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In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases. [ABSTRACT FROM AUTHOR]

Associations of exertional heat exhaustion (EHE)-related symptoms with lifestyle habits and health factors specific to female athletes, ranked by relative risks from high to low, remain elusive. Addressing this issue would benefit athletes and coaches, ensuring safer practices during summer sports activities. To address this issue, we distributed paper-based questionnaires to seven universities in Japan, and 983 respondents completed our survey. The questionnaires covered various personal characteristics, lifestyle habits, perceived health factors, perceived athletic activity, and EHE-related symptoms. In this retrospective case–control study, we analyzed the relationships of EHE-related symptoms (objective variables) with lifestyle habits, health factors, and athletic activity conditions (explanatory variables) using the partial proportional odds model. The questionnaire responses revealed that perceived dehydration, sickness, loss of appetite, perceived accumulated fatigue, perceived mental stress, lack of ambient wind, and insufficient rest breaks were positively associated with EHE-related symptoms, with relative risks ranging from high to low. Using an air conditioner during sleep and having a sleep duration of ≥ 6 h were associated with a reduced risk of EHE-related symptoms. The study results suggest that female athletes should be allowed to postpone exercise or reduce its intensity and volume based on their perceptions of dehydration, sickness, loss of appetite, accumulated fatigue, and mental stress in summer to prevent heat-related illnesses. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

In this initial study of a research project, this paper seeks to understand the thermal conditions in the cities of Lisbon and Munich, specifically focusing on Urban Heat Island intensity and on thermal comfort using the Universal Thermal Climate Index modeling data at the Local Climate Zone scale. Based on these datasets, Munich has exhibited more unfavourable thermal conditions than Lisbon. In terms of UHII, both cities have shown that low, medium, and high rise compact urban areas and bare rock or paved areas have the highest values, while sparsely built areas have the lowest. These results differ from the UTCI, which indicates that in Lisbon and Munich, these sparsely built areas as well as areas with low plants and vegetation are the most uncomfortable. In Munich, the population was exposed to very strong heat stress, while Lisbon experienced strong heat stress conditions. Conversely, low, medium, and high rise compact urban areas and densely wooded areas in Munich, and scattered trees areas and large low-rise urban areas in Lisbon, have demonstrated the lowest monthly mean and average maximum values. These results will be further explored in future studies in the city of Lisbon and cross-checked with data obtained from roving missions. This will enable a more detailed temporal and local analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of golfers' performance to meteorological conditions at the men's US Masters tournament over the 40-year period 1980–2019. The mean and standard deviation of round scores are related to local temperature, humidity, wind speed and direction, and concurrent and antecedent precipitation. Mean scores are more dependent on weather conditions than the variability of scores in a given round. The best predictor of mean scores is the wet-bulb temperature in rounds one and two, and the zonal wind speed in rounds three and four. Across both sets of rounds (1 and 2, and 3 and 4), the wet-bulb temperature is a better predictor of mean scores than the air temperature, which implies that atmospheric moisture content affects scores. In general, golfers take fewer shots and so perform better in warmer and calmer conditions. The synergestic effect of several weather variables explains over 44% of the variance in mean scores. Mean meteorological conditions during play are a much better predictor of the players' average performance than the standard deviation of the weather variables. The golfers' performance becomes more variable in cooler conditions with a wider range of scores. Precipitation during play and the dampness of the ground (as quantified by rainfall up to ten days before play) do not have a consistent and statistically significant effect on the competitors' performance. In short, this paper demonstrates that golf scores are dependent on weather conditions. [ABSTRACT FROM AUTHOR]

Ambulance data has been reported to be a sensitive indicator of health service use during hot days, but there is no comprehensive summary of the quantitative association between heat and ambulance dispatches. We conducted a systematic review and meta-analysis to retrieve and synthesise evidence published up to 31 August 2022 about the association between heat, prolonged heat (i.e. heatwaves), and the risk of ambulance dispatches. We initially identified 3628 peer-reviewed papers and included 48 papers which satisfied the inclusion criteria. The meta-analyses showed that, for each 5 °C increase in mean temperature, the risk of ambulance dispatches for all causes and for cardiovascular diseases increased by 7% (95% confidence interval (CI): 5%, 10%) and 2% (95% CI: 1%, 3%), respectively, but not for respiratory diseases. The risk of ambulance dispatches increased by 6% (95% CI: 4%, 7%), 7% (95% CI: 5%, 9%), and 18% (95% CI: 12%, 23%) under low-intensity, severe, and extreme heatwaves, respectively. We observed two potential sources of bias in the existing literature: (1) bias in temperature exposure measurement; and (2) bias in the ascertainment of ambulance dispatch causes. This review suggests that heat exposure is associated with an increased risk of ambulance dispatches, and there is a dose-response relationship between heatwave intensity and the risk of ambulance dispatches. For future studies assessing the heat-ambulance association, we recommend that (1) using data on spatially refined gridded temperature that is either very well interpolated or derived from satellite imaging may be an alternative to reduce exposure measurement bias; and (2) linking ambulance data with hospital admission data can be useful to improve health outcome classification. [ABSTRACT FROM AUTHOR]

Urban development creates several inadvertent impacts related to urban climate and human biometeorology. Monitoring systems based on microcontrollers are slowly emerging as an alternative to conventional devices for monitoring outdoor thermal comfort (OTC), thus overcoming limitations imposed by the high costs of commercially available equipment. This review was conducted using the Scopus database, searching for articles and conference papers according to a pre-defined search string, which included the terms "microcontrollers" and "human thermal comfort" up to 2022. From a total sample of 113 articles, 52 papers met the desired criteria (written in English, published in peer-reviewed journals, and within the given time frame). Results show a growing, yet timid trend of published material on low-cost, open-source technologies for diverse applications in human biometeorology. [ABSTRACT FROM AUTHOR]

