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To investigate the associations between long-term exposure to ambient air pollution and age at menopause and the risk of early menopause in two Asian cohorts.A total of 53,167 female adults were enrolled from two ongoing cohorts, one each in Taiwan and Hong Kong, between 2003 and 2018, yielding 200,000 person-years of follow-up. We performed a Cox regression model with time-dependent covariates to investigate associations between air pollution and menopause.The mean age at baseline and at natural menopause was 34.4 and 51.0 years, respectively. In the single-pollutant models, we found that increase in PMLong-term exposure to ambient air pollution was associated with the age at menopause and the risk of early menopause. More effective strategies to mitigate air pollution are required.

South China, with a long and meandering shoreline and well-developed economy, is highly vulnerable to tropical cyclone (TC)-caused storm surges and extreme sea states. The current global warming is expected to continue or even worsen in the rest of the 21st century. Therefore, the warmer sea surface temperature (SST) and lifted mean sea level tend to fuel much more ferocious storm surges. In this study, three severe landfalling TCs have been reproduced by a sophisticated air-wave-ocean coupled model of ultra-high spatial resolution (1-km atmosphere and 500-m wave and ocean). The present-day and pseudo-global warming context mimicking the 2090s have been simulated to investigate the climate change effect. Results indicate that the coupled model can accurately reproduce the air-wave-ocean status during the TC episodes. The 2090s thermodynamic status will effectively increase the intensity of the severe TCs. Typhoon Vicente (2012) even attained a 30% increase in 10-meter wind speed due to its 24-hour stagnant track over the northern South China Sea. On average, the maximum storm surges are lifted by 0.3-0.8 meters over the open sea while aggravating much higher along the coastline, especially for narrowing estuaries where the maximum surge level can be elevated up to 2 meters. Changes in maximum significant wave height show more complicated patterns due to their sensitivity to TC tracks. For Typhoon Vicente (2012), a more than 2-meter wave height increase is observed both in open sea and along the coastline. In the 2090s context, a combination of mean sea level rise, storm surge, and wave height can reach more than 4 meters increase in total water level at certain coastal hot spots. This will cause much more severe damage and losses at the end of the 21st century.

Background and aims White blood cell (WBC) count is an easily obtainable biomarker of systematic inflammation. Our study aimed to investigate the associations of differential WBC count with all-cause and cause-specific mortality in a general Asian population. Methods and Results Cox proportional hazards model was used to evaluate the associations of WBC count with mortality separately for men and women, with adjustment for multiple variables including age, smoking, and other lifestyle factors. Stratified analyses by age, smoking, diabetes, and hypertension were conducted to explore potential effect modification. Elevated WBC count was significantly associated with increased mortality risk. The adjusted hazard ratios of total WBC (10th decile compared to decile of lowest risk) for all-cause mortality were 1.42 (95% CI: 1.33, 1.53) for men and 1.54 (95% CI: 1.42, 1.68) for women. Similar risks were observed for neutrophils, monocytes, and neutrophil/lymphocyte (NL) ratio. The highest deciles of neutrophils, monocytes, and NL ratio were also positively associated with risk of cardiovascular/cerebrovascular, cancer, and respiratory mortality after adjusting for covariates. Results for all-cause mortality remained statistically significant for participants who were Conclusions Total and differential WBC counts (neutrophils, monocytes, and NL ratios) are positively associated with increased risk of all-cause mortality, cardiovascular and cerebrovascular, cancer, and respiratory mortality among Taiwanese adults.

Habitual exercise may amplify the respiratory uptake of air pollutants in the lung, exacerbating the adverse effects of air pollution. However, it is unclear whether this can reduce the health benefits of habitual exercise (referred to as leisure-time exercise). Thus, the combined effects of habitual exercise and chronic exposure to ambient fine particulate matter 2.5 on cardiovascular mortality were examined among adults in Taiwan.A total of 384,128 adults were recruited between 2001 and 2016 and followed up to May 31, 2019. Participants' vital status was obtained by matching their unique identification numbers with records of cardiovascular death in the National Death Registry of Taiwan. A time-varying Cox regression model was used to analyze the data. Analyses were conducted in 2021.Cardiovascular death risks were inversely associated with habitual exercise and positively associated with chronic exposure to particulate matter 2.5. The beneficial effects of habitual exercise on cardiovascular mortality were not modified by chronic exposure to particulate matter 2.5. Inactive participants with high particulate matter 2.5 exposure exhibited a 123% higher risk of cardiovascular death than high-exercise-group participants exposed to low levels of particulate matter 2.5 (95% CI=89, 163).High level of habitual exercise combined with low exposure level of ambient particulate matter 2.5 is associated with the lowest risk of cardiovascular death. A higher level of habitual exercise is associated with a lower risk of cardiovascular death at all levels of particulate matter 2.5 exposure studied. The results indicate that habitual exercise is a safe health promotion strategy even for people residing in relatively polluted regions.

