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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.

Lack of characterization of indoor pollutant concentrations has been identified as a key barrier for exposure and health estimates. In this study, a field campaign was conducted to measure indoor concentrations of PM1, PM2.5, PM10, CO, and NO2 in a mechanically ventilated building. Statistical method using multivariate linear regression (MLR) and machine learning using random forest (RF) were used and compared to quantify variations in observed concentrations and were then used to predict indoor concentrations for selected pollutants. The two methods were consistent in identifying major predictors for each pollutant. Outdoor particles were the single largest predictors found for PM1 and PM2.5, while indoor environment and occupant-related variables were dominant predictors for PM10, CO, and NO2 in the selected mall. Based on MLR models, outdoor PM accounted for 91%, 64%, and 25% of variations in indoor PM1, PM2.5, and PM10 during opening hours. More than 30% of indoor CO variations were related to time-dependent activities. Nearly 50% of the indoor NO2 variations were explained by temperature and relative humidity. Both models are useful in predicting indoor concentrations. In the tenfold cross validation, RF models showed high prediction capability for PM1 (R2 > 0.9) and moderate (R2: 0.5 ~ 0.7) for the other four pollutants in both periods except for PM10 during non-opening hours (R2 = 0.3). MLR models exhibited comparable prediction power for PM1 and PM2.5, but generally lower for PM10 and gases. Availability of parameter information in modern cities facilitates the application of such models on large scale, based on proper validation, for better characterizing of indoor air quality.

Background: Exercise may exacerbate the adverse health effects of air pollution by increasing the inhalation of air pollutants. We investigated the combined effects of long-term exposure to fine particle matter (PM2.5) and habitual exercise on deaths from natural causes in Taiwan. Methods: We recruited 384 130 adults (aged ≥ 18 yr) with 842 394 medical examination records between 2001 and 2016, and followed all participants until May 31, 2019. We obtained vital data from the National Death Registry of Taiwan. We estimated PM2.5 exposure using a satellite-based spatiotemporal model, and collected information on exercise habits using a standard self-administered questionnaire. We analyzed the data using a Cox regression model with time-dependent covariates. Results: A higher level of habitual exercise was associated with a lower risk of death from natural causes, compared with inactivity (hazard ratio [HR] 0.84, 95% confidence interval [CI] 0.80–0.88 for the moderate exercise group; HR 0.65, 95% CI 0.62–0.68 for the high exercise groups), whereas a higher PM2.5 exposure was associated with a higher risk of death from natural causes compared with lower exposure (HR 1.02, 95% CI 0.98–1.07, and HR 1.15, 95% CI 1.10–1.20, for the moderate and high PM2.5 exposure groups, respectively). Compared with inactive adults with high PM2.5 exposure, adults with high levels of habitual exercise and low PM2.5 exposure had a substantially lower risk of death from natural causes. We found a minor, but statistically significant, interaction effect between exercise and PM2.5 exposure on risk of death (HR 1.03 95% CI 1.01–1.06). Subgroup analyses, stratified by PM2.5 categories, suggested that moderate and high levels of exercise were associated with a lower risk of death in each PM2.5 stratum, compared with inactivity. Interpretation: Increased levels of exercise and reduced PM2.5 exposure are associated with a lower risk of death from natural causes. Habitual exercise can reduce risk regardless of the levels of PM2.5 exposure. Our results suggest that exercise is a safe health improvement strategy, even for people residing in relatively polluted regions.