In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this paper to represent the thermal comfort of human body. According to the literature, the main environmental factors affecting PMV index are temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, and there is a complex nonlinear relationship between the six variables. Due to the coupling relationship between the six parameters, the PMV formula can be simplified under specific conditions, reducing the monitoring of variables that are difficult to observe. Then, the improved grey system prediction model GM(1,1) with optimized selection dimension is used to predict the future time of PMV. Due to the irregularity, uncertainty and fluctuation of PMV values in time series, based on the original GM(1,1) time series prediction, an adaptive GM(1,1) improved model is proposed, which can continuously change with time series and enhance its prediction accuracy. By contrast, the improved GM(1,1) model can be derived from the sliding window of the adaptive model through changes in the dataset and get better model grades. It lays a foundation for the future research on the predicted index of PMV, so as to set and control the air conditioning system in advance, to meet the intelligence of modern intelligent home and humanized function of sensing human comfort. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses. [ABSTRACT FROM AUTHOR]

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks. [ABSTRACT FROM AUTHOR]

Since pedestrians are impacted by solar radiation differently, urban designers must evaluate solar radiation exposure of pedestrian paths adopting an inclusive approach. This paper proposes a maximum threshold of direct solar radiation exposure for pedestrians based on activity, user profile and environmental conditions, defined as the difference between the energy consumption before feeling exhausted and the energy cost of walking. Two users of diverse walking abilities, a young adult and an elderly person with mobility impairment, were characterised by metabolic activity, walking speed and maximum energy capacity. Based on the theoretical framework, the energy budget of young adults to cope with thermal stress was set as three times higher than for the elderly. This framework was used to quantify the contribution of direct solar radiation to energy balance and then classify walkability during clear-sky summer hours; the term 'walkable' referred to environmental conditions allowing users to walk without feeling exhausted. The methodology was tested on an open area and an urban canyon in Milan; applicability by urban designers was key in developing a simplified way to evaluate shading needs. This approach could be applied to evaluate solar radiation exposure of pedestrian paths adopting diverse user experiences as an evaluation criterion. [ABSTRACT FROM AUTHOR]

The importance of spa therapy is growing worldwide, with care practitioners paying more attention to how this intervention can alleviate various health issues. Multiple studies have been performed and reported on the efficiency of balneotherapy, creating a need to understand the quality of the information in the existing studies and how they can inform practitioners in promoting evidence-based practice. The SPAC assessment tool describes and assesses the quality of reports of interventional trials in balneotherapy. The present study had two goals. The first goal was the translation and cross-cultural adaptation of the SPAC assessment tool in Greek language following an internationally accepted methodology. The second goal was to assess the reliability of the tool employing test–retest and interrater reliability studies in a sample of 15 relative RCT papers and to assess the criterion validity of the Greek version by comparing its results with the original English version when assessing the same 15 papers. The results revealed that the SPAC checklist was translated and adapted in Greek without difficulties between the translators. Reliability of the SPAC was found excellent in both test–retest (ICC =.98) and interrater (ICC =.95) indexes. The criterion validity assessment for Greek version of SPAC checklist was also almost perfect (ICC =.98). In the present study, the Greek version of the SPAC checklist was found to be a comprehensible, reliable, and valid tool to assess the quality of interventional trial reports on the efficacy of spa therapy for cure and health enhancement. [ABSTRACT FROM AUTHOR]

Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (Tmean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking. [ABSTRACT FROM AUTHOR]

The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project's needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology. [ABSTRACT FROM AUTHOR]

This systematic review aims to give an overview of the diversity of research areas related to human biometeorology in Brazil. The main focus of this paper addresses research trends, represented by published papers with national and international authorship, main contributions and shortcomings, as well as challenges and prospects of research in this area of study. An extensive literature search was conducted in the Scopus, Web of Science, and Science Direct databases so as to identify relevant publication output up to July 2021 related to the research area. The screening resulted in 96 studies chosen for full-text reading. Overall, results indicated a reduced amount of articles on the subject matter published internationally, with noticeable gaps in research in some regions of the country, such as the Amazon region and in the Brazilian Midwest region. Research gaps in relevant areas have been identified with limited output in the climate dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers. Such gaps should further encourage researchers to engage in research focused on those areas. [ABSTRACT FROM AUTHOR]

The objective of this study is to determine the impacts of low-intensity heat on human health in regions with hot, humid summers. Current literature has highlighted an increase in mortality and morbidity rates during significant heat events. While the impacts on high-intensity events are established, the impacts on low-intensity events, particularly in regions with hot, humid summers, are less clear. A scoping review was conducted searching three databases (PubMed, EMBASE, Web of Science) using key terms based on the inclusion criteria. We included papers that investigated the direct human health impacts of low-intensity heat events (single day or heatwaves) in regions with hot, humid summers in middle- and high-income countries. We excluded papers written in languages other than English. Of the 600 publications identified, 33 met the inclusion criteria. Findings suggest that low-intensity heatwaves can increase all-cause non-accidental, cardiovascular-, respiratory- and diabetes-related mortality, in regions experiencing hot, humid summers. Impacts of low-intensity heatwaves on morbidity are less clear, with research predominantly focusing on hospitalisation rates with a range of outcomes. Few studies investigating the impact of low-intensity heat events on emergency department presentations and ambulance dispatches were found. However, the data from a limited number of studies suggest that both of these outcome measures increase during low-intensity heat events. Low-intensity heat events may increase mortality. There is insufficient evidence of a causal effect of low-intensity heat events on increasing morbidity for a firm conclusion. Further research on the impact of low-intensity heat on morbidity and mortality using consistent parameters is warranted. [ABSTRACT FROM AUTHOR]

