Limpopo, reflecting hydrological carry-over effects. Following the April-May 2022 floods, migration into Thembelihle increased markedly, and a co-movement analysis indicated temporal alignment between malaria tail years, elevated rainfall, higher minimum temperatures, and increased migration during climatologically disrupted periods. Climatic extremes and post&#x2011;shock mobility reorganise malaria risk in South Africa. Tail&#x2011;aware, lag&#x2011;sensitive and mobility&#x2011;integrated surveillance is essential for adaptive elimination planning.

Heatwaves are becoming more frequent and intense in Bangladesh, particularly in urban areas such as Dhaka, where the combined effects of extreme heat and humidity pose serious public health risks. Temperature-based warning systems frequently underestimate physiological heat stress. This study proposes a machine-learning framework to estimate and project the Heat Index (HI) using 3-hourly meteorological records from 2014 to 2023. Because HI is a deterministic function of air temperature and relative humidity, the framework performs conditional estimation of HI based on meteorological predictors rather than independently forecasting HI itself. Five models: ARIMAX, SARIMAX, Random Forest Regressor (RFR), XGBoost, and Long Short-Term Memory (LSTM), were trained using air temperature, relative humidity, and seasonal indicators as predictors. For scenario-based projections beyond 2023, future temperature and humidity were approximated using historical monthly averages, generating scenario-based HI projections that preserve seasonal and diurnal patterns. These projections represent climatological scenarios rather than true meteorological forecasts. The Random Forest Regressor (RFR) achieved the highest conditional estimation accuracy, with the lowest RMSE (0.85&#xb0;C) and highest R&#xb2; (0.987) on the test set. Empirical 95% prediction intervals achieved 98.85% coverage, indicating slightly conservative uncertainty bounds. Scenario-based projections yielded mean HI values of 29.02&#xb0;C (optimistic), 29.90&#xb0;C (moderate), and 31.33&#xb0;C (pessimistic). A substantial proportion of projected 3-hourly periods fall within the "Extreme Caution" category (32-41&#xb0;C), indicating persistently elevated heat-stress exposure under climatological assumptions. The proposed framework demonstrates strong potential for generating high-resolution scenario-based HI projections by capturing nonlinear temporal dynamics and sub-daily variability. These findings can support scenario-based early warning systems and inform adaptive urban heat-management strategies in climate-vulnerable cities such as Dhaka, although results should be interpreted as conditional projections rather than deterministic forecasts. Unlike conventional HI studies, this framework translates meteorological inputs into high-resolution, operational heat-risk insights by modeling temporal persistence at sub-daily scales.

Stratospheric ozone (O3) losses and consequent increases in surface ultraviolet (UV) radiation remain a health concern due to close association with skin cancers and cataracts. This study estimated how emissions of ozone-depleting substances (ODS) affect, via O3 reductions, surface UV radiation and associated health impacts in Australia compared to the United States. We used climatological atmospheric data and the U.S. Environmental Protection Agency's Atmospheric Health Effects Framework (AHEF) model to estimate these effects for historic and projected ODS emissions. Compared to the United States, the Australian population is potentially exposed to ca. 24% more biologically weighted UV radiation because of the closer proximity of its population to the equator, less stratospheric O3 at comparable latitudes, and seasonal variations in the Earth-Sun distance. Surface UV increments owing to ODS-caused stratospheric O3 depletion were largest in the 1990s, while the estimated health impacts peak several decades later at ca. 0.5% for cataracts, 1% for melanoma incidence, and 5% for keratinocyte cancer incidence (relative to 1980), and are similar in both countries. The study found that differences in population-weighted surface UV can only partly explain Australia's substantially higher incidence and mortality rates for UV-related maladies. Other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role. Ozone in the upper atmosphere helps block harmful ultraviolet (UV) radiation from reaching Earth's surface. Chemicals known as ozone&#x2010;depleting substances (ODS) can reduce ozone levels, allowing more UV radiation through. Increased UV exposure increases the risk of health problems like skin cancer and cataracts. The amount of UV that reaches the surface varies by location. In this study, we used a model to compare UV exposure and resulting skin cancer and cataract rates between the United States and Australia. Australians receive about 24% more harmful UV radiation under clear skies. Our study explains that this is due mostly to Australia's closer proximity to the equator, and partly due to climatological differences in stratospheric ozone and seasonal variations in the Earth&#x2010;Sun distance. We further estimate that ozone depletion has increased incidence rates of UV&#x2010;induced illness similarly in both Australia and the USA, and by less than 5 percent. Overall, the study found that differences in UV radiation only partly explain the significantly higher rates of UV&#x2010;induced illness in Australia than in the USA, suggesting that other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role.