It remains unknown whether reduced air pollution levels can prevent type 2 diabetes mellitus. In this study, we investigated the associations between dynamic changes in long-term exposure to ambient fine particulate matter, defined as particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), and changes in fasting plasma glucose (FPG) levels and incidence of type 2 diabetes. A total of 151,398 adults (ages ≥18 years) were recruited in Taiwan between 2001 and 2014. All participants were followed up for a mean duration of 5.0 years. Change in PM2.5 (ΔPM2.5) was defined as the value at a follow-up visit minus the corresponding value at the immediately preceding visit. The PM2.5 concentration in Taiwan increased during 2002–2004 and began to decrease in 2005. Compared with participants with little or no change in PM2.5 exposure, those with the largest decrease in PM2.5 had a decreased FPG level (β = −0.39, 95% confidence interval: −0.47, −0.32) and lower risk of type 2 diabetes (hazard ratio = 0.86, 95% confidence interval: 0.80, 0.93). The sensitivity analysis and analyses stratified by sex, age, body mass index, smoking, alcohol drinking, and hypertension generally yielded similar results. Improved PM2.5 air quality is associated with a better FPG level and a decreased risk of type 2 diabetes development.

Many countries have dedicated to the mitigation of air pollution in the past several decades. However, evidence of beneficial effects of air quality improvement on chronic kidney disease (CKD) remains limited. We thus investigated the effects of dynamic changes (including deterioration and improvement) in air quality on the incidence of CKD in a longitudinal study in Taiwan. During 2001-2016, this study recruited a total of 163,197 Taiwanese residents who received at least two standard physical examinations. The level of fine particle matter (PM2.5) was estimated using a high-resolution (1 km2) satellite-based spatio-temporal model. We defined changes of PM2.5 concentrations (ΔPM2.5) as the difference between the two-year average measurements during follow-up and during the immediately preceding visit. The time-dependent Cox regression model was adopted to evaluate the relationships between ΔPM2.5 and the incidence of CKD after adjusting for a series of covariates. The concentrations of PM2.5 in Taiwan peaked around 2004 and began to decrease since 2005. We observed an approximate linear concentration-response relationship of ΔPM2.5 with CKD incidence. Every 5 μg/m3 decrease in the ambient concentration of PM2.5 was associated with a 25% reduced risk of CKD development [hazard ratio (HR): 0.75; 95% CI: 0.73, 0.78]. In conclusion, this study demonstrated that the improvement of PM2.5 air quality might be associated with a lower risk of CKD development. Our findings indicate that reducing air pollution may effectively prevent the development of CKD.

Physical activity may increase a person’s inhalation of air pollutants and exacerbate the adverse health effects. This study aimed to investigate the combined associations of chronic exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) and habitual physical activity with the incidence of type 2 diabetes in Taiwan. We selected 156,314 non-diabetic adults (≥18 years old) who joined an ongoing longitudinal cohort between 2001 and 2016. Incident type 2 diabetes was identified at the follow-up medical examinations. Two-year mean PM2.5 exposure was estimated at each participant’s address using a satellite-based spatiotemporal model. Information on physical activity and a wide range of covariates was collected using a standard self-administered questionnaire. We analysed the data using a Cox regression model with time-varying covariates. An interaction term between PM2.5 and physical activity was included to examine the overall interaction effects. Compared with high physical activity, moderate and inactive/low physical activity were associated with a higher risk of diabetes (HR [95% CI] 1.31 [1.22, 1.41] and 1.56 [1.46, 1.68], respectively). Participants with moderate/high PM2.5 had a higher risk of type 2 diabetes than the participants exposed to low PM2.5 (HR 1.31 [1.22, 1.40] and 1.94 [1.76, 2.14], respectively). The participants with high physical activity and low PM2.5 had a 64% lower risk of type 2 diabetes than those with inactive/low physical activity and high PM2.5. Higher physical activity and lower PM2.5 exposure are associated with lower risk of type 2 diabetes. Habitual physical activity can reduce the risk of diabetes regardless of the levels of PM2.5 exposure. Our results indicate that habitual physical activity is a safe diabetes prevention strategy for people residing in relatively polluted regions.

