International Journal of Biometeorology最新文献

Increasing temperature will impact future outdoor worker safety but quantifying this impact to develop local adaptations is challenging. Wet bulb globe temperature (WBGT) is the preferred thermal index for regulating outdoor activities in occupational health, athletic, and military settings, but global circulation models (GCMs) have coarse spatiotemporal resolution and do not always provide outputs required to project the full diurnal range of WBGT. This article presents a novel method to project WBGT at local spatial and hourly temporal resolutions without many assumptions inherent in previous research. We calculate sub-daily future WBGT from GCM output and then estimate hourly WBGT based on a site-specific, historical diurnal cycles. We test this method against observations at U.S. Army installations and find results match closely. We then project hourly WBGT at these locations from January 1, 2025, to December 31, 2100, to quantify trends and estimate future periods exceeding outdoor activity modification thresholds. We find regional patterns affecting WBGT, suggesting accurately projecting WBGT demands a localized approach. Results show increased frequency of hours at high WBGT and, using U.S. military heat thresholds, we estimate impacts to future outdoor labor. By mid-century, some locations are projected to average 20 or more days each summer when outdoor labor will be significantly impacted. The method’s fine spatiotemporal resolution enables detailed analysis of WBGT projections, making it useful applied at specific locations of interest.

Traditional climate classification and weather typing systems are not designed to understand and prevent heat illness, or to design effective cooling strategies during extreme heat. Thus, we developed the Heat Stress Compensability Classification (HSCC) combining open-source historical weather data (2005–2020) with biophysical modeling of a standard human, in the sun or shade, during peak city-specific hot hours on the top 10th percentile hottest days in 96 U.S. cities. Four categories of uncompensable heat stress (UHS)––which can result in rising core temperature––were established based on the relative constraints of dry and evaporative heat exchanges for achieving heat balance in proportion to constant metabolic heat production (112Wm⁻²). Results show that 88.7% of these peak-hot hours meet the UHS criterion, and 41% present a dry heat gain of 70 to 150Wm⁻² while allowing a maximum evaporative loss between 90 and 140Wm⁻². Evaporative heat loss constraints dominate the eastern U.S. Dry heat gain was widespread, yet particularly high in the south and southwest. Full shade reduces UHS frequency to 7.6%, highlighting the importance of quality shade access and accounting for radiative load in heat stress assessments. Although there are five distinct categories (one compensable and four UHS), the HSCC is dynamic and customizable, providing actionable information on thermal variations within a given category. These variations depict the reason for UHS (e.g., limited evaporative cooling) and, thus, how to concentrate cooling efforts, particularly at the limits of physiological adaptability. Findings facilitate developing targeted criteria for heat stress reduction with potential global applications.

To evaluate the impact of acute meteorological changes (i.e., maximum temperature, humidity, wind speed, atmospheric pressure, cloud coverage, visibility, precipitation) as situational risk factors proximal (i.e., present in the hours directly preceding) to suicide attempts. Participants were 578 adult patients who were hospitalized within 24 h of a suicide attempt at the only Level 1 trauma hospital in the state of Mississippi. Participants completed a semi-structured interview to determine home address and exact timing of their suicide attempt. A within-person, case-crossover design was used with each patient serving as their own control. Meteorological variables were generated for the 6-hours preceding each patient’s suicide attempt (case period) and corresponding hours the day prior (control period). Conditional logistic regression analyses were used to examine predictors of suicide attempts, and biological sex and season were evaluated as potential moderators. The presence of precipitation was associated with reduced odds of suicide attempts. Wind speed was marginally positively associated with suicide attempts among males, and visibility was positively associated with suicide attempts among females. Maximum temperature was positively associated with suicide attempts in the spring. Wind speed, visibility, maximum temperature, and precipitation (absence of) may represent situational risk factors for suicide attempts. Future studies should evaluate additional near-term situational risk factors and determine how to leverage this information to improve suicide risk management efforts to ultimately ameliorate the burden of suicide.

The Intergovernmental Panel on Climate Change, IPCC predicts that hot seasons will get even hotter due to global climate change. There exists a critical dependence of human metabolic processes on temperature. Changes in thermal balance therefore, have an adverse effect on health because they raise body temperature, cause excessive sweating, and accelerate the rate of dehydration. Different nations and professional groups use different techniques to measure heat strain. This paper aims to review previous research conducted in the area of heat strain due to heat exposure among workers in Southeast Asia and also to profile mitigation strategies in North East India. Studies conducted between the years 2011 to 2023 in the evaluation of the health impacts of occupational heat stress were searched systematically using several sources of databases like PubMed, Google Scholar, Science Direct, Web of Science, Scopus, etc. It was noted that a greater proportion of previous research on evaluating physiological effects was carried out in controlled environments as opposed to real-world field settings. While such studies give us valuable insights into the relationship, applying the same methodology in the workplace may not be feasible. In India, very few research has been carried out on workplace heat stress, and even fewer have been done in North East India using physiological indicators. North East India is also affected by global climate change leading top more hotter days than before. The region of Northeast India, particularly Guwahati (Assam), has recently seen extreme heat waves during the sweltering summer months. With less literature available in this geographical location, studies with actual field-based settings are much needed to understand the occupational health impacts in this region. This review can formulate a suitable methodology for assessing the health impacts in working environment. This can also help the local health professionals to recognize the heat strain parameters that are acceptable worldwide, and use as pertinent indicators to scrutinize worker's health and develop preventive agendas as climate change advances.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

