International Journal of Biometeorology最新文献

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.

This study focuses on assessing tourists' perception of bioclimatic comfort in the urban context of Porto, Portugal, specifically in the areas of Avenida dos Aliados and Praça da Liberdade. The study examines the relationship between meteorological conditions, tourists' clothing choices, and their physical activity levels. The study integrates microclimatic measurements and questionnaire surveys carried out during the summers of 2019 and 2020, and the winter of 2019-2020. A comprehensive questionnaire following international standards was administered to a representative sample of 563 tourists. The results show significant variations in mean air temperature (AT), wind speed (Wχ), relative humidity (RH), global radiation (G_RAD), and total mean radiant temperature (T_MRT) over the study periods. The assessment of Outdoor Thermal Comfort (OTC) is based on ASHRAE 55 standards, using the Thermal Sensation Vote (TSV) scale and the tourists' opinions on their thermal preferences. Clothing choices are found to be influenced by AT, with tourists choosing lighter clothing in warmer conditions. Gender and age differences in clothing insulation (Icl) are identified, suggesting potential differences in OTC perception. AT varied significantly, with an inflection point in clothing choices at 21.7°C and a correlation between AT and reduction in clothing layers (r² = 0.846; p < 0.05). The study also observes seasonal variations in physical activity levels of tourists, with higher activity levels in summer due to milder weather (110.0 W·m⁻²). More thermally comfortable environments tend to promote a sense of well-being among visitors, which directly affects their satisfaction during their stay in the city. When tourists feel comfortable with the thermal conditions of the urban environment, they are more likely to explore and enjoy local attractions for longer periods of time, thereby enhancing their cultural and leisure experiences. Women tend to wear fewer layers of clothing than men in summer, reflecting potential differences in OTC perception. Results align with previous studies, indicating the impact of clothing insulation of individual subject (Icl) on OTC varies across locations and cultures. Cultural factors influence clothing preferences and thermal tolerance, emphasizing the need for nuanced considerations in understanding OTC perceptions. The study provides to the understanding of the OTC of tourists in the city of Porto, but also offers relevant contributions for improving the visitor experience and sustainable development, namely in other geographical contexts. The major contribution of this research lies in the comparative analysis of Icl and OTC between tourists, based on physical measurements and questionnaire surveys conducted in summer and winter, providing valuable insights for tourist spot design.

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.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

Introduction: The 1.2 °C rise of global ambient temperature since the pre-industrial era has led to an increase the intensity and frequency of heatwaves. Given the heightened vulnerability of pregnant women to heat stress, there is an urgent need for tools which accurately assess the knowledge, risk, and perception of pregnant woman toward heatwaves, enabling effective policy actions. In this research, we developed and validated tools to evaluate pregnant women's perceptions of heat wave risks and behaviors.

Method: We developed 50 items across seven constructs using the Health Belief Model, identified through a systematic literature review. The constructs comprised 8 Knowledge(K) items, 4 in Perceived Vulnerability (PV), 5 in Perceived Severity (PS), 6 in Perceived Benefit (PB), 4 in Perceived Barrier (PBa), 5 in Cue to Action(Cu) and 18 in Adaptation(A). Cognitive testing was performed with a separate group of pregnant women(n = 20). The tested tools were then administered to 120 pregnant women residing during the spring-summer 2023. Construct validation utilized exploratory factor analysis.

Results: The Principal Axis Factoring Method was employed in the EFA with oblimin rotation for 51 items, considering communality > 0.20, and aiming to extract three factors. Across the three factors with Cronbach's alpha > 0.70, a total of 11 items were distributed. Factor 1 included Perceived Severity (PS1, PS2, PS3 and PS5); Factor 2 included Cue to Action (Cu1, Cu2, Cu3, and Cu4); and Factor 3 encompassed Perceived Vulnerability (PV1, PV2, PV4). Only two of the retained items had factor loadings > 0.50, namely PV4 and PS5. Consequently, the three constructs measuring Perceived Severity, Cues to Action, and Perceived Vulnerability using the HBM among pregnant women were deemed valid.

Conclusion: Our study has successfully validated a highly reliable tool which stands ready for application in assessing pregnant women's risk perception regarding heatwaves.

Climate change is a global problem that is accompanied by the significant changes in humidification conditions in many regions all over the world. The study examined spatiotemporal changes in humidification zones in southern Russia in the period 1961-2020. Humidification zones were determined in accordance with the classification of the Selyaninov hydrothermal coefficient. During the research period, a significant increase in average annual temperatures was noted for the region (+ 0.31 °C/decade) and generally positive, but insignificant, dynamics of annual precipitation (+ 4.80 mm/decade). These changes were accompanied by a significant shift in the humidification zones. Despite some periods of decline, the territories of semi-desert and desert zones, as well as dry steppes, have significantly expanded from the east of the region to the west in the last decade. The expansion of these zones was primarily due to a reduction in the area of the more humid steppe zone, with a lesser contribution from the forest-steppe zone. Overall, during the study period, the area of semi-desert and desert zone expanded by an average of + 5.505 thou km²/decade. The zone of the Taiga and deciduous forests has not undergone significant changes. The results obtained indicate arid warming and a general deterioration in humidification conditions in most of southern Russia in the period 1961-2020. According to calculations, further warming, other things being equal, can lead to an even greater expansion of the semi-desert and desert bioclimatic zone, which can provoke a number of socio-economic and environmental problems, especially in the eastern part of the region.