International Journal of Biometeorology最新文献

People experiencing unsheltered homelessness (PEUH) are disproportionately vulnerable to extreme temperatures. This study measured overnight exposure to hot and cold temperatures inside tents occupied by PEUH in Knoxville, Tennessee, and examined participants’ coping strategies through semi-structured interviews (n = 20). In winter, many participants successfully warmed their tents by 10 °C or more, by burning materials and using layers of blankets and tarps. Winter warmth required an abundance of resources, making moving to shelters infeasible, as it meant abandoning these hard-won supplies. Tents quickly dropped in temperature after burning materials, forcing inhabitants to decide between sleep or warmth. In summer, tent location was crucial for access to shade and water, but options were limited by policing and displacement. Shady spots helped keep tents from warming beyond the ambient temperature during the day, but participants avoided going inside them due to being uncomfortably hot, likely due to lack of air flow. When they returned to their tent to sleep, trapped heat caused tents to be up to 5 °C warmer than ambient temperatures. Some tent strategies allowed for better ventilation on hot nights. Overall, tents reduced winter cold exposure but exacerbated summer heat exposure. Although not the focus of this study, participants repeatedly described the damaging effects of rain and flooding, which destroyed belongings and deepened feelings of helplessness. These challenges compounded existing precarity, including chronic health conditions and frequent sweeps. Findings underscore the partial effectiveness of behavioral and material coping strategies and show how the criminalization of homelessness and restrictions on movement increase PEUH’s exposure to extreme weather and related health risks.

Chilling is an important cue in the spring phenology of boreal and temperate tree species. It is well established that increased chilling reduces the days to bud break (DTB), but the effectiveness of different cold temperatures for chilling accumulation remains unknown for most species. Depending on this effectiveness, future warmer winters could either reduce or increase chilling accumulation for different tree species, resulting in delayed or advanced bud break. This could alter primary productivity and ecological interactions. We investigated chilling effects on DTB experimentally, using twigs of boreal and temperate tree species in Minnesota, USA. (8 species), and Bavaria, Germany (6 species). We collected twigs and applied artificial chilling in cooling chambers at three different temperatures (-7/-6.5 °C; 1.5/2°C; 4.5/4°C - USA/Germany) and with two different lengths (4/8 weeks), before placing them into forcing chambers at 21 °C/16°C, 16 h photoperiod. We additionally took twigs from the same locations on three different dates and immediately placed them into the forcing chambers. In both experiments, we observed DTB. Both experiments showed that longer chilling exposure reduced DTB and that temperatures below freezing contributed to chilling accumulation. Changing the chilling temperatures had a significant effect on DTB for 8 out of the 14 species. For most species, higher chilling temperatures more effectively reduced DTB than colder temperatures. With few exceptions, species growing in Germany required less chilling, and boreal species broke bud before temperate species. Our study confirms the need to understand species-specific chilling requirements since generalizations seem inappropriate when predicting future leaf out.

Visual perception significantly influences human thermal comfort. However, few studies have examined its role in predicting thermal comfort, especially among university students undergoing outdoor military training. This study investigates the outdoor thermal comfort of such students and evaluates the performance of different algorithms and features in predicting their thermal comfort. A questionnaire survey was conducted to investigate the thermal and visual perceptions of freshmen at Tianjin Chengjian University during their military training, yielding 1,754 valid responses. Microclimatic conditions were monitored continuously throughout the study. The collected data were then used to develop thermal comfort prediction models based on eight machine learning algorithms. The results demonstrate that models utilizing the visual dataset achieved superior predictive performance. Notably, the thermal acceptability model attained the highest accuracy of 68.2%. Compared to a model using only PET as an input feature with Logistic Regression, this represents a maximum accuracy increase of 34.1%. Furthermore, Bayesian Optimization enhanced the accuracy of the thermal sensation, acceptability, and comfort models by 2.2%, 0.7%, and 2.4%, respectively. Among all features, the visual comfort vote was identified as the most significant in predicting thermal comfort. This study concludes that visual comfort is a critical factor in predicting the thermal comfort of students during military training. The findings provide a scientific basis for the design and planning of outdoor open spaces.