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: We investigated the joint associations of habitual physical activity (PA) and long-term exposure to fine particulate matter (PM 2.5 ) with the development of hypertension in a longitudinal cohort in Taiwan. Methods: We selected 140 072 adults (≥18 years of age) without hypertension who joined a standard medical screening program with 360 905 medical examinations between 2001 and 2016. PM 2.5 exposure was estimated at each participant’s address using a satellite data-based spatiotemporal model with 1 km 2 resolution. Information on habitual PA and a wide range of covariates was collected using a standard self-administered questionnaire. We used the Cox regression model with time-dependent covariates to examine the joint associations. Results: The mean age of all observations was 41.7 years, and 48.8% were male. The mean value for systolic and diastolic blood pressure was 112.5 and 68.7mm Hg, respectively. Approximately 34.2% of all observations were inactive (0 metabolic equivalence values-hours), 29.8% had moderate-PA (median [interquartile range]; 3.75 [3.38 to 4.38] metabolic equivalence values-hours), and 36.0% had high-PA (15.7 [10.3 to 24.8] metabolic equivalence values-hours). The mean±SD of PM 2.5 was 26.1±7.3 μg/m 3 . The prevalence of cardiovascular disease, diabetes mellitus, and cancer was 2.1%, 2.9%, and 1.5%, respectively. After adjusting for a wide range of covariates (including a mutual adjustment for PA or PM 2.5 ), a higher PA level was associated with a lower risk of hypertension (hazard ratio [HR] for the moderate- and high-PA was 0.93 [95% CI, 0.89–0.97] and 0.92 [95% CI, 0.88–0.96], respectively, as compared with the inactive-PA), whereas a higher level of PM 2.5 was associated with a higher risk of hypertension (HR for the moderate- and high-PM 2.5 was 1.37 [95% CI, 1.32–1.43] and 1.92 [95% CI, 1.81–2.04], respectively, as compared with the low-PM 2.5 group]. No significant interaction was observed between PA and PM 2.5 (HR 1.01 [95% CI, 1.00–1.02]). Conclusions: A high-PA and low PM 2.5 exposure were associated with a lower risk of hypertension. The negative association between PA and hypertension remained stable in people exposed to various levels of PM 2.5 , and the positive association between PM 2.5 and hypertension was not modified by PA. Our results indicated that PA is a suitable hypertension prevention strategy for people residing in relatively polluted regions.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

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.

Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM

Summer-spring predominance of tuberculosis (TB) has been widely reported. The relative contributions of exogenous recent infection versus endogenous reactivation to such seasonality remains poorly understood. Monthly TB notifications data between 2005 and 2017 in Hong Kong involving 64,386 cases (41% aged ≥ 65; male-to-female ratio 1.74:1) were examined for the timing, amplitude, and predictability of variation of seasonality. The observed seasonal variabilities were correlated with demographics and clinical presentations, using wavelet analysis coupled with dynamic generalised linear regression models. Overall, TB notifications peaked annually in June and July. No significant annual seasonality was demonstrated for children aged ≤ 14 irrespective of gender. The strongest seasonality was detected in the elderly (≥ 65) among males, while seasonal pattern was more prominent in the middle-aged (45–64) and adults (30–44) among females. The stronger TB seasonality among older adults in Hong Kong suggested that the pattern has been contributed largely by reactivation diseases precipitated by defective immunity whereas seasonal variation of recent infection was uncommon.

Shopping malls in Hong Kong are usually located near major roads. Indoor air quality (IAQ) in these buildings is subject to infiltration of outdoor traffic-related pollutants, such as PM10, PM2.5, CO, and NO2. Furthermore, the existence of indoor sources and building geometry added to the complexity of variations in IAQ. To understand outdoor infiltration and spatial heterogeneity of these pollutants, we conducted fixed and cruise indoor sampling, together with simultaneous outdoor measurements, in a typical mall in Hong Kong. The cruise sampling was conducted indoors on a predesigned route and repeated 15 times. Outdoor infiltration was quantified based on regression analysis between indoor and outdoor sampling. Results showed that 75% of PM2.5, 53% of PM10, and 59% of NO2 were infiltrated into the mall during opening hours. Elevated PM2.5 and CO were observed during the dinner period, suggesting an impact from cooking. Substantial spatial variations were observed for PM10, PM2.5, and NO2, particularly at locations near entrances and restaurants. Measures are needed to reduce pollution intrusion from building openings and cooking-related sources to improve air quality in the selected mall. Fixed and cruise sampling methods used in this study provide insights on sensor deployment for future air quality monitoring in buildings.