The timing of spring onset is a widely used indicator of climate change impacts, yet estimates of phenological trends depend critically on the choice of reference climate period. Using long-term daily air temperature records from 21 meteorological stations across Estonia, this study examines how different climatological normals influence estimates of spring onset timing and its change. Spring onset was defined as the sustained transition of mean daily air temperature above 5&#xa0;&#xb0;C and analysed across four overlapping reference periods (1965-1990, 1971-2000, 1981-2010, and 1991-2020). Mean spring onset dates vary substantially among reference periods, demonstrating that the concept of an "average" spring is sensitive to the selected normal. Cumulative changes derived from linear trends reveal a widespread advancement of spring onset over 1965-2020, with particularly strong shifts at several inland and coastal stations. When contrasting equal-length 30-year normals (1971-2000 vs. 1991-2020), trend magnitudes are generally weaker in the most recent period and, at some stations, near zero or positive. Comparison across reference periods indicates pronounced temporal heterogeneity in phenological responses. While some stations exhibit strong advances during the late twentieth century followed by weakening in the most recent climate normal, others show sustained strong change or weak and inconsistent responses. These patterns cannot be adequately described by a single linear trend and do not follow a simple geographic gradient. The results demonstrate that reference period selection can substantially affect both the magnitude and interpretation of phenological trends. It is therefore recommended that phenological studies explicitly report reference periods and, where possible, compare trend estimates across multiple climatological normals to improve the robustness and transparency of climate impact assessments.

Heat stress limits for human survivability have been previously defined by a 6-hour exposure to a wet-bulb temperature of 35oC. However, the recently developed physiology-based HEAT-Lim model demonstrates that environmental heat stress thresholds may be cooler and drier than previously thought. We employ HEAT-Lim to determine whether non-survivable thresholds were surpassed during six historical events where conditions were climatologically extreme and/or high heat-related mortality was reported. Our results show that non-survivable conditions are occurring during present-day heat events, all of which are below 35oC wet-bulb temperature. Of concern is regular exceedances of deadly thresholds for older people directly exposed across all events. Moreover, extremely hot yet dry conditions are found to be just as deadly as hot and humid conditions. For future climatological assessments, we emphasise the importance of employing increasingly accurate physiology-derived methods to assess the risk of potentially deadly heat stress.

Among the myriad consequences of climate change, global warming and extreme weather events are particularly critical due to their well-documented impact on neurological and psychological well-being. However, the severity of these impacts varies significantly by geography. This article assesses the potential adverse effects of climate change on "brain health" through a risk management framework. The analysis begins by evaluating existing governance and risk-assessment procedures, followed by an examination of human adaptive capacities and natural risks. The latter draws upon climatological data-specifically regarding global warming and the "tropicalization" of the Euro-Mediterranean region-as well as anthropological insights. Building on this foundation, we propose strategies for effective risk control, including adaptation, mitigation, and preparedness. Success depends on the mobilization of public health researchers and professionals to drive organizational change and implement preventative measures to address extreme events. Consequently, the article advocates for specific decisions regarding communication, education, and early-warning systems to enhance rescue efficiency and prevent disasters. The discussion concludes with a focus on mitigation strategies specifically tailored to the Euro-Mediterranean region to address the challenges of climate tropicalization.

