It is reported that sudden sensorineural hearing loss (SSNHL) is closely related to diabetes, hypertension, and hyperlipidemia. While the metabolic syndrome (MetS) is a multifactorial disease that includes diabetes, hypertension, dyslipidemia, and obesity, which are known to be associated with SSNHL. Weather conditions have long been known to affect the SSNHL. This study aimed to make a clear connection between MetS, or weather conditions, and the severity and prognosis of SSNHL. 127 SSNHL patients have been divided into the MetS group and the non-MetS group, and the demographic and clinical characteristics of the 2 groups have been analyzed retrospectively. There were 52 (40.9%) patients in the MetS group, while there were 75 (59.1%) patients in the non-MetS group. The rate of vertigo, hypertension, diabetes, lower high-density lipoprotein cholesterol (HDL-C) levels, high triglyceride (TG), and body mass index (BMI) ≥25 (kg/m2 ) were significantly higher in the MetS group than those in non-MetS group. Vertigo, hypertension, and Mets were linked to the severity of hearing loss. The rate of complete recovery and partial recovery in the MetS group was clearly lower than that in non-MetS group. According to the multivariate analysis, MetS was significantly associated with a poorer prognosis of SSNHL; a high ambient temperature difference at onset and hypertension were correlated with a poor prognosis. These results demonstrate that the severity and prognosis of SSNHL can be influenced by the MetS. High ambient temperature differences at onset and hypertension were indicators of a poor prognosis for SSNHL.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

UNASSIGNED: Although the regular exercise population is a key metric for gaging the success of China\'s fitness-for-all activities, effective policy approaches to increase mass sports participation remain unclear. Previous research suggests that GDP, educational attainment, sports resources, and meteorological conditions could influence regular exercise participation. Therefore, this study first analyzed the macro-level correlates influencing China\'s regular exercise population.
UNASSIGNED: We utilize ordinary least squares (OLS) regression and geographical weighted regression (GWR) to theorize the relationship. The analysis encompasses data from the 31 administrative regions of Mainland China, as reported at the end of the 13th Five-Year Plan period. The log-log model enables us to quantify the marginal effect (elasticity) of the explanatory variables.
UNASSIGNED: The OLS regression showed that regional GDP and the proportion of the population with a university education were significant predictors. In the global model, the marginal effects of regional GDP and university education were 0.048 and 0.173, respectively. Furthermore, the GWR revealed a distinct geographic pattern that corresponds to the classic Hu Line.
UNASSIGNED: While regional GDP was also a significant correlate in our model, the elasticity demonstrates that university education had an asymmetric effect on China\'s regular exercise population. Therefore, this paper sheds light on a policy priority for the upcoming 15th Five-Year Plan, emphasizing the strategic importance of expanding university education to enhance mass sports participation. In turn, a better-educated populace may yield significant secondary effects on public health and contribute to the high-quality development of the Chinese path to modernization.

BACKGROUND: The association between ambient coarse particulate matter (PM2.5-10) and mortality in multi-drug resistant tuberculosis (MDR-TB) patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown.
OBJECTIVE: To evaluate the effects of long-term PM2.5-10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients.
METHODS: A Chinese cohort of 3469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study\'s end, averaging 2567 days per patient. PM2.5-10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 μg/m3 (interquartile range, IQR) exposure to PM2.5-10 and all-cause mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines.
RESULTS: Hazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5-10 exposures for the full cohort under both long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and surrounding environment measured by greenness and nighttime light levels. Nonlinear exposure-response curves suggestes a cumulative risk of PM2.5-10-related mortality starting from a low exposure concentration around 15 μg/m3.
CONCLUSIONS: Long-term exposure to PM2.5-10 poses significant harm among MDR-TB patients, with effects modified by temperature and humidity. Immediate surveillance of PM2.5-10 is crucial to mitigate the progression of MDR-TB severity, particularly due to co-exposures to air pollution and adverse weather conditions.