To investigate the effects of Artificial light at night (ALAN) exposure on reproductive hormones and to determine whether there are any lag effects of ALAN exposure on these hormones. There were 971 infertile females available for the current analysis from a prospective cohort. Each female’s baseline address was matched with ALAN imagery. The short-term effects of ALAN on reproductive hormones were analyzed using generalized linear models combined with distributed lag non-linear models. For the long-term effects, the average ALAN level over the 12 months prior to hormone measurement was used as the exposure, and the association between ALAN and reproductive hormones was assessed with multiple linear regression. Short-term exposure to ALAN was associated with increased levels of follicle-stimulating hormone (FSH) and progesterone (P), and a decrease in testosterone (T) levels. The reference value for ALAN was set at the 5th percentile (0.61 nW/cm²/sr). At the 95th percentile of ALAN, FSH levels were reduced with a lag of 3 days to 5 days. At the 75th percentile of ALAN, P levels were reduced with a lag of 0 days to 1 day. For the long-term effects of ALAN on reproductive hormones, a reduction in P concentrations was observed. The results suggest that short-term exposure to ALAN disrupts reproductive hormone levels, with the association becoming non-significant as the number of lag days increases.

Heat stress poses a momentous challenge in poultry production, especially in tropical and subtropical areas, resulting in diminished growth performance, impaired health, and elevated mortality rates. While with the increase in global temperatures, it is imperative to reform effective strategies to alleviate heat stress. Phytase supplementation presents a promising solution, utilizing its enzymatic potential to improve phosphorus availability from plant-derived feed components. This review explores the role of phytase supplementation in alleviating the adverse effects of heat stress in chicken, emphasizing its physiological, metabolic, and immune-modulatory implications. Phytase treatment obviously enhances phosphorus use, facilitates skeletal development, and aids in regulating energy metabolism, essential for maintaining thermoregulation in chicken. Additionally, increased phosphorus bioavailability can enhance bone mineralization, immunological function, and general performance. Besides, phytase supplementation has demonstrated its ability to modulate gut health that can exacerbate heat stress and thus diminishing the hostile effects of heat stress. Recent studies have demonstrated that phytase can beneficially influence physiological parameters, including thermoregulation, feed intake, and water consumption, thereby enhancing poultry welfare during heat stress. This review evaluates the role of phytase in ameliorating adverse impacts of heat stress in chicken and examines the biochemical pathways whereby phytase affects thermal tolerance, including its possible influence on oxidative stress and inflammation.

The present study was undertaken to determine the threshold level of heat stress manifestation on test day milk production traits and to estimate the genetic parameters for effects of heat stress on these traits in Jersey crossbred cows, maintained at the National Dairy Research Institute, Kalyani, West Bengal, India. Test day production traits included milk yield (MY), fat% (FP), fat yield (FY), protein% (PP), protein yield (PY), 4% fat corrected milk yield (4% FCM) and energy corrected milk yield (ECM). The heat stress was assessed by Temperature-Humidity Index (THI), calculated using meteorological information obtained from NASA POWER database. A repeatability test-day reaction norm (RN) model with the heat stress function was used for genetic evaluation of heat stress of animals. The study showed a significant (P < 0.05) decline in all studied traits except for FP per unit increase in heat load in Jersey crossbred cows. Heritability decreased for all the production traits along the THI trajectory, except for PP, for which a U-shaped curve was obtained. However, the permanent environmental effect increased along the THI trajectory with exception in PP, where a decline was observed. The correlations between general additive and heat stress additive were negative for all the production traits and ranged from medium to high (-0.35 to -0.95). The genetic correlation between permanent environmental and heat stress permanent environmental ranged from -0.22 to -0.53. In this study, antagonistic genetic relationship between cows’ ability to produce under thermo-neutral vs. thermo-stress conditions, indicates that cows with higher production potential will tend to have larger decays in production in heat stress condition. Hence, breeding programs should incorporate heat tolerance by using reaction norm models or selecting animals with flatter production declines across rising THI for Jersey crossbred cattle in India.