Meteorology can alter bioaerosol properties, potentially enhancing their impact on public health and cloud microphysics. The BioAerosols and Convective Storms (BACS) study was conducted over May-June 2022 and 2023 in Northern Colorado and examines how convective storm processes such as precipitation and cold pools affect bioaerosol concentrations and properties, including pollen, fungal spores and bacterial endotoxin. The two seasons were vastly different climatologically, with drought-like conditions and greater endotoxin concentrations during 2022 and near record rainfall with higher fungal spore concentrations during 2023. Online (fluorescence) and offline (chemical tracer) measurements were used to characterize bioaerosols, alongside collocated measurements of ice-nucleating particles (INPs). Precipitation events generally increased supermicron fluorescent particle concentrations which consisted primarily of fungal spores, as determined from fungal spore counts, chemical tracers, and fluorescent particle types. Storm-generated cold pools had more varied impacts on bioaerosols, sometimes causing depletion and other times enrichment, with peak fluorescent particle concentrations correlating significantly with cold pool strength (r s = 0.79, p < 0.05, n = 12), indicating that stronger cold pools produce greater increases in local bioaerosol concentrations. Biological INP concentrations in air active at warmer than -15 &#xb0;C from 1-10 &#xb5;m in size were enhanced by roughly one order of magnitude in samples collected during convective storms compared to pre-rain samples. Contributions of fungal spores to the enhanced INPs were supported by a significant correlation between large (2.5-10 &#xb5;m) heat-labile INP concentrations active at -15 &#xb0;C with mannitol, a fungal spore tracer (r = 0.91, n = 8, p < 0.01). This study found convective storms can greatly increase boundary-layer concentrations of fungal spores and warm-temperature biological INPs, leading to high exposure risks for sensitized populations and the potential for bioaerosols to influence cloud processes.

The digitization of academic publications and newspapers from the 1800s has permitted identification of several authoritative sources that credit Dr. Joseph W. Gleitsmann with establishing the first successful tuberculosis sanitarium in the United States in Asheville, North Carolina, in 1875, antedating by 9 years the Trudeau Sanatorium in Saranac Lake, New York. The facility used German climatological methods and a defined medical treatment program. Gleitsmann's Mountain Sanitarium for Pulmonary Diseases had a 30-bed occupancy and published outcomes data from 5 years of clinical experience by 1880. By 1910, Asheville had become a tuberculosis care "colony," with 25 private tuberculosis sanitaria with a national referral base. Asheville was a key driver of the development of climatotherapy in the treatment of tuberculosis and other respiratory ailments in the preantibiotic era. From 1870 to 1930, medical, mental health, and wellness tourism largely drove the population growth (1500 to 50&#x2009;000) of Asheville, a previously remote Appalachian town. The stigmatization of tuberculosis sufferers is illustrated by restrictive municipal regulations that led to the demolition of almost all tuberculosis sanitaria within Asheville city limits by the 1920s. The Von Ruck Research Laboratory for Tuberculosis produced more than 50 papers from 1890 to 1930, published mostly in the Journal of the American Medical Association and the Journal of Immunology. These included pioneering immunotherapy studies with tuberculin variants and the first robust description of the antigenic profile of Mycobacterium tuberculosis. Tuberculosis was both incurable and a leading cause of death, and thus perseverance with fractionated tubercle bacillus products and subunits by so many is understandable in the context of the times. By analogy, public health now seems more ready to accept disease-specific immunotherapy agents and vaccines that save lives even if they are substantially less than 100% effective.

While previous studies have reported an increase in future fire weather under global warming scenarios, there is still a limited understanding on how it may evolve under mitigation scenarios. Using idealized emission-driven climate simulations, this analysis examines changes in the extreme fire weather conditions under net-zero emission (ZeroE) and net-negative emission (NegE) scenarios. Compared to peak CO2 levels, the NegE scenario achieves substantially greater fire danger mitigation, particularly in climatologically high-fire-danger regions, because of lower temperatures and increased relative humidity. In contrast, the ZeroE scenario leaves fire danger elevated globally, especially in Northern low-latitude regions. Regional responses are shaped by differences in atmospheric moisture supply, influenced by changes in the strength of the Atlantic meridional overturning circulation and the latitudinal position of the intertropical convergence zone. These findings highlight the crucial role of aggressive CO2 removal strategies in mitigating fire weather dangers and underscore the limitations of ZeroEs alone in addressing global wildfire challenges.

