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The official air-quality statistic reported that Beijing had a 9.9% decline in the annual concentration of PM2.5 in 2016. While this statistic offered some relief for the inhabitants of the capital, we present several analyses based on Beijing's PM2.5 data of the past 4 years at 36 monitoring sites along with meteorological data of the past 7 years. The analyses reveal the air pollution situation in 2016 was not as rosy as the 9.9% decline would convey, and improvement if any was rather uncertain. The paper also provides an assessment on the city's PM2.5 situation in the past 4 years.

Objective At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. Methods Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. Results In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. Conclusions The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

The widespread and pervasive use of artificial light at night (ALAN) in our modern 24-hour society has emerged as a substantial disruptor of natural circadian rhythms, potentially leading to a rise in unhealthy lifestyle-related behaviors (e.g., poor sleep; shift work). This phenomenon has been associated with an increased risk of type 2 diabetes mellitus (T2DM), which is a pressing global public health concern. However, to date, reviews summarizing associations between ALAN and T2DM have primarily focused on the limited characteristics of exposure (e.g., intensity) to ALAN. This literature review extends beyond prior reviews by consolidating recent studies from 2000 to 2024 regarding associations between both indoor and outdoor ALAN exposure and the incidence or prevalence of T2DM. We also described potential biological mechanisms through which ALAN modulates glucose metabolism. Furthermore, we outlined knowledge gaps and investigated how various ALAN characteristics beyond only light intensity (including light type, timing, duration, wavelength, and individual sensitivity) influence T2DM risk. Recognizing the detrimental impact of ALAN on sleep health and the behavioral correlates of physical activity and dietary patterns, we additionally summarized studies investigating the potential mediating role of each component in the relationship between ALAN and glucose metabolism. Lastly, we proposed implications of chronotherapies and chrononutrition for diabetes management in the context of ALAN exposure.

Hemorrhagic Fever with Renal Syndrome (HFRS) continues to pose a significant public health threat to the well-being of the population. Given that the spread of HFRS is susceptible to meteorological factors, we aim to probe into the meteorological drivers of HFRS. Thus, novel techniques that can discern time-delayed non-linear relationships from nonlinear dynamical systems are compulsory. We analyze the epidemiological features of HFRS in Weifang City, 2011-2020, via the employment of the Empirical Dynamic Modeling (EDM) method. Our analysis delves into the intricate web of time-delayed non-linear associations between meteorological factors and HFRS. Additionally, we investigate the repercussions of minor perturbations in meteorological variables on future HFRS incidence. A total of 2515 HFRS cases were reported in Weifang from 2011 to 2020. The number of cases per week was 4.81, and the average weekly incidence was 0.52 per 1,000,000. The propagation of HFRS is significantly impacted by the mean weekly temperature, relative humidity, cumulative rainfall, and wind speed, and the ρCCM converges to 0.55,0.48,0.38 and 0.39, respectively. The graphical representation of the relationship between temperature (lagged by 2 weeks) and the incidence of HFRS exhibits an inverted U-shaped curve, whereby the incidence of HFRS culminates as the temperature reaches 10 °C. Moreover, temperature, relative humidity, cumulative rainfall, and wind speed exhibit a positive correlation with HFRS incidence, with a time lag of 4-6 months. Our discoveries suggest that meteorological factors can drive the transmission of HFRS both at a macroscopic and microscopic scale. Prospective alterations in meteorological conditions, for instance, elevations in temperature, relative humidity, and precipitation will instigate an upsurge in the incidence of HFRS after 4-6 months, and thus, timely public health measures should be taken to mitigate these changes.

Background The relationship between nighttime lights and human health is being discussed in a growing number of studies, with a vast majority of results supporting an association. However, the impact of potential confounding has been insufficiently addressed in existing studies, undermining the reported associations. Objective This study aimed to assess the relationship between exposure to nighttime lights and population health outcomes in the USA with a careful adjustment of potential confounders. Methods This ecological study analyzed county-level data from 3104 counties in the USA. Nighttime light exposure data were derived from the DMSP-OLS Nighttime Lights Time Series. The outcomes were county-level health indicators including life expectancy at birth, age-specific mortality risks, and 21 cause-specific mortality rates. Covariates included county-level data on population demographics, socio-economics, healthcare, and environmental variables. Univariate regression and multivariate regression models were applied to analyze the crude and confounding-adjusted association between the exposure and outcomes. Results After adjusting for confounders, no significant associations were found between nighttime light exposure and the population health outcomes except mortality rates from mental and substance use disorders, and transport and unintentional injuries. Substantial changes in the direction and magnitude of the association estimates were identified before and after adjusting for the potential confounders, particularly the urbanization and socio-economic factors. Conclusions This national study provides evidence that the relationship between nighttime light exposure and population health outcomes is complex and often confounded by environmental and socio-economic factors, highlighting the demands of cautions and further investigations.