Photochemical regime for ozone (O

Sensor technology has enabled the development of portable low-cost monitoring kits that might supplement many applications in conventional monitoring stations. Despite the sensitivity of electrochemical gas sensors to environmental change, they are increasingly important in monitoring polluted microenvironments. The performance of a compact diffusion-based Personal Exposure Kit (PEK) was assessed for real-time gaseous pollutant measurement (CO, O3, and NO2) under typical environmental conditions encountered in the subtropical city of Hong Kong. A dynamic baseline tracking method and a range of calibration protocols to address system performance were explored under practical scenarios to assess the performance of the PEK in reducing the impact of rapid changes in the ambient environment in personal exposure assessment applications. The results show that the accuracy and stability of the ppb level gas measurement is enhanced even in heterogeneous environments, thus avoiding the need for data post-processing with mathematical algorithms, such as multi-linear regression. This establishes the potential for use in personal exposure monitoring, which has been difficult in the past, and for reporting more accurate and reliable data in real-time to support personal exposure assessment and portable air quality monitoring applications.

The annual assessment of emission control effects on air quality is essential for policy adjustments. However, this assessment is difficult as the inter‐annual changes in pollution are impacted by complex meteorological conditions. In this study, based on our wind‐pollution decomposition (WPD) method, which decomposes wind effects (wind‐driven) and nonwind effects, we established the meteorology‐pollution decomposition (MPD) method, which separates meteorological effects (met‐driven) and nonmeteorological effects by importing other meteorological parameters, to approximate the emission change effects (emission‐driven) by the nonmeteorological effects. The performance of the MPD method was assessed by comparing the results of the WPD and MPD methods at multiple representative stations in Hong Kong with the longest continuous data available in China from 2000 to 2018. The decomposed emission‐driven impact is also validated by using the gross domestic product growth rates data. The emission‐driven contributions determined by the WPD and MPD methods generally show agreement in trend, while the met‐driven effects are generally larger than the wind‐driven effects with the same or opposite trend. The results showed that the met‐driven effect from the MPD method is more reasonable than the wind‐driven effect from the WPD method in representing complex meteorological influences, which indicates a better representation of emission‐driven impacts by the nonmeteorological effects during the unusual meteorological years, such as El Niño‐Southern Oscillation events. The MPD method represents an independent approach, which is based only on regular observations, to quantify the long‐term trend in emission‐driven impacts as well as the integrated meteorological impacts on the atmospheric environment under the background of climate change.

Retrofit on existing buildings for better energy performance is widely desired due to large share of building energy consumption. Meanwhile, the importance of thermal comfort and air pollution exposure has attracted increasing attention for occupant health, productivity and sustainable development. Heat, ventilation and air conditioning (HVAC) systems are responsible for a substantial proportion of energy use in buildings and are closely related to indoor environment quality. This study examined the energy consumption and indoor environment in a commercial office building with a retrofitted HVAC system. The retrofitting measures included a sensor-based building management system, dehumidification of outdoor air, and a two-stage particle filtration system. Energy data were collected before and after the retrofit. Field measurements were conducted in both winter and summer to evaluate the thermal comfort and indoor exposure to air pollutants in the retrofitted area. An experiment was designed to assess the benefits of upgraded filters on exposure to ambient particles during summer. By combining all of these measures, the retrofitted HVAC system was able to reduce energy use by 50% while maintaining generally acceptable indoor thermal comfort. Most of the time, the indoor particle levels complied with the World Health Organization's guidelines. The upgraded filtration system with a pleated filter reduced outdoor PM ingress by 30% to 60% more than the aluminum filter used before the retrofit. Co-benefit assessment provides insights into sustainability in building development during a retrofit by holistically examining energy use and the environment.