Outbreaks of airborne viral emerging infectious diseases (EIDs) cause an increasing burden on global public health, particularly with a backdrop of intensified climate change. However, infection sources and drivers for outbreaks of airborne viral EIDs remain unknown. Here, we aim to explore the driving mechanisms of outbreaks based on the one health perspective. Outbreak information for 20 types of airborne viral EIDs was collected from the Global Infectious Disease and Epidemiology Network database and a systematic literature review. Four statistically significant and high-risk spatiotemporal clusters for airborne viral EID outbreaks were identified globally using multivariate scan statistic tests. There were 112 outbreaks with clear infection sources, and zoonotic spillover was the most common source (95.54%, 107/112). Since 1970, the majority of outbreaks occurred in healthcare facilities (24.82%), followed by schools (17.93%) and animal-related settings (15.93%). Significant associations were detected between the number of earthquakes, storms, duration of floods, and airborne viral EIDs\' outbreaks using a case-crossover study design and multivariable conditional logistic regression. These findings implied that zoonotic spillover and extreme weather events are driving global outbreaks of airborne viral EIDs, and targeted prevention and control measures should be made to reduce the airborne viral EIDs burden.

Knowledge of the effect of harsh weather on the phenotypic traits of organisms is essential for understanding the environmental influence on phenotype evolution and holds implications for predicting how species respond to current climate change. For many birds, harsh weather in winter often imposes a strong selective effect on their survival, and only the individuals with certain phenotypes may survive. However, whether the selective effect on phenotype varies with winter weather conditions has been poorly investigated. Here, we explored the selective effect of winter weather on black-throated tit\'s (Aegithalos concinnus) morphological traits under winters with and without severe snowstorms. We found that for males, the sizes of their bills, heads and wings significantly affected their overwinter survival, but the effects varied with winter conditions. In relatively benign winters, males with smaller bill depths, smaller bill surface areas, and greater head lengths survived better; whereas, in winters with severe snowstorms, a reverse pattern was found. This phenomenon was likely driven by selection pressures from heat retention and foraging requirements, with their relative importance depending on winter conditions. Additionally, wing length was positively correlated with male survival and the relationship was stronger in harsher winters, which was probably due to longer wings\' higher flight efficiency in adverse weather. By contrast, we found no correlation between morphological traits and survival in females. These results suggest a sex-specific and condition-dependent selective effect of environment on bird phenotypes, implying complicated interactions between different selection pressures and phenotype evolution.

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O3) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T2) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O3 concentration increased by 1.12, 1.37, and 0.76 μg/m3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O3 concentrations decreased most in the BEP simulation (-0.77 μg/m3), while O3 concentrations increased most in the BEM simulation (+1.85 μg/m3). The average observed O3 concentration was 108.35 μg/m3 (three-year average), while the simulated value was 75.65-83.72 μg/m3 (R = 0.69-0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O3 in urban agglomerations could be improved by integrating the results of the UCMs.

As a central topic in Behavioral Ecology, animal space use involves dynamic responses to social and ecological factors. We collared 22 rhesus macaques (Macaca mulatta) from six groups on Neilingding Island, China, and collected 80,625 hourly fixes over a year. Using this high-resolution location data set, we quantified the macaques\' space use at the individual level and tested the ecological constraints model while considering various environmental and human interfering factors. As predicted by the ecological constraints model, macaques in larger groups had longer daily path lengths (DPLs) and larger home ranges. We found an inverted U-shape relationship between mean daily temperatures and DPLs, indicating that macaques traveled farther on mild temperature days, while they decreased DPLs when temperatures were too high or too low. Anthropogenic food subsidies were positively correlated to DPLs, while the effect of rainfall was negative. Macaques decreased their DPLs and core areas when more flowers and less leaves were available, suggesting that macaques shifted their space use patterns to adapt to the seasonal differences in food resources. By applying GPS collars on a large number of individuals living on a small island, we gained valuable insights into within-group exploitation competition in wild rhesus macaques.

BACKGROUND: While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people.
METHODS: Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures.
RESULTS: A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt.
CONCLUSIONS: This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

BACKGROUND: Environmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences.
METHODS: We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2).
RESULTS: Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4.
CONCLUSIONS: Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.