Cross-national studies examining the relationship between weather and crime are rare. Reasons are manifold but include the differences in countries due to geographical, climatic, and seasonal variations. In contrast in this study we examine the causal impact of temperature and rainfall anomalies on violent crime in locations located in two comparable geographic zones: Khayelitsha (in South Africa) and Ipswich (in Australia). We use ANOVA and Tukey's tests to identify statistical meaningful differences (if any) in the impact of these weather anomalies on crime alongside the use of visualisations capturing the anomalous weather-violence relationship in these two contexts. Results show some similarities but also notable differences between locations which we attribute to their inherent socio-demographic differences which we expand upon. We conclude by highlighting the benefits of cross-national crime research, and motivate for its increased application in future research of this nature.

Understanding the influence of climatic factors on vegetation dynamics and cumulative effects is critical for global sustainable development. However, the response of vegetation to climate and the underlying mechanisms in different climatic zones remains unclear. In this study, we analyzed the response of vegetation gross primary productivity (GPP) to climatic factors and the cumulative effects across various vegetation types and climatic zones, utilizing data on precipitation (P_r), temperature (T_a), and the standardized precipitation evapotranspiration index (SPEI). The results showed that: (1) GPP showed significant differences among the seven climatic zones, with the highest value observed in zone VII, reaching 1860.07 gC·m^- 2, and the lowest in zone I, at 126.03 gC·m^- 2. (2) GPP was significantly and positively correlated with temperature in climatic zones I, IV, V, and VI and with precipitation in climatic zones I, II, and IV. Additionally, a significant positive correlated was found between SPEI and GPP in climatic zones I, II, and IV. (3) Drought exerted a cumulative effect on GPP in 45.10% of the regions within China, with an average cumulative duration of 5 months. These effects persisted for 6-8 months in zones I, II, and VII, and for 2-4 months in zones III, IV and VI. Among different vegetation types, forests experienced longest cumulative effect time of 6 months, followed by grasslands (5 months), croplands (4 months), and shrublands (4 months). The cumulative time scale decreased with increasing annual SPEI. The varying responses and accumulation of GPP to drought among different vegetation types in various climatic zones underscore the complexity of vegetation-climate interactions the response and accumulation of GPP to drought.

Olea europaea L. is an emblematic tree plantation of the Mediterranean basin and one of the main sources of allergenic pollen. In this study, we examined variations in airborne Olea pollen season, trends and built forecast models based on multiple regression analysis over a 13-year period (2008-2019, 2022) in NW of Morocco (Tétouan), focusing on start date of pollination (SDP), end date of pollination (EDP), peak date (PD), and pre-peak pollen Integral (PPI). Spearman's correlation analysis highlighted the importance of different pre-season meteorological parameters on the features of Olea pollen season depending on the period considered. SDP became earlier with increasing minimum temperature in March, while EDP was mainly influenced by precipitation in February and PD is earlier with increasing maximum temperature and precipitation in February. Linear regression results indicated a trend toward a shorter pollination period, almost significant, by delaying SDP rather than earlier EDP, probably due to the significant decrease in minimum temperature between January and April. The best regression models predicted the characteristics of the Olea pollen season to within 2 days and a value close to the PPI at 45 pollen*day/m³, and achieved an accuracy between 58 and 95%. The strongest predictors when forecasting SDP, EDP, PD and PPI were minimum temperature in March, precipitation in April, maximum temperature in February and minimum temperature in November, respectively. Findings suggest that olive reproductive cycle is considerably dependent on pre-season meteorological parameters. Further performed statistical analysis should be made to improve traditional models using a long data series.

Purpose: This study aimed to investigate the relationship between meteorological factors, specifically temperature and precipitation, and the incidence of appendicitis in Seoul, South Korea.

Methods: Using data from the National Health Insurance Service spanning 2010-2020, the study analyzed 165,077 appendicitis cases in Seoul. Time series regression modeling with distributed-lag non-linear models was employed.

Results: Regarding acute appendicitis and daily average temperature, the incidence rate ratio (IRR) showed an increasing trend from approximately - 10 °C to 10 °C. At temperatures above 10 °C, the increase was more gradual. The IRR approached a value close to 1 at temperatures below - 10 °C and above 30 °C. Both total and complicated appendicitis exhibited similar trends. Increased precipitation was negatively associated with the incidence of total acute appendicitis around the 50 mm/day range, but not with complicated appendicitis.

Conclusions: The findings suggest that environmental factors, especially temperature, may play a role in the occurrence of appendicitis. This research underscores the potential health implications of global climate change and the need for further studies to understand the broader impacts of environmental changes on various diseases.