To examine the level of agreement between teachers' perceptions of weather conditions within the National Safe Routes to School (SRTS) tally method with objective weather conditions. Secondary data analysis of the Safe Travel Environment Evaluation in Texas Schools (STREETS) study from January to April 2024 included 47 elementary schools in Central Texas. The third, fourth, and fifth-grade teachers reported perceived weather conditions (sunny, rainy, overcast, snow) in the morning and afternoon of each school day on the SRTS tally. National Oceanic and Atmospheric Administration Local Climatological data were used to calculate objective weather conditions (sunny, rainy, overcast) at the time closest to the start (7:10) and end (15:25) of school for the same days of SRTS tally measures. Kappa statistics and percent agreement assessed the level of agreement between perceived and objective weather conditions in the morning and afternoon for each tally day per grade. For third-grade teachers, percent agreement between perceived and objective weather ranged from 0 to 14.3&#xa0;% for sunny, 2.9-10.3&#xa0;% for rainy, and 0-20&#xa0;% for overcast. For fourth-grade teachers, the percent agreement ranged from 2.4 to 7.9&#xa0;% for sunny, 2.6-12.2&#xa0;% for rainy, and 0-13.2&#xa0;% for overcast. For fifth-grade teachers, the percent agreement ranged from 2.5 to 8.1&#xa0;% for sunny, 2.5-12.8&#xa0;% for rainy, and 2.6-25&#xa0;% for overcast. Kappa statistics ranged from -0.06 to 0.15, -0.10 to 0.07, and&#xa0;-&#xa0;0.04 to 0.07 for grades three, four, and five. We found poor agreement between teachers' perceptions and objective measures of weather conditions.

This paper uses monthly district-level data to analyze the economic and social consequences of natural disasters in Latin America. The identification strategy exploits spatial and temporal variation in disaster occurrence through an event study design. The findings show that natural disasters substantially reduce economic activity during the first month following the event, with heterogeneous effects. Hydrological disasters have immediate impacts, while meteorological and climatological disasters show lagged effects. Small districts are particularly vulnerable, exhibiting decreases in economic activity that persist for up to 4 months. In the social dimension, disasters increase protest frequency by 69%, although no changes in the use of public force are observed. These findings suggest the need to (i) develop differentiated response systems according to disaster type, (ii) strengthen resilience capabilities in small districts, and (iii) incorporate conflict prevention and management mechanisms in disaster response protocols.

Exposure to ultraviolet (UV) radiation significantly impacts human health. Consequently, comprehensive UV climatological databases are of great interest. UV exposure is evaluated by weighting UV spectra with spectral functions that describe physiological responses at each wavelength. The most widely used function is the erythemal weighting function, which is used to compute the UV Index (UVI) to assess the health risk associated with UV overexposure. The ERA5 datasets, produced by the Copernicus Climate Change Service (CDS), offer hourly ground-level UV radiation ([Formula: see text]), but do not include UVI. This study proposes a model to compute hourly UVI using exclusively ERA5 data, enabling direct access through the CDS to derive UVI statistics for locations of interest and potentially supporting the integration of a dedicated UVI product into ERA5. The model was developed using UV spectra simulated under clear-sky conditions with the uvspec radiative transfer model, accounting for atmosphere type, solar zenith angle, visibility, altitude, albedo, total ozone, and aerosol type. For these parameters, representative values typical of tropical, mid-latitude, and subarctic regions were used, effectively excluding Arctic conditions, and considering UVI values&#x2009;&#x2264;&#x2009;12. The resulting formulation expresses UVI as a function of [Formula: see text], sun elevation, and total ozone. The model was validated using ground-based UVI measurements from six stations (over 17000 cases) and, in addition, compared with UVI derived from Copernicus Atmospheric Monitoring Service (CAMS) products (over 6000 cases) taken as a reference. Performance was assessed through the statistics of the differences between measured/modelled values and CAMS data under three scenarios: clear-sky conditions, varying cloud cover, and all-sky conditions. Under clear-sky conditions, the model uncertainties showed a small positive bias (&#x2264;&#x2009;0.5), with the absolute difference (AD)&#x2009;<&#x2009;1 in 73% of the cases for ground measurements and in 86% of the cases for CAMS. The root mean squared difference (RMS) and the mean absolute deviation (MAD) were 0.9 and 0.7, respectively, for ground measurements, and 0.7 and 0.6 for CAMS. Under cloudy-sky conditions, model performance worsens significantly for CC&#x2009;>&#x2009;0.4, with RMS and MAD reaching values of about 1.5. However, when considering relative uncertainties (percentage ratio between RMS and reference values of UVI), up to CC&#x2009;<&#x2009;0.7 the RMS% remains below 15% for Very High-to-Extreme WHO/ICNIRP exposure categories and below 20% for Moderate-to-Extreme categories. A comparison between CAMS UVI and ground measurements was also performed, yielding results consistent with those described above. As an example, Appendix A illustrates how the model can be applied to generate daily and monthly UVI statistics over large geographical areas using only ERA5 data accessed through the CDS web portal.

