BACKGROUND: Traumatic fractures occur frequently worldwide. However, research remains limited on the association between short-term exposure to temperature and traumatic fractures. This study aims to explore the impact of apparent temperature (AT) on emergency visits (EVs) due to traumatic fractures.
METHODS: Based on EVs data for traumatic fractures and the contemporary meteorological data, a generalized Poisson regression model along with a distributed lag nonlinear model (DLNM) were undertaken to determine the impact of AT on traumatic fracture EVs. Subgroup analysis by gender and age and sensitivity analysis were also performed.
RESULTS: A total of 25,094 EVs for traumatic fractures were included in the study. We observed a wide \"J\"-shaped relationship between AT and risk of traumatic fractures, with AT above 9.5 °C positively associated with EVs due to traumatic fractures. The heat effects became significant at cumulative lag 0-11 days, and the relative risk (RR) for moderate heat (95th percentile, 35.7 °C) and extreme heat (99.5th percentile, 38.8 °C) effect was 1.311 (95% CI: 1.132-1.518) and 1.418 (95% CI: 1.191-1.688) at cumulative lag 0-14 days, respectively. The cold effects were consistently non-significant on single or cumulative lag days across 0-14 days. The heat effects were higher among male and those aged 18-65 years old. The sensitivity analysis results remained robust.
CONCLUSIONS: Higher AT is associated with cumulative and delayed higher traumatic fracture EVs. The male and those aged 18-65 years are more susceptible to higher AT.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide, posing a significant threat to public health. Research on the relationship between CVD and temperature has primarily focused on developed urban settings, with limited studies conducted in rural regions with lower levels of development. Additionally, compared to relative risks, attributable risks can provide more information when assessing the risk of CVD hospitalizations associated with exposure to apparent temperature (AT). Apparent temperature is a composite temperature index that takes into account both meteorological factors and temperature, providing an objective reflection of human thermal sensation. Therefore, this study investigates the impact of AT on CVD hospitalization and quantifies the burden of CVD admission in the rural areas of China. We employed the distributed lag non-linear model (DLNM) to estimate the relationship between AT and the relative risk (RR) of CVD hospitalization. Finally, we used attributable risk methods to quantify this relationship further.

BACKGROUND: Non-optimum temperatures are associated with increased risk of respiratory diseases, but the effects of apparent temperature (AT) on respiratory diseases remain to be investigated.
METHODS: Using daily data from 2016 to 2020 in Ganzhou, a large city in southern China, we analyzed the impact of AT on outpatient and inpatient visits for respiratory diseases. We considered total respiratory diseases and five subtypes (influenza and pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma and chronic obstructive pulmonary disease [COPD]). Our analysis employed a distributed lag nonlinear model (DLNM) combined with a generalized additive model (GAM).
RESULTS: We recorded 94,952 outpatients and 72,410 inpatients for respiratory diseases. We found AT significantly non-linearly associated with daily outpatient and inpatient visits for total respiratory diseases, influenza and pneumonia, and URTI, primarily during comfortable AT levels, while it was exclusively related with daily inpatient visits for LRTI and COPD. Moderate heat (32.1 °C, the 75.0th centile) was observed with a significant effect on both daily outpatient and inpatient visits for total respiratory diseases at a relative risk of 1.561 (1.161, 2.098) and 1.276 (1.027, 1.585), respectively (both P < 0.05), while the results of inpatients became insignificant with the adjustment for CO and O3. The attributable fractions in outpatients and inpatients were as follows: total respiratory diseases (24.43% and 18.69%), influenza and pneumonia (31.54% and 17.33%), URTI (23.03% and 32.91%), LRTI (37.49% and 30.00%), asthma (9.83% and 3.39%), and COPD (30.67% and 10.65%). Stratified analyses showed that children ≤5 years old were more susceptible to moderate heat than older participants.
CONCLUSIONS: In conclusion, our results indicated moderate heat increase the risk of daily outpatient and inpatient visits for respiratory diseases, especially among children under the age of 5.

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China\'s rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it\'s observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), [Formula: see text]. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

Permeable pavements help reduce surface temperatures and have been widely implemented in urban areas. This study utilized an in-use permeable pavement sidewalk in front of a mass rapid transit station in the Taipei city center of Taiwan to determine the actual pavement surface temperature performance. A neighboring asphalt road and impervious pavement were also monitored. With a full year of continuous monitoring, the results showed that the temperature of permeable pavement was 3.7 °C lower than that of impervious pavement and 4.5 °C lower than that of asphalt pavement in the hot season. The frequent rainfall in spring resulted in the smallest temperature differences between the different pavement types. The cooling effects of permeable pavement differed at the different air temperatures. At air temperatures lower than 15 °C, the differences among pavement surface temperatures were noticeable. However, when the air temperature was higher than 35 °C, the surface temperature of permeable pavement was not different from that of impervious pavement and was greater than 55 °C. Field observations were carried out to determine the effects on the apparent temperature and the future surface temperature of climate change scenarios. The results showed that permeable pavement could reduce the average apparent temperature to near the air temperature, and asphalt pavement could increase the apparent temperature by 1.2 °C, assuming that the pavement temperature completely affects the air temperature. With the good prediction ability of the machine learning approach and 15 environmental factors, the preliminary prediction showed the projected surface temperature change in Taipei city in 2033. In the worst-case scenario, the average impervious pavement temperature is as high as 39.12 °C, whereas the average permeable pavement temperature is 32.50 °C.