Many countries dedicated in mitigation of air pollution in the past several decades. However, little is known about how air quality improvement affects health. Therefore, we conducted current study to investigate dynamic changes in long-term exposure to ambient particulate matter (PM 2.5 ) and incidence of hypertension in a large longitudinal cohort. We recruited 134 978 adults aged 18 years or above between 2001 and 2014. All the participants received a series of standard medical examinations, including measurements of blood pressure. The PM 2.5 concentration was estimated using a satellite-based spatiotemporal model at a high resolution (1×1 km 2 ). The change in long-term exposure to PM 2.5 (ΔPM 2.5 ) was defined as the difference between the values measured during follow-up and during the immediately preceding visit, and a negative value indicated an improvement in PM 2.5 air quality. Time-varying Cox model was used to examine the associations between ΔPM 2.5 and the development of hypertension. The results show that PM 2.5 concentrations increased in 2002, 2003, and 2004, but began to decrease in 2005. Every 5 µg/m 3 change in exposure to PM 2.5 (ie, a ΔPM 2.5 of 5 µg/m 3 ) was associated with a 16% change in the incidence of hypertension (hazard ratio, 0.84; 95% CI, 0.82–0.86). Both stratified and sensitivity analyses generally yielded similar results. We found that an improvement in PM 2.5 exposure is associated with a decreased incidence of hypertension. Our findings demonstrate that air pollution mitigation is an effective strategy to reduce the risk of cardiovascular disease.

The below-cloud washout (BCW) effect on PM2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h−1 to 60 mm h−1. The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10−5 to 10−4 s−1 when the rain intensity ranges from 1 to 40 mm h−1. After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM2.5 simulation during rainy days. The BCW effect can clean around 10–40% of the PM2.5 over our study region, which can help to reduce the PM2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM2.5 concentration, but it cannot be the key method to reduce the PM2.5 level to the standard during episodic cases (e.g., >200 μg/m3).

During China's 11th Five Year Plan (FYP) and 12th FYP (2006–2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 μg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 μg/m3 in 2006 to 2.6, 3.1 and 4.1 μg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 μg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

ObjectivesAnimal experiments indicate that exposure to particulate matter (PM) can induce hepatotoxic effects but epidemiological evidence is scarce. We aimed to investigate the associations between long-term exposure to PM air pollution and liver enzymes, which are biomarkers widely used for liver function assessment.MethodsA cross-sectional analysis was performed among 351 852 adult participants (mean age: 40.1 years) who participated in a standard medical screening programme in Taiwan. Aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) levels were measured. A satellite-based spatio-temporal model was used to estimate the concentrations of ambient fine particles (PM with an aerodynamic diameter ≤2.5 µm, PM2.5) at each participant’s address. Linear and logistic regression models were used to investigate the associations between PM2.5 and the liver enzymes with adjustment for a wide range of potential confounders.ResultsAfter adjustment for confounders, every 10 µg/m3 increment in 2-year average PM2.5 concentration was associated with 0.02%(95% CI: −0.04% to 0.08%), 0.61% (95% CI: 0.51% to 0.70%) and 1.60% (95% CI: 1.50% to 1.70%) increases in AST, ALT and GGT levels, respectively. Consistently, the odds ratios of having elevated liver enzymes (>40 IU/L) per 10 µg/m3 PM2.5 increment were 1.06 (95% CI: 1.04 to 1.09), 1.09 (95% CI: 1.07 to 1.10) and 1.09 (95% CI: 1.07 to 1.11) for AST, ALT and GGT, respectively.ConclusionsLong-term exposure to PM2.5 was associated with increased levels of liver enzymes, especially ALT and GGT. More studies are needed to confirm our findings and to elucidate the underlying mechanisms.