ObjectivesExercise may increase the inhaled amount of air pollutants and exacerbate the adverse health effects. We investigated the combined effects of chronic exposure to fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) and habitual exercise on C reactive protein (CRP), a sensitive marker of inflammation.MethodsWe selected 40 209 Taiwanese adults who joined a standard medical screening programme between 2001 and 2016. The PM2.5 exposure was estimated at each participant’s address using a satellite-based spatiotemporal model. Information on habitual exercise was collected using a standard self-administered questionnaire. Mixed-effects linear regression models were used to investigate the associations of CRP with PM2.5 and exercise. An interaction term of PM2.5 and exercise was introduced in the models to test the modifying effects.ResultsA greater amount of habitual exercise was associated with a decreased level of CRP, while a higher concentration of PM2.5 exposure was associated with an increased level of CRP. The inverse associations of habitual exercise with CRP were not modified by chronic exposure to PM2.5. The participants in the group with a low level of exercise and a high level of PM2.5 exposure exhibited a 19.1% higher level of CRP than those in the group with a high level of exercise and a low level of PM2.5 exposure (95% CI: 13.7% to 24.8%; pConclusionsIncreased levels of exercise and reduced exposure levels of PM2.5 are associated with a lower CRP level. Habitual exercise reduces CRP level regardless of the levels of chronic PM2.5 exposure. Our results support that habitual exercise is a safe approach for reducing systemic inflammation to improve cardiovascular health even for people residing in relatively polluted areas.

Background Several studies reported that long-term exposure to fine particulate matter (PM2.5) was associated with an increased risk of chronic obstructive pulmonary disease (COPD). It remains unclear whether reduced PM2.5 can decrease the risk of COPD development. Objective To investigate the associations of dynamic changes (including deterioration and improvement) in long-term exposure to ambient PM2.5 with changes in lung function and the incidence of COPD. Methods A total of 133,119 adults (aged 18 years or older) were recruited in Taiwan between 2001 and 2014. All participants underwent at least two standard medical examinations including spirometry test. We estimated PM2.5 concentrations using a high-resolution (1 km2) satellite-based spatio-temporal model. The change in PM2.5 (ΔPM2.5) was defined as the difference in concentration of PM2.5 between the respective visit and the previous visit. We used a multivariable mixed linear model and time-varying Cox model to investigate the associations of change in PM2.5 with annual change of lung function and the incidence of COPD, respectively. Result The PM2.5 concentration in Taiwan increased during 2002–2004 and began to decrease around 2005. Every 5-µg/m3/year decrease in the annual change of PM2.5 (i.e., ΔPM2.5/year of 5 µg/m3/year) was associated with an average increase of 19.93 mL/year (95 %CI: 17.42,22.43) in forced expiratory volume in 1 s (FEV1), 12.76 mL/year (95 %CI: 9.84,15.66) in forced vital capacity (FVC), 70.22 mL/s/year (95 %CI: 64.69,76.16) in midexpiratory flow between 25 and 75% of the forced vital capacity (MEF25-75), 0.27%/year (95 %CI: 0.21%, 0.32%) in FEV1/FVC/year. Every 5 µg/m3 decrease in PM2.5 (i.e., ΔPM2.5 of 5 µg/m3) was associated with a 12% (95 %CI: 7%, 17%) reduced risk of COPD development. The stratified and sensitivity analyses generally yielded similar results. Conclusion An improvement in PM2.5 pollution exposure was associated with an attenuated decline in lung function parameters of FEV1, FVC, MEF25-75, and FEV1/FVC, and a decreased risk of COPD development. Our findings suggest that strategies aimed at reducing air pollution may effectively combat the risk of COPD development.