The study aims to present reliable information about thermal conditions and their impacts on visitors to ski travel destinations. Mountain tourism areas are specific since high altitudes affect the ambient weather conditions which can affect different types of human activities. In this paper, the thermal comfort and its changes in Kopaonik Mountain, the most popular ski resort in Serbia over the last 30 years, have been evaluated. Information about thermal comfort is presented by using the Universal Thermal Climate Index (UTCI), physiologically equivalent temperature (PET), and modified physiologically equivalent temperature (mPET) in 3-h resolution for the period 1991–2020. The results indicate prevailing cold stress all year round. Days with moderate, strong, and very strong heat stress were not recorded. Strong and extreme cold stress prevailed during winter, while slight and moderate cold stress prevailed during summer. Transitional seasons were very cold, but autumn was more comfortable than spring. The occurrence of days with neutral and slightly warm/cool conditions is concentrated in the summer months. However, summer is not used enough for tourism because the choice of tourists to stay at Kopaonik is not primarily based on favorable bioclimatic conditions, but on resources for winter tourism. With global warming, the annual number of thermally favorable days has been increasing, while the number of days with extreme and strong cold stress is decreasing. Continuing this trend can significantly influence tourism in the future, and therefore, new strategies in ski resorts will be required to adapt to the changing climate. [ABSTRACT FROM AUTHOR]

The intensity and frequency of climate extremes such as heat waves, droughts and extreme rainfall events are projected to rise. This will increase the severity of their impacts across socio-ecological systems. Economic sectors such as nature-based tourism become more vulnerable because of their reliance on climate and natural capital as key resources. While attempts have been made to understand how climate change may impact tourists and the industry itself, little is known about the same on tourism-dependent communities. This paper determines the extent to which tourism-dependent communities are vulnerable to climate change in the Okavango Delta, Botswana, to enhance their wider livelihood the development of strategies for improving adaptive capacity, resilience, and reduced exposure sensitivities. A household survey of 172 households was conducted in three purposively selected villages of Mababe, Sankuyo and Khwai, actively involved in community-based tourism for their socio-economic development. Information sourced related to livelihood options, peoples’ resilience, local risks, and hazards. The data was analysed using descriptive statistics and thematic analysis. The results indicate that respondents have observed climatic changes over the years such as increased temperatures, decreased rainfall, and increased frequencies of extreme events. The respondents attributed changes in natural capital to these observed climatic conditions in the form of desiccation, dwindling populations of some wildlife species, decreased fish stocks and reduced vegetation cover. This renders the tourism-dependent communities vulnerable as their livelihood is threatened. The paper thus concludes that climate change adaptation is an urgent priority for local communities who are already exposed to existing climatic and non-climatic stresses. [ABSTRACT FROM AUTHOR]

Aedesalbopictus (Diptera: Culicidae) is a mosquito from Asia that can transmit a variety of diseases. This paper aimed to explore the effects of temperature, relative humidity, and illumination on the entomological parameters related to the population growth of Aedes albopictus, and provide specific parameters for developing dynamic models of mosquito-borne infectious disease. We used artificial simulation lab experiments, and set 27 different meteorological conditions to observe and record mosquito's hatching time, emergence time, longevity of adult females, and oviposition amount. We then applied generalized additive model (GAM) and polynomial regression to formulate the effects of temperature, relative humidity, and illumination on the biological characteristics of Aedes albopictus. Our results showed that hatchability closely related to temperature and illumination. The immature stage and the survival time of adult female mosquitoes were associated with temperature and relative humidity. The oviposition rate related to temperature, relative humidity, and illumination. Under the control of relative humidity and illumination, ecological characteristics of mosquitoes such as hatching rate, transition rate, longevity, and oviposition rate had an inverted J shape with temperature, and the thresholds were 31.2 °C, 32.1 °C, 17.7 °C, and 25.7 °C, respectively. The parameter expressions of Aedes albopictus using meteorological factors as predictors under different stages were established. Meteorological factors especially temperature significantly influence the development of Aedes albopictus under different physiological stages. The established formulas of ecological parameters can provide important information for modeling mosquito-borne infectious diseases. [ABSTRACT FROM AUTHOR]

Context: Documenting trends in the health impacts of ambient temperature is key to supporting adaptation strategies to climate change. This paper explores changes in the temperature-related mortality in 18 French urban centers between 1970 and 2015. Method: A multicentric time-series design with time-varying distributed lag nonlinear models was adopted to model the shape of the relationship and assess temporal changes in risks and impacts. Results: The general shape of the temperature-mortality relationship did not change over time, except for an increasing risk at very low percentiles and a decreasing risk at very high percentiles. The relative risk at the 99.9th percentile compared to the 50th percentile of the 1970–2015 temperature distribution decreased from 2.33 [95% confidence interval (CI): 1.95:2.79] in 1975 to 1.33 [95% CI: 1.14:1.55] in 2015. Between 1970 and 2015, 302,456 [95% CI: 292,723:311,392] deaths were attributable to non-optimal temperatures, corresponding to 5.5% [95% CI: 5.3:5.6] of total mortality. This burden decreased progressively, representing 7.2% [95% CI: 6.7:7.7] of total mortality in the 1970s to 3.4% [95% CI: 3.2:3.6] in the 2000s. However, the contribution of hot temperatures to this burden (higher than the 90th percentile) increased. Discussion: Despite the decreasing relative risk, the fraction of mortality attributable to extreme heat increased between 1970 and 2015, thus highlighting the need for proactive adaptation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate associations between lifestyle habits, health factors, athletic activity conditions, and exertional heat exhaustion (EHE)–related symptoms among male college athletes in Japan based on a self-completed questionnaire. The paper-based questionnaires were distributed to 11 universities in Japan, and 2006 respondents completed the survey. Data of personal characteristics (age, body mass), lifestyle habits (sleep duration, use of air-conditioner while sleeping, and practice duration), perceived health factors (loss of appetite, sleep deprivation, sickness, dehydration, accumulated fatigue, and mental stress), perceived athletic activity (insufficient rest breaks, high ambient temperature, excessive humidity, strong solar radiation, lack of ambient wind, and clothing discomfort), and EHE-related symptoms (dizziness, headaches, nausea, and malaise) were collected. The association between lifestyle habits, health factors, athletic activity conditions (explanatory variables), and EHE-related symptoms (objective variables) was analyzed using the partial-proportional odds model. "Perceived dehydration" (odds ratios [ORs] 1.70–2.31, p < 0.002)," "sickness" (ORs 1.35–1.76), p < 0.001), "perceived accumulated fatigue" (ORs 1.13–1.31, p ≤ 0.001), "perceived mental stress" (ORs 1.17–1.31, p < 0.019), "lack of ambient wind" (ORs 1.12–1.19, p < 0.022), "loss of appetite" (ORs 1.16–1.23, p < 0.037), and "sleep deprivation" (ORs 1.15–1.17, p < 0.025) were positively associated with EHE-related symptoms, whereas "using an air conditioner during sleeping" (ORs 0.91, p = 0.047) during summer seasons was negatively associated. These findings suggest that athletes should be allowed to postpone or downregulate exercise intensity and/or volume based on their perceptions of dehydration, sickness, accumulated fatigue, mental stress, loss of appetite, and/or sleep deprivation in the summer to prevent heat illness. [ABSTRACT FROM AUTHOR]