Background Evidence of the effects of long-term exposure to fine particulate matter (PM2.5) air pollution on the development of dyslipidaemia is limited. This study aimed to investigate the association between long-term exposure to ambient PM2.5 and incident dyslipidaemia in a large cohort. Methods We studied 66,702 participants aged ≥18 years belonging to a cohort from a standard medical examination programme conducted in Taiwan between 2001 and 2014. The PM2.5 concentration at each participant's address was estimated using a satellite-based spatiotemporal model at a high resolution (1 km2). A time-varying Cox regression model was used to examine the association between long-term exposure to ambient PM2.5 and the development of dyslipidaemia. Additionally, sensitivity analyses were conducted to examine the stability of these associations. Results Compared with participants exposed to the 1st tertile of PM2.5, participants exposed to the 2nd and 3rd tertiles of PM2.5 had respective hazard ratios of 1.02 [95% confidence interval (CI): 0.98–1.06] and 1.08 (95%CI: 1.04–1.13) for incident dyslipidaemia. Sensitivity analyses generally yielded similar results. Conclusion Long-term exposure to ambient PM2.5 is associated with a higher risk of dyslipidaemia. Global strategies for reducing air pollution are needed to prevent the development of dyslipidaemia.

Automatic Identification System (AIS) data have been widely used to estimate ship emission inventories. However, although the AIS dataset can provide high spatial and temporal ship activities, the estimation of ship emission inventory requires vessel information that may not always be available in the vessel parameter database (e.g. engine power). Hence, earlier AIS-based emission inventory studies mainly included vessels with engine specifications (referred to as identified vessels); the significance of emissions from vessels without engine or other pertinent specifications (referred to as unidentified vessels) has not been studied in detail. In this study, we develop an emission inventory of all vessels with installed AIS instruments, including both identified and unidentified vessels. In particular, missing ship parameters for unidentified vessels are estimated through categorical regression with vessels with similar ship types and sizes in the AIS database. Using this approach, we estimate the ship emission inventory for the Pearl River Delta (PRD) in 2015. Our results show that unidentified vessels contributed significantly, accounting for almost 49% of the overall ship emissions of CO2, NOx, CO, and hydrocarbon in the PRD. Overall, we find that identified vessels contributed more over the open ocean and near port areas, whereas unidentified vessels contributed significantly along inland waterways with clear emission tracks and hot spots.

Variations in aerosol characteristics play an essential role in satellite remote sensing of PM2.5 concentrations. The lack of measurement of aerosol characteristics, however, limits the assessment of their effects. This study presented an observation-based model that directly considered the effects of aerosol characteristics. In this model, we used an integrated humidity coefficient (γ') and an integrated reference value ( K) to delineate the effects of aerosol characteristics. We then investigated the effects of the long-term variations in aerosol characteristics on satellite remote sensing of PM2.5 concentration in Hong Kong from 2004 to 2012. The results show that the γ' value peaked in 2009 because the percentages of highly hygroscopic components (e.g., sulfate and nitrate) in aerosols reached their peaks. The K value increased from 2004 to 2011 because of the increasing percentages of strong light-extinction components (e.g., organic matter) and the decreasing fine mode fraction in aerosols. The accuracy of PM2.5 retrieval improved greatly after accounting for the long-term variations in aerosol characteristics (e.g., correlation coefficient increased from 0.56 to 0.80). The results underscore the need to incorporate the variations in aerosol characteristics in the PM2.5 estimation models.

The health effects of atmospheric particulate matter (PM) have become a major environmental concern in urban areas. Most PM studies are mainly designed to measure the “ambient” or “emitted” concentrations of PM. Some studies are specifically designed to address exposure to PM for pedestrians and/or commuters on-board vehicles or at bus stops, but less attention is paid to the exposure during physical exercise such as jogging. To this end, concentrations of both fine particulate matter (PM2.5) and black carbon (BC) were measured along three jogging trails in the densely populated city Macau in China. The three jogging trails include the campus of University of Macau (UM), Guia Municipal Park (GP), and Saivan Lake (SL). In our measurements, PM2.5 and BC ranged from 2.9 to 84.1 and 0.4–19.5 μg/m³, respectively. BC/PM2.5 ratio ranged from 0.016 to 0.448. Among all three jogging trails, the highest BC concentration was found at SL (19.5 μg/m³), and the highest PM2.5 concentration was found at UM (84.1 μg/m³). On the contrary, the BC and PM2.5 concentrations at the elevated (about 50 m above sea level) GP trail were lower than those at the other two jogging trails. BC and PM2.5 concentrations were generally lower in the night loops (21:30–23:00) than those in the morning loops (07:30–09:00), which coincide with morning rush hours, with only a few exceptions. The difference in geographical locations also affects the BC and PM2.5 concentrations measured, with locations near bus terminals, busy roads, or with congested street canyons having higher concentrations. Doses of BC and PM2.5 after 60 min of exposure during typical jogging exercise are also estimated to evaluate the exposure to PM pollution at these three jogging trails when exercising. The results from the current studies provide information both on personal choice for the time/venue for jogging exercise and on future abatement policy to mitigate such risks of exposure to BC and PM2.5.