Photochemical regime for ozone (O

Vertical transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) along a vertical column of flats has been documented in several outbreaks of coronavirus disease 2019 (COVID-19) in Guangdong and Hong Kong. We describe an outbreak in Luk Chuen House, involving two vertical columns of flats associated with an unusually connected two-stack drainage system, in which nine individuals from seven households were infected. The index case resided in Flat 812 (8th floor, Unit 12), two flats (813, 817) on its opposite side reported one case each (i.e., a horizontal sub-cluster). All other flats with infected residents were vertically associated, forming a vertical sub-cluster. We injected tracer gas (SF

In the modern era, people spend approximately 90% of their time in indoor settings, such as offices and residential buildings. As prolonged exposure to indoor environments can significantly impact health outcomes, it is important to ensure that good indoor air quality (IAQ) is achieved. The main obstacles to achieving effective control of IAQ are twofold. First, it is very difficult to monitor the values of IAQ parameters, especially within a person's breathing zone. Second, current heating ventilation and air conditioning systems are unable to rapidly predict and optimize IAQ. This study aims to obtain accurate indoor environmental parameters for rapidly predicting and optimizing IAQ. To achieve this, a computational fluid dynamics (CFD)-based back propagation neural network (BPNN) combined with a particle swarm optimizer (PSO) algorithm is proposed. Notably, the BPNN-PSO algorithm can rapidly predict and optimize IAQ while using a limited number of CFD runs. In comparison with other state-of-the-art methods for controlling IAQ, such as the artificial neural network (ANN)-genetic algorithm (GA), the BPNN-PSO algorithm further reduces the concentration of indoor air pollutants by up to 6.44% and computational costs by as much as 23.53%. The use of CFD ensures that the information obtained is accurate and allows for rapid prediction of indoor environmental conditions. The BPNN-PSO algorithm has the potential to contribute very effective and intelligent control strategies for ventilating indoor environments.

Heating ventilation and air conditioning (HVAC) systems provide a comfortable indoor thermal environment, but in the process of attaining appropriate indoor thermal comfort levels, they usually entail high energy consumptions. It is therefore imperative to balance thermal comfort value with energy consumption. However, such research currently faces two problems: one, it is difficult to obtain accurate parameters pertaining to the indoor environment of buildings, particularly near heat source areas; two, it is the diametrical nature of having to simultaneously maintain thermal comfort and keep energy consumption low. Therefore, this study aims to propose a rapid thermal comfort level prediction and optimization algorithm, as well as a method to minimize the energy consumption using only a computational fluid dynamic (CFD) database that is compact in size. Firstly, CFD is used to implement the database that stores data on indoor airflow and temperature distributions of different building structures and indoor conditions. Next, using the database as a basis, a back-propagation neural network (BPNN) is developed to predict the thermal comfort level. The adaptive grey wolf optimizer (GWO) algorithm is then applied to optimize the thermal comfort value, and the latest control methods: the artificial neural network (ANN)-genetic algorithm (GA) and ANN-particle swarm optimizer (PSO) algorithm are compared against the BPNN-based adaptive GWO method. The results show that the BPNN-based adaptive GWO algorithm can rapidly predict the thermal comfort level and have strong optimization ability. Meanwhile, 1.01% of energy savings are achieved.

The majority of urban residents live in places with air quality exceeding World Health Organization guidelines. To quantify infiltration of outdoor pollution, and key factors affecting it, common outdoor air pollutants (PM2.5, NO2, and O3) were measured at 49 homes in Hong Kong. Infiltration factors (Finf) were derived based on linear regression of simultaneous indoor and outdoor measurements for each pollutant at each home. Finf estimated based on data for home occupancy, during which people were actually exposed, differed by up to 22%, 73% and 63% for PM2.5, NO2 and O3, respectively, from estimates based on whole monitoring data. This indicates the importance of separating occupancy time to quantify Finf for exposure estimation. The inter-home variability in occupancy Finf ranged from 0.11 to 1.00 (mean: 0.75) for PM2.5, 0.14 to 1.00 (mean: 0.53) for NO2, and 0.05 to 0.95 (mean: 0.47) for O3. Ventilation practices (e.g., window opening duration and air-conditioning on/off) explained 48%, 20%, and 10% of the inter-home variations in PM2.5, NO2 and O3 Finf, respectively. Use of air purifiers explained an additional 8%–9% of variations for PM2.5 and NO2. Thus, there is potential to reduce outdoor infiltration by modifying occupant behaviours. Compared to PM2.5 (R2 = 0.63), the developed models explained less variability in Finf for NO2 (R2 = 0.40) and O3 (R2 = 0.10). These two gases are chemically reactive. Further investigation, supported by additional measurements of related chemical species, is needed to improve understanding of infiltration process of reactive gases such as NO2 and O3.