Tropical inland capture fisheries are susceptible to a series of vulnerabilities such as habitat destruction, biodiversity loss, pollution, overfishing, invasive species and anthropogenic climate change. A comprehensive review of the impact of climatic uncertainties on Indian inland fisheries has not been adequately attempted yet. Recent approaches emphasizing ecosystem-based management in a regional context, specific to inland fisheries for combating climatic changes, have not been reported to date. The paper presents a critical bibliometric review of the climatic vulnerabilities faced by Indian inland fishery resources and various adaptive and mitigation strategies put forward by the country for the sustainability of the resources. In this communication, a systematic review of the impact of climate change and other stressors on various inland ecosystems of the subcontinent and the ecosystem-based management strategies adopted in India is presented and discussed. [ABSTRACT FROM AUTHOR]

This paper describes a newly developed software tool to evaluate human thermal safety and thermal comfort in cold-weather activities aimed at guiding users to arrange activity plans and select appropriate clothing ensembles. The software inputs include conditions of activity, environment, human body, and clothing ensemble. It outputs physiological temperatures, cold injury risks, thermal sensations, and thermal comforts in intuitive ways like cloud maps and curves. The software tool is characterized by (1) integration of a thermoregulatory model that predicts human thermophysiological responses under exercise conditions in cold environments, (2) the functions of clothing ensemble database and individual parameter database, (3) the human centric outputs that directly reflect human physiological and mental status, and (4) the user-friendly operation interface and output interface, as well as a wide applicability. The software is validated with human test studies covering ambient temperatures from − 30.6 to 5 °C, clothing ensembles from 1.34 to 3.20 clo, and activity intensities from 2 to 9 Mets. The average prediction RMSEs of core temperature, mean skin temperature, thermal sensation, and thermal comfort are 0.16 °C, 0.45 °C, 0.58, and 1.41, respectively. The software is an advanced expansion to current standards and guidance of cold exposure assessment and a meaningful tool for the fields of occupational health care, cold protection, and environmental ergonomics. [ABSTRACT FROM AUTHOR]

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models. [ABSTRACT FROM AUTHOR]

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize. [ABSTRACT FROM AUTHOR]

Leptospirosis, the infectious disease caused by a spirochete bacteria, is a major public health problem worldwide. In Argentina, some regions have climatic and geographical characteristics that favor the habitat of bacteria of the Leptospira genus, whose survival strongly depends on climatic factors, enhanced by climate change, which increase the problems associated with people's health. In order to have a method to predict leptospirosis cases, in this paper, five time series forecasting methods are compared: two parametric (autoregressive integrated moving average and an alternative one that allows covariates, ARIMA and ARIMAX, respectively), two nonparametric (Nadaraya-Watson Kernel estimator, one and two kernels versions, NW-1 K and NW-2 K), and one semiparametric (semi-functional partial linear regression, SFPLR) method. For this, the number of cases of leptospirosis registered from 2009 to 2020 in three important cities of northeastern Argentina is used, as well as hydroclimatic covariates related to the presence of cases. According to the obtained results, there is no method that improves considerably the rest and can be recommended as a unique tool for leptospirosis prediction. However, in general, the NW-2 K method gets a better performance. This work, in addition to using a long-term high-quality time series, enriches the area of applications of statistical models to epidemiological leptospirosis data by the incorporation of hydroclimatic variables, and it is recommended directing further efforts in this line of research, under the context of current climate change. [ABSTRACT FROM AUTHOR]

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm. [ABSTRACT FROM AUTHOR]

High altitudes have been exposed to enhanced levels of surface ozone (O3) concentrations over recent decades compared to the pre-industrial era. The responses of vegetation to this toxic pollutant are species-specific and depend on the climate conditions. In this paper, we explored the reaction of Pinus mugo (P. mugo) to O3-induced stress in the continental climate of an ozone-rich mountain area in the High Tatra Mountains (Western Carpathians). The effects of O3 doses modelled by a deposition model, O3 concentrations and other factors on P. mugo were identified from (a) satellite-based data via NDVI (normalised differenced vegetation index) over 2000–2020 and (b) visible injury on needle samples gathered from P. mugo individuals at ground-truth sites in 2019 and 2020. Analysing the NDVI trend, we observed non-significant changes (p > 0.05) in the greenness of P. mugo despite growing in an environment with the average seasonal O3 concentration around 51.6 ppbv, the maximum hourly concentrations more than 90 ppbv and increasing trend of O3 doses by 0.1 mmol m−2 PLA (plant leaf area) year−1. The visible O3 injury of samples collected at study sites was low (mean injury observed on 1–10% of needles' surface), and the symptoms of injury caused by other biotic and abiotic factors prevailed over those caused by O3. In addition, the correlation analyses between NDVI and the climatic factors indicated a significant (p < 0.05) and positive relationship with photosynthetic active radiation (R = 0.45) in July, and with stomatal conductance (R = 0.52) and temperature factor (R = 0.43) in August. Therefore, we concluded that the positive effect of climate conditions, which support the growth processes of P. mugo, may suppress the negative effect of the mean O3 doses of 17.8 mmol m−2 PLA accumulated over the growing season. [ABSTRACT FROM AUTHOR]