A case-crossover study among 511,767 cardiovascular disease (CVD) deaths in Jiangsu province, China, during 2015-2021 was conducted to assess the association of exposure to ambient ozone (O3) and heat wave with CVD mortality and explore their possible interactions. Heat wave was defined as extreme high temperature for at least two consecutive days. Grid-level heat waves were defined by multiple combinations of apparent temperature thresholds and durations. Residential O3 and heat wave exposures were assessed using grid data sets (spatial resolution: 1 km × 1 km for O3; 0.0625° × 0.0625° for heat wave). Conditional logistic regression models were applied for exposure-response analyses and evaluation of additive interactions. Under different heat wave definitions, the odds ratios (ORs) of CVD mortality associated with medium-level and high-level O3 exposures ranged from 1.029 to 1.107 compared with low-level O3, while the ORs for heat wave exposure ranged from 1.14 to 1.65. Significant synergistic effects on CVD mortality were observed for the O3 and heat wave exposures, which were generally greater with higher levels of the O3 exposure, higher temperature thresholds, and longer durations of heat wave exposure. Up to 5.8% of the CVD deaths were attributable to O3 and heat wave. Women and older adults were more vulnerable to the exposure to O3 and heat wave exposure. Exposure to both O3 and heat wave was significantly associated with an increased odds of CVD mortality, and O3 and heat wave can interact synergistically to trigger CVD deaths.

UNASSIGNED: Most studies evaluating the possible seasonal variation of semen quality have considered temperature as the only causal factor.
UNASSIGNED: To assess possible seasonality in sperm quality and associations between semen parameters and several meteorological variables (temperature, humidity, apparent temperature and atmospheric pressure) in a large cohort of andrological patients.
UNASSIGNED: This was a retrospective, cross-sectional and correlational/descriptive study.
UNASSIGNED: Patients (n: 15665) were categorised into four groups (summer, winter, spring and autumn) according to the date of assistance at the fertility centre. Daily values of temperature, apparent temperature, humidity and atmospheric pressure were provided by the National Weather System and were calculated as the average of the 74 days previous to semen collection (spermatogenic cycle).
UNASSIGNED: As appropriate, the results were analysed by analysis of variance/Kruskal-Wallis, Chi-square test, t-test/Mann-Whitney, forward conditional regression model and Spearman/Pearson\'s correlations.
UNASSIGNED: We detected seasonality effects on sperm count, total sperm count and total motile sperm count, with the highest values in winter and the lowest in summer. Correlation analysis showed that temperature, apparent temperature and humidity negatively correlated with semen parameters, being humidity the most powerful predictive meteorological variable.
UNASSIGNED: Sperm quality is influenced by seasons; increased environmental temperature and humidity negatively affect semen quality.

OBJECTIVE: Although it has previously been shown that temperature is associated with cardiovascular disease, no investigations exploring the association between apparent temperature (AT) and hypertension in farmers in Zhangye and Longnan, Gansu Province, China, have been undertaken. As hypertension is a commonly known risk factor for cardiovascular disease, the relationship between apparent temperature (AT) and hypertension is examined in Zhangye and Longnan to provide advice to local governments on preventive measures.
METHODS: Daily data and weather conditions were collected in Zhangye and Longnan from 2014-2015. The Poisson generalized linear model and the distributed lag nonlinear model (DLNM) were combined to investigate the relationship between AT and hypertension in hospital admissions in the study areas.
RESULTS: A non-linear relationship between AT and hypertension in hospital admissions in both Zhangye and Longnan were recorded. The cold effects were stronger in Zhangye than that in Longnan for both study group and subgroups. The heat effects were more deleterious for the entire study group, female subgroup and adult subgroup in Longnan, but stronger for the male subgroup and elderly subgroup in Zhangye.
CONCLUSIONS: This investigation indicates that AT has adverse impacts on hypertension hospital admissions in Zhangye and Longnan, especially under low AT exposure levels. The results from this study may promote the formulation of further prevention measures for hypertension disease.

Among the various thermal stress indices, apparent temperature (AT) is closely related to public health indicators, and consequently is widely used by weather agencies around the world. Therefore, in this paper we estimate the changes in AT and contributing components in Korea as a whole and in five major cities (Seoul, Gwanju, Daegu, Daejeon, and Busan) using national standard climate scenarios based on the coupled model inter-comparison project (CMIP6). In the present day, high AT occurs in major cities due to high temperature (TAS) and relative humidity (RH). Our findings reveal that even when TAS is relatively low, large AT occurs with higher humidity. Notably, in future warmer climate conditions, high AT may first appear in the five major cities and then extend to the surrounding areas. An increase in TAS and RH during the pre-hot season (March to June) may lead to earlier occurrence of thermal risks in future warmer climate conditions and more frequent occurrence of high thermal stress events. Our study can serve as a reference for future information on thermal risk changes in Korea. Considering those who have not adapted to high temperature environments, our findings imply that thermal risks will become more serious and that heat adaptation strategies will be needed during the pre-hot season under future warmer climate conditions.

Robust spatio-temporal delineation of extreme climate events and accurate identification of areas that are impacted by an event is a prerequisite for identifying population-level and health-related risks. In prior research, attributes such as temperature and humidity have often been linearly assigned to the population of the study unit from the closest weather station. This could result in inaccurate event delineation and biased assessment of extreme heat exposure. We have developed a spatio-temporal model to dynamically delineate boundaries for Extreme Heat Events (EHE) across space and over time, using a relative measure of Apparent Temperature (AT). Our surface interpolation approach offers a higher spatio-temporal resolution compared to the standard nearest-station (NS) assignment method. We show that the proposed approach can provide at least 80.8 percent improvement in identification of areas and populations impacted by EHEs. This improvement in average adjusts the misclassification of about one million Californians per day of an extreme event, who would be either unidentified or misidentified under EHEs between 2017 and 2021.