Understanding how nighttime sleep affects next-day thermal comfort is critical in naturally ventilated (NV) buildings, especially in warm-humid climates where mechanical cooling is absent. This study presents a novel investigation linking previous night’s sleep quality to next-day’s thermal sensation, comfort, and adaptive behavior in NV hostels in eastern India. Field data were collected over two seasons from four NV hostels using ASHRAE Class II measurements and subjective responses, including the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses including correlation, multiple regression and probit modeling were used to quantify the relationships between environmental variables, sleep-quality and comfort. Results revealed significant seasonal variation in operative temperature (22.8 °C winter vs. 31.2 °C summer), clothing insulation (0.68 vs. 0.38 clo), PMV (–0.48 vs. +1.83), and TSV (–0.74 vs. +0.69). Poor sleep (PSQI > 5) was associated with up to 1.5 units extreme TSV, and lower satisfaction and acceptance. PMV overpredicted discomfort compared to TSV, reinforcing the limitations of heat-balance models in NV settings. Thermal neutrality shifted seasonally (T_nG: 24.2 ~ 29.8 °C). This study provides the first empirical evidence that prior sleep quality significantly influences thermal comfort perception, highlighting the need to integrate sleep dynamics into adaptive comfort frameworks.

Wet Bulb Globe Temperature is a heat stress metric that accounts for air temperature, humidity, solar radiation, and wind speed. While methods of WBGT calculation from standard meteorological variables have been developed, these are often only used at coarse spatial scales or at specific points and overlook the underlying land use and land cover that is vitally important to microclimate development. This is particularly important in cities where the urban heat island has significant and spatially disparate impacts on local climate; some areas warm more than others. To address this gap, easily accessible and standard instrumentation was utilized to gather meteorological and environmental factors relevant to WBGT with common mobile measurement methods: walking, cycling, and driving transects. Machine learning methods are used to model and estimate each component of WBGT at a 10-m resolution using underlying land use and land cover data as inputs. The viability, challenges, accuracy, applications, and benefits of this novel mobile WBGT mapping method are assessed. Results indicate that for a sound UHI mapping campaign, a variety of transects should be used in a dense route network as allowed by the total number of citizen science volunteers and the size of the total study area.

Phenological datasets for temperate fruit trees are often short, fragmented and geographically restricted, which hampers the development of cultivar-specific spring phenology models. To address this, we propose a novel calibration approach (“combined-fitting”), which pools observations from several cultivars of the same species, distinguishing between shared and cultivar-specific parameters. This method requires fewer observations per cultivar and allows jointly analyzing cultivars of the same species. We evaluate combined-fitting using the PhenoFlex framework, comparing it to a baseline model and to models that are fitted only with data for single cultivars (“cultivar-fit”). Our analysis is based on flowering data from nine almond, six apricot and six sweet cherry cultivars across Mediterranean (Spain, Morocco, Tunisia) and German climates. The combined-fit model failed to achieve higher prediction accuracy compared to the cultivar-fit and the baseline approach, as evidenced by similar root mean square errors across the data splits and calibration dataset sizes. When comparing the estimated parameters of the chill and heat accumulation submodels, we observed a large variation among cultivars of the same species in the cultivar-fit models. In contrast and by design, the combined-fit yielded only one parameter set for cultivars of the same species. Our findings demonstrate that integrating data from multiple cultivars can yield spring phenology models with high accuracy. Even though the combined-fit approach did not outperform the cultivar-fit approach, combined-fitting offers a practical solution for spring phenology modeling with limited datasets and facilitates comparison across cultivars of the same species.