Biometeorology research continues to grow and accelerate in terms of productivity (papers produced, studies conducted, etc.) as well as its direct impact on society and policy. Simultaneously, the scientific community is increasingly acknowledging that research has predominantly focused on the Global North. Additionally, work conducted in the Global South often follows extractive practices that primarily advance the careers and scientific knowledge of researchers from the Global North, offering minimal benefit to the communities studied in the Global South. This short communication intends to serve as a call to the biometeorology community to work collaboratively across continents to understand the current knowledge of biometeorology research in the Global South in addition to identifying the gaps, challenges, and opportunities of conducting grounded research in the Global South led by Global South researchers to support societies equitably. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is growing concern over the increased risk of heat stress induced by the changing climate, with increased global temperatures expected to exacerbate conditions. Heat stress in school sports is a critically understudied research area in South Africa (SA). As demonstrated by several studies conducted in the global north, the stress elicited by meteorological conditions on students participating in physical education lessons and after-school sports activities could have serious consequences on their health. This paper represents the first research in the region to investigate the risk of heat stress to school students during physical activity. Meteorological data were collected at a school in Johannesburg between March and July, which encompasses the seasons of late summer, autumn, and winter. These were inputted into four heat stress indices: Humidex, Heat Index, Wet Bulb Globe Temperature, and the Universal Thermal Climate Index, together with the measured Wet Bulb Temperature to determine the level of risk of heat stress during physical education lessons and after-school sports activities between March and July. Of the 2700 index scores calculated over this period, 56% indicate some level of heat stress, with almost 6% indicating a high and very high level of risk. Heat stress is calculated to be most likely to occur between 11h00-15h00, and the danger of pre-season training, which takes place in the summer months, is demonstrated by these results. Additionally, this research finds that heat stress is far more likely on artificial surfaces than on natural grass. The findings of this research intimate that school stakeholders should consider the implications of heat stress when considering timetabling and policy-making in the interests of safeguarding their students. [ABSTRACT FROM AUTHOR]

Human comfort outdoors is widely investigated, but most studies explore the comfort domains singularly. This paper aimed to evaluate human comfort in parks, verifying the importance of using a multi-domain (simultaneously evaluating thermal, visual, acoustic, and air quality) and multi-disciplinary (combining environmental and social fields) approach. A walk through a pre-defined path from one park to another was repeated twice per day on four consecutive days in June, with three participants per walk. The two investigated parks are in central Italy and were chosen because they differ in their design and spatial characteristics. Environmental data were recorded with an innovative wearable device during the whole walk, and surveys were used to assess people's perceptions of the parks. Despite observed differences in collected physical parameters, the survey's responses were similar, and different comfort domains showed dependence on each other in the two parks. Logistic regression models were developed for each park, and they revealed that the qualitative information predicted the overall comfort level more accurately than the environmental data. In detail, the models based on environmental data resulted in R2 equal to 0.126 and 0.111 in Parks 1 and 2, respectively, whereas using the survey answers increased it up to 0.820 (Park 1) and 0.806 (Park 2). This study contributes to addressing the gap in multi-domain comfort studies outdoors and confirms the importance of using multi-disciplinary and multi-domain approaches for a complete comfort analysis, supporting holistic human-biometeorology-oriented models and forecasting opportunities that can promote improvements in urban environmental quality and liveability. [ABSTRACT FROM AUTHOR]

Urban dwellers experience overheating due to both global and urban warming. The rapid urbanisation, especially in hot, humid cities, lead to greater exposure to heat risk, both due to increasing urban populations as well as overheating due to global/urban warming. However, a nation-wide exploration of thermal comfort trends, especially in the hot, humid tropics, remains relatively unexplored. In this paper, we explore the recent historical trends (1991–2020) in outdoor thermal comfort across the entire island of Sri Lanka and evaluate the likely effects of known urban climate mitigation strategies — shade and vegetative cover. We find that 'very strong heat stress' is moving towards 'extreme heat stress' that was barely registered in 1990s and is now common across two-thirds of the landmass of Sri Lanka in the hottest month (April). Even in the coolest month (January), 'moderate heat stress' unknown in the 1990s is now becoming a common trend across the most densely populated parts of the country. High shading and vegetation could reduce heat stress, even in the hottest month, but its utility will diminish as the warming continues in future. As such, policies to reduce global warming needs to be urgently pursued while simultaneously adapting to urban warming in Sri Lanka. [ABSTRACT FROM AUTHOR]

A versatile meteorological index for predicting heat stress in dairy cattle remains elusive. Despite numerous attempts at developing such indices and widespread use of some, there is growing skepticism about the accuracy and adequacy of the existing indices as well as the general statistical approach used to develop them. At the same time, precision farming of high-yielding animals in a drastically changing climate calls for more effective prediction and alleviation of heat stress. The present paper revisits classical work on human biometeorology, particularly the apparent temperature scale, to draw inspiration for advancing research on heat stress in dairy cattle. The importance of a detailed, mechanistic understanding of heat transfer and thermoregulation is demonstrated and reiterated. A model from the literature is used to construct a framework for identifying and characterizing conditions of potential heat stress. New parameters are proposed to translate the heat flux calculations based on heat-balance models into more tangible and more useful meteorological indices, including an apparent temperature for cattle and a thermoregulatory exhaustion index. A validation gap in the literature is identified as the main hindrance to the further development and deployment of heat-balance models. Recommendations are presented for systematically addressing this gap in particular and continuing research within the proposed framework in general. [ABSTRACT FROM AUTHOR]