While numerous studies have investigated associations between meteorological factors and the incidence of rhegmatogenous retinal detachment (RRD), findings remain inconclusive. We have identified key limitations in establishing this relationship due to confounding bias in prior research. This retrospective, observational study aims to address these gaps by minimizing residual confounding bias, focusing exclusively on patients aged ≥65 to limit seasonal recreational activity as a risk factor, and using incidence ratio calculations to account for patient intake fluctuations. This study explores these dynamics within the distinct climatic conditions of Las Vegas, Nevada, characterized by intense summer heat, cold winters, and low humidity. A retrospective chart review was performed across four Retina Consultants of Nevada locations from December 2020 to November 2023. The study included patients aged ≥65 diagnosed with a primary RRD. Exclusion criteria were non-residency, recurrent RRD, and comorbid retinal conditions. Monthly RRD incidence ratios were calculated relative to all new patients seen for retinal conditions in the same age group. Environmental data (temperature, atmospheric pressure, humidity, UV index) were obtained from national meteorological databases. Correlations between meteorological factors and RRD incidence were assessed using Pearson correlation coefficients. Among 308 patients (mean age 72.4 years (SD=5.93), 59.7% male, p=0.0006), no significant correlations were found between RRD incidence and average monthly temperature (r=0.004, p=0.982), barometric pressure (r=-0.047, p=0.786), humidity (r=0.15, p=0.368), or UV index (r=0.072, p=0.68). Seasonal analysis showed a non-significant trend of higher RRD incidence during colder months (r=-0.70, p=0.30). Laterality (right eye: 53.2%) was also not statistically significant (p=0.254). This study's strength lies in addressing confounding factors by focusing on an older, less active population and using incidence ratios to adjust for patient intake fluctuations. The findings challenge prior reports by showing no significant association between meteorological factors and RRD incidence, suggesting a limited role for environmental variables in older subjects.

Drowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. We aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. Unintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. A total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and -9.5% to the overall effects, respectively. This study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Methane (CH4) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH4 concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH4 concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH4 (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission.

The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM 2.5 (20%), PM 10 (24%), SO 2 (12%), and O 3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM 2.5 (3%), PM 10 (1%), SO 2 (7.7%), and O 3 (10%). Similarly, there is an increment in COVID-19 deaths for PM 2.5 (2.8%), PM 10 (1%), SO 2 (4.5%), and O 3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies.

NOABSTRACTPrevious studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease (CAD). However, there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes, especially in cold regions. This study aimed to systematically investigate the relationship between exposure to meteorological changes, air pollutants, and hospitalization for CAD in cold regions.We conducted a cross-sectional study using hospitalization records of 86,483 CAD patients between January 1, 2009, and December 31, 2019. Poisson regression analysis, based on generalized additive models, was applied to estimating the influence of hospitalization for CAD.Significant associations were found between low ambient temperature [-10°C, RR = 1.65; 95% CI: (1.28–2.13)] and the incidence of hospitalization for CAD within a lag of 0–14 days. Furthermore, O3 [95.50 μg/m3, RR = 12; 95% CI: (1.03–1.21)] and NO2 [48.70 μg/m3, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. Furthermore, O3 [95.50 μg/m3, RR = 1.12; 95% CI: (1.03–1.21)] and NO2 [48.70 μg/m3, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO2 and O3 concentrations were higher, with a pronounced effect at 7 days, dissipating by lag 14 days. No significant associations were observed between exposure to PM, SO2, air pressure, humidity, or wind speed and hospitalization incidences due to CAD and its subtypes.Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants (O3 and NO2) and hospitalizations for CAD, STEMI, and NSTEMI. These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.