Climatological variations, triggered by global warming and rising temperatures, have become a growing concern, posing challenges to communities across the United States, Europe, and Asia. Earth heats, climate patterns shift, and the resulting climate instability triggers more persistent and powerful heatwaves, leading to significant ecological and health-related consequences. Disturbances in thermoregulation can lead to elevated core body temperature (CBT>39&#xa0;&#xb0;C); this typically occurs during heat stress (HS), a state wherein the body's capacity to cool itself is challenged by multiple external (environmental conditions, pathogens) or internal factors (inflammatory, metabolic, hormonal, and neurological), often precipitating systemic inflammation and multiple organ failure. HS pathological cascade involves different interconnected processes like oxidative stress, inflammation, compromised circulation, disrupted blood-brain barrier (BBB), coagulation irregularities, organ-specific responses, electrolyte imbalances, heat shock proteins (HSPs), and interactions with pre-existing conditions. To effectively address this emerging public health issue, a combined approach is needed, like incorporating pharmacological treatments such as non-steroidal anti-inflammatory drugs (NSAIDs), diuretics, muscle relaxants, vasodilators, beta-blockers, and anti-anxiety agents with essential non-pharmacological supports like public health education, cooling centres, early detection systems, and individualized plans specifically designed for high-risk groups. This review provides insight into the concept of heat-induced injury on the cellular level, the worldwide prevalence of HS, the pathogenic mechanisms behind Heat Stress-induced Multiple Organ Dysfunction (HS-MOD), and the various therapeutic strategies available.

Concurrent extreme fire weather creates favorable conditions for widespread large fires, which can complicate the coordination of fire suppression resources and degrade regional air quality. Here, we examine the patterns and trends of intra- and interregional synchronous fire weather (SFW) and explore their links to climate variability and air quality impacts. We find climatologically elevated intraregional SFW in boreal regions, as well as interregional synchronicity among northern temperate and boreal regions. Significant increases in SFW occurred during 1979 to 2024, with more than a twofold increase observed in most regions. We estimate that over half of the observed increase is attributable to anthropogenic climate change. Internal modes of climate variability strongly influence SFW in several regions, including Equatorial Asia, which experiences 43 additional intraregional SFW days during El Ni&#xf1;o years. Furthermore, SFW is strongly correlated with regional fire-sourced PM2.5 in multiple regions globally. These findings highlight the growing challenges posed by SFW for firefighting coordination and human health.

Climate indicators are essential for monitoring ongoing climate change, supporting climate impact research, conducting spatial hot spot analyses and assessing attribution questions. These efforts rely on high-quality, reliable datasets that adhere to FAIR data principles. We present a curated dataset of 117 climate indicators for Austria, covering the period from 1961 onward at a 1-km spatial resolution. The dataset includes climate indicators related to temperature, precipitation, radiation, snow, runoff and humidity, with spatial (area means) and temporal (climatological reference period means) aggregations to enable rapid climate impact analysis. The workflow used to compute these indices is supported by a careful technical validation procedure and is designed to ingest diverse climate datasets, enabling the creation of climate indices beyond the scope presented here. Both the dataset and the workflow thus offer a robust, flexible and user-friendly resource for advancing climate research and supporting informed decision-making.