The dysregulation in heat balance, the main cause of exertional heat stroke, occurs not only in midsummer but also in the cold season. Possible causes of this are a reduction in convection and evaporation due to tailwinds and an acceleration of radiant heat inflow. Although the amount of radiant heat that reaches the surface can be estimated, the actual amount of heat that flows into the body cannot be specified yet. This paper made an experimental attempt at this. A device is made up of a temperature controllable heat sink and heat flow detector, which keeps the surface temperature constant and has a heat exchange coefficient comparable to that of the human body surface. The output of this device (total heat exchange) was divided into radiant heat exchange and other heat exchange using a standard radiant heat calibrator, Leslie cube. A phenomenon, in which a wet surface while the surface temperature was low absorbed larger heat than that of the dry surface, was found. And authors named this "hidden heat inflow". As a result of multiple regression analyses, both radiant heat exchange and other heat exchanges are closely related to the surface temperature, and the maximum difference in total heat exchange during the experiment reached 200 kcal/m2/h. It has been suggested that this phenomenon may also occur on the surface of human skin. One of the causes of this "hidden heat inflow" is considered to be the decrease in evaporative cooling due to the decrease in surface temperature. However, this alone cannot explain all of the phenomena, so water vapor aggregation may also be involved. A "hidden heat inflow" as a sufficient heat source for exertional heat stroke or collapse during a marathon race on a cold day was evidenced experimentally. [ABSTRACT FROM AUTHOR]

Recent studies have characterized individually experienced temperatures or individually experienced heat indices, including new exposure metrics that capture dimensions of exposure intensity, frequency, and duration. Yet, few studies have examined the personal thermal exposure in underrepresented groups, like outdoor workers, and even fewer have assessed corresponding changes in physiologic heat strain. The objective of this paper is to examine a cohort of occupationally exposed grounds and public safety workers (n = 25) to characterize their heat exposure and resulting heat strain. In addition, a secondary aim of this work is to compare individually heat index exposure (IHIE) across exposure metrics, fixed-site in situ weather stations, and raster-derived urban heat island (UHI) measurements in Charleston, SC, a humid coastal climate in the Southeastern USA. A Bland–Altman (BA) analysis was used to assess the level of agreement between the personal IHIE measurements and weather-station heat index (HI) and Urban Heat Island (UHI) measurements. Linear mixed-effect models were used to determine the association between individual risk factors and in situ weather station measurements significantly associated with IHIE measurements. Multivariable stepwise Cox proportional hazard modeling was used to identify the individual and workplace factors associated with time to heat strain in workers. We also examined the non-linear association between heat strain and exposure metrics using generalized additive models. We found significant heterogeneity in IHIE measurements across participants. We observed that time to heat strain was positively associated with a higher IHIE, older age, being male, and among Caucasian workers. Important nonlinear associations between heat strain occurrence and the intensity, frequency, and duration of personal heat metrics were observed. Lastly, our analysis found that IHIE measures were significantly similar for weather station HI, although differences were more pronounced for temperature and relative humidity measurements. Conversely, our IHIE findings were much lower than raster-derived UHI measurements. Real-time monitoring can offer important insights about unfolding temperature-health trends and emerging behaviors during thermal extreme events, which have significant potential to provide situational awareness. [ABSTRACT FROM AUTHOR]

Since the first issue of the International Journal of Biometeorology in 1957, a total of 135 papers have reported on research in or of African countries. The majority of these have been on topics of animal biometeorology (36%), and the greatest proportion (24%) are situated in Nigeria. There has been a considerable increase in papers on African biometeorology since 2011, with those from this past decade accounting for 58% of all African papers in the journal. This occurs concurrent to an increase in the total number of papers published in the journal, driven by a move to the Editorial Manager system. While 66% of the papers on African biometeorology in the journal are authored by at least one person with an affiliation in the African continent, only 15 African countries are represented in the total authorship. As much of the African continent is projected to experience climatic changes exceeding the global mean, as much of the region is involved in animal and plant farming, and as seasonally-fluctuating and climatically affected diseases are common place, this low representation of work in Africa is surprising. This points to the need for greater awareness among African researchers of the discipline of biometeorology, greater involvement of African biometeorologists in International Society of Biometeorology and Commission meetings, and the inclusion of a greater number of African academics in the review process. This would be beneficial to the Society in increasing diversity and encouraging a more cosmopolitan engagement, and to the recognition of scientific development in African countries. [ABSTRACT FROM AUTHOR]

Land cover change (LCC) significantly changed the local/regional temperature. This paper attempts to reveal the effects of cropland expansion in different ways on temperature change from the 1900s to 2010s in Heilongjiang Province. To reach this goal, we conducted four simulation research schemes with the coupled Weather Research and Forecast (WRF)-Noah model to investigate the warming/cooling effect of cropland expansion. The results show that cropland expansion exerted different effects with different land-use type conversions. In the last century, the areas with grassland-to-cropland and wetland-to-cropland transition show the warming effect, and the average surface temperature in Heilongjiang Province increased by 0.023 ℃ and 0.024 ℃, respectively. The areas with forest-to-cropland transition show the cooling effect, in which the average temperature decreased by 0.103 ℃. The variation of air temperature is mainly caused by the variation of surface reflectance and surface net radiation flux. The results provide evidence that cropland expansion changes to biophysical landscape characteristics, warming/cooling the land surface and thus enhancing/reducing the temperature, and lead to regional climate change eventually. [ABSTRACT FROM AUTHOR]

Under-five years old acute watery diarrhea (U5AWD) accounts for most diarrheal diseases' burden, but little is known about the adjusted effect of meteorological and socioeconomic determinants. A dataset containing the seasonal numbers of U5AWD cases at the district level of Iran is collected through MOHME. Accordingly, the district-level standardized incidence ratio and Moran's I values are calculated to detect the significant clusters of U5AWD over sixteen seasons from 2014 to 2018. Additionally, the author tested twelve Bayesian hierarchical models in order to determine which one was the most accurate at forecasting seasonal number of incidents. Iran features a number of U5AWD hotspots, particularly in the southeast. An extended spatiotemporal model with seasonally varying coefficients and space–time interaction outperformed other models, and so became the paper's proposal in modeling U5AWD. Temperature demonstrated a global positive connection with seasonal U5AWD in districts (IRR: 1.0497; 95% CrI: 1.0254–1.0748), owing to its varying effects during the winter ((IRR: 1.0877; 95% CrI: 1.0408–1.1375) and fall (IRR: 1.0866; 95% CrI: 1.0405–1.1357) seasons. Also, elevation (IRR: 0.9997; 95% CrI: 0.9996–0.9998), piped drinking water (IRR: 0.9948; 95% CrI: 0.9933–0.9964), public sewerage network (IRR: 0.9965; 95% CrI: 0.9938–0.9992), years of schooling (IRR: 0.9649; 95% CrI: 0.944–0.9862), infrastructure-to-household size ratio (IRR: 0.9903; 95% CrI: 0.986–0.9946), wealth index (IRR: 0.9502; 95% CrI: 0.9231–0.9781), and urbanization (IRR: 0.9919; 95% CrI: 0.9893–0.9944) of districts were negatively associated with seasonal U5AWD incidence. Strategically, developing geoinformation alarm systems based on meteorological data might help predict U5AWD high-risk areas. The study also anticipates increased rates of U5AWD in districts with poor sanitation and socioeconomic level. Therefore, governments should take appropriate preventative actions in these sectors. [ABSTRACT FROM AUTHOR]

This paper aims at extending earlier models of outdoor thermal perception by fusing new knowledge from recent literature and deriving perspectives for future research and methods from the new model. Previous models focused on physical and physiological aspects. Only recently, the psychological aspects of thermal perception received more attention, such as spatial perception. Furthermore, in recent literature on thermal perception, two time scales have been described: the short-term and the long-term thermal perception. Based on this new literature, we develop a conceptual, more comprehensive model that takes these factors into account as well. It hypothesizes how thermal sensation and psychological processes interact on the two time scales. However, to be able to describe relationships between psychological aspects of thermal perception more precisely, more research is required on the following issues: (1) influence of momentary personal references and preferences (e.g., mood), (2) influence of long-term personal references and preferences (e.g., cultural aspects, habituation), and (3) influence of the perception of the spatial environment on thermal perception. Moreover, the relation between momentary and long-term thermal perception has not been studied yet. We conclude this paper with an outlook on possible methods to study these factors. [ABSTRACT FROM AUTHOR]

Religious spaces are an integral part of Indian cities. Unique in their spatiality, they function as socio-cultural hubs drawing users from varied economic and social hierarchies. This study deals with physical and perceptional assessments of micrometeorological conditions in two religious squares namely Hanuman Mandir Square (HMS) and Gurudwara Bangla Sahib Square (GBS) located in New Delhi (28.6° N; 77.2° E), India. The study involved real-time physical measurement of environmental variables such as dry-bulb temperature (Ta, °C), globe temperature (Tg, °C), relative humidity (RH), and air velocity (Va). Variables such as physiological effective temperature (PET), universal thermal comfort index (UTCI), and mean radiant temperature (Tmrt, °C) were computed from measured variables. Concurrent thermal comfort surveys were carried out with 353 respondents in both the squares. The paper describes the thermal characteristics of the studied squares and presents the associated subjective thermal response and preferences of the users. PET was found to correlate well with the subjective responses. The neutral value of PET is found to be 24.7 °C. The neutral PET value of respondents visiting for non-worship purposes was found to be 2.7 °C lesser than those visiting for worship purposes. People visiting the squares for non-worship purpose however were found to be more tolerant of higher PET conditions as compared to others. Factors such as intent of visit, solar exposure, thermal history, and landscape elements were found to have a statistically significant influence on the thermal perception. The paper further summarizes the adaptive opportunities preferred by the users in order to improve thermal comfort in the studied squares. A weighted ranking of adaptive preferences reported by the respondents has also been presented. [ABSTRACT FROM AUTHOR]

Animal biometeorology (insects excluded) has been part of the International Journal of Biometeorology since its inception in 1958. Over the first 60 years of the journal, 480 animal biometeorology papers were published. Thus, approximately 14% of published papers dealt with animals. Over the first 60 years, data from more than 50 animal species was presented, with the lead authors coming from 48 countries. The two most common species used in animal papers between 1957 and 2016 were cattle (109 papers: 22.7% of all animal papers) and rats (96 papers: 20.0% of all animal papers). Although cattle and rats dominated, the species in the most cited paper (240 citations) was chickens, followed by bird migration (155 citations), and general livestock (118 citations). Overall, five papers exceeded 100 citations, and a further two exceeded 200 citations. In the last decade, 126 animal papers were published (26% of all animal papers). Many of these papers had a focus on livestock production in developing countries especially Brazil. [ABSTRACT FROM AUTHOR]

Mud therapy is a safe, cost-effective approach for Knee Osteoarthritis (OA), promoting healing through warmth and minerals, providing pain relief, and improving function. The main objective of the current review is to assess the effectiveness of local mud application for pain management in patients with Knee OA. The published papers were obtained from PubMed, Embase, Scopus, Cochrane Library, ClinicalTrails.gov, Web of Science, and references from relevant systematic reviews from 1990 January to February 2023. Trials meeting the predefined criteria were included in this meta-analysis. The main focus of this study was to assess pain as a primary outcome measure. R software version 4.3.1 and Cochrane Risk of Bias Tool 1 were used to derive meta-analysis and risk of bias in the included studies. A meta-analysis was performed using the fixed-effects model to obtain a summary of treatment effects, which calculated the effect size (standardized mean difference; SMD) along with a 95% Confidence Interval (CI).In the current meta-analysis, a total of ten studies with 560 Knee OA patients were included. Findings demonstrated significant effects of local mud application on pain (SMD: -0.36, 95% CI: -0.56, -0.16), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale (SMD: -0.49, 95% CI: -0.68, -0.29) for individuals suffering from Knee OA. The current study identified compelling evidence to support the recommendation of mud application as an effective intervention for individuals with Knee OA. While local mud application may serve as a supplementary therapy alongside conventional medical approaches for improved Knee OA management, additional high-quality randomized controlled trials are required to validate these findings. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the HEAT (Healthy Environments for AthleTes) project, which aims to understand the impact of environmental conditions on athlete health and performance during major sporting events such as long-distance running, cycling, and triathlons. In collaboration with the SAFER (Strategies to reduce Adverse medical events For the ExerciseR) initiative, the HEAT project carried out a field campaign at the 2022 Comrades Marathon in the KwaZulu-Natal province of South Africa. The measurement campaign deployed seven weather stations, seven PM2.5 monitors and one spore trap along the 90 km route to capture spatially representative measurements of complex micro-climates, allergenic aerospora, and particulate matter exposure. The results indicate that runners were exposed to moderate risk heat stress conditions. Novel findings from this initial campaign shows elevated and potentially harmful PM2.5 levels at spectator areas, possibly coinciding with small fire events around the race day festivities. Our findings show values PM2.5 levels over the WHO 24-h guidelines at all stations, while 2000 µg/m3 at two stations. However, the lack of an acute exposure standard means direct health impacts cannot be quantified in the context of a sport event. The HEAT project highlights important aspects of race day monitoring; regional scale climatology has an impact on the race day conditions, the microclimatic conditions (pollution and meteorology) are not necessarily captured by proximity instruments and direct environmental measurements are required to accurately capture conditions along the route. [ABSTRACT FROM AUTHOR]

Timber extraction is often cited as detrimental to wildlife ecology. Little information, however, in particular from the Southeast Asian tropics, is available on how exactly logging affects wildlife food security. To address the gap, this paper presents the first high-resolution comparison of fruit production between logged and intact forests in lowland Borneo. In the period of 2004–2008, dry weight of fruit litter was assessed as a proxy for food security of wildlife. The pheno-phases of 1,054 trees in 14 sampling plots were monitored for 54 months. A total of 143,184 fruits from 50 tree families were collected from six sampling transects totalling 810 km in 34 months. Surprisingly, logged forest (mean = 23.3 kg ha−1, SD = 48.9) produced more fruit litter than intact forest (mean = 16.7 kg ha−1, SD = 23.3), although the difference is not significant based on Student's t test; t(66) = 0.702, p = 0.485. Pheno-phases could not be entirely explained by rainfall and temperature variables. Some evidence, however, indicates tree species composition, stand structure and sunlight exposure were likely determinants of flowering and fruit litter intensity. All things being equal, results imply selective logging if considerately practiced may increase food security for wildlife. The findings, however, should be interpreted with caution since tropical forest phenology and fruit productivity are also driven by a suite of small-scale edaphic attributes and large-scale spatio-temporal meteorological forcing. Although this research deals mainly with Borneo, the principles discussed and insights offered herein are valuable for furthering conversation around sustainable forestry in tropical Asia and elsewhere globally. [ABSTRACT FROM AUTHOR]

Skin cancers are strongly linked to personal exposure to ultraviolet (UV) radiation, yet UV exposure is also the most preventable risk factor. People are exposed to UV rays when they engage in outdoor activities, particularly exercise, which is an important health behavior. Thus, researchers and the general public have shown increasing interest in measuring UV exposure using wearable sensors during outdoor physical activity. However, minimal research exists at the intersection of UV sensors, personal exposure, adaptive behavior due to exposures, and risk of skin damage. Recent years have seen an influx of new consumer-based and research-based UV-sensing technologies with wide-ranging form factors and purposes to fill this research gap, yet a thorough review of available sensors for specific purposes does not exist. Hence, the overall goal of this state-of-the-art review is to synthesize the current academic and gray literature surrounding personal UV-sensing technologies. Peer-reviewed journal articles and "gray literature," such as working papers, manuals, and UV sensor websites, were reviewed, assessing attributes of UV wearables marketed for research use, personal use, or both. Overall, 13 wearable UV sensors are available for personal use and/or research applications. These sensors vary from electronic to photochromic, with large differences in price, data outputs, accuracy, and precision. Recommendations are provided for which sensors are most suitable for various types of research or public use. Notably, the review findings will help guide researchers in future studies assessing UV exposure during physical activity. [ABSTRACT FROM AUTHOR]

Abstract  Birch pollen is highly allergenic. Knowledge of daily variations, atmospheric transport and source areas of birch pollen is important for exposure studies and for warnings to the public, especially for large cities such as London. Our results show that broad-leaved forests with high birch tree densities are located to the south and west of London. Bi-hourly Betula pollen concentrations for all the days included in the study, and for all available days with high birch pollen counts (daily average birch pollen counts >80 grains/m3), show that, on average, there is a peak between 1400 hours and 1600 hours. Back-trajectory analysis showed that, on days with high birch pollen counts (n = 60), 80% of air masses arriving at the time of peak diurnal birch pollen count approached North London from the south in a 180 degree arc from due east to due west. Detailed investigations of three Betula pollen episodes, with distinctly different diurnal patterns compared to the mean daily cycle, were used to illustrate how night-time maxima (2200–0400 hours) in Betula pollen counts could be the result of transport from distant sources or long transport times caused by slow moving air masses. We conclude that the Betula pollen recorded in North London could originate from sources found to the west and south of the city and not just trees within London itself. Possible sources outside the city include Continental Europe and the Betula trees within the broad-leaved forests of Southern England. [ABSTRACT FROM AUTHOR]