In this study, the trend of PM2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM2.5 data. PM2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM2.5 concentration and mortality are analyzed. The PM2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.

Source apportionment of PM2.5 was performed using positive matrix factorization (PMF) based on chemical composition data from 24-h filters collected throughout the year of 2015 at two sites in Macao (TG and PE), where TG is a general station and PE is near the city's ferry terminal. The input data sets include major inorganic ions, organic carbon and elemental carbon, elements and organic tracers (non-polar organic compounds and sugar compounds). Eight factors were resolved, namely, (1) secondary sulfate formation process, (2) secondary nitrate formation process, (3) biomass burning, (4) industrial emissions, (5) residual oil combustion, (6) sea salt, (7) dust, and (8) vehicle + helicopter emissions. Among the resolved sources, secondary sulfate and secondary nitrate were the major PM2.5 contributors, contributing to 33% and 23% of PM2.5 mass annually. Vehicle + helicopter emissions contributed to 12%, followed by industrial emissions (10%). Back trajectory analysis and conditional probability function (CPF) analysis were performed on the PMF-resolved factor contributions to distinguish the local and regional sources. Industrial emissions and vehicle + helicopter emissions showed different air mass impact at the two sites. The CPF plots confirmed the site differences in the resolved vehicle + helicopter emissions and industrial emissions were consistent with the geographical features of Macao and known local emission sources. The role of organic tracers was examined by comparing PMF runs with and without the inclusion of organics, denoted PMForg and PMFt, respectively. The results showed that the same set of contributing sources were resolved while noticeable differences of absolute PM2.5 contributions were observed between PMForg and PMFt and lower mass closure was noted for PMFt. PMF runs without source-indicative organic tracers tend to produce higher estimations of source contributions from biomass burning and vehicle exhaust factors than that with the organics while underestimate secondary nitrate, industrial emissions, and dust. This work highlights the importance of organic tracers in achieving more accurate source apportionment.

Information on the associations of long-term exposure to fine particulate matter (with an aerodynamic diameter less than 2.5 μm; PM2.5) with the development of type 2 diabetes is scarce, especially for south-east Asia, where most countries are experiencing serious air pollution. This study aimed to investigate the long-term effects of exposure to ambient PM2.5 on the incidence of type 2 diabetes in a population of Taiwanese adults. A total of 147,908 participants without diabetes, at least 18 years of age, were recruited in a standard medical examination programme between 2001 and 2014. They were encouraged to take medical examinations periodically and underwent at least two measurements of fasting plasma glucose (FPG). Incident type 2 diabetes was identified as FPG ≥7 mmol/l or self-reported physician-diagnosed diabetes in the subsequent medical visits. The PM2.5 concentration at each participant’s address was estimated using a satellite-based spatiotemporal model with a resolution of 1 × 1 km2. The 2 year average of PM2.5 concentrations (i.e. the year of and the year before the medical examination) was treated as an indicator of long-term exposure to ambient PM2.5 air pollution. We performed Cox regression models with time-dependent covariates to analyse the long-term effects of exposure to PM2.5 on the incidence of type 2 diabetes. A wide range of covariates were introduced in the models to control for potential effects, including age, sex, education, season, year, smoking status, alcohol drinking, physical activity, vegetable intake, fruit intake, occupational exposure, BMI, hypertension and dyslipidaemia (all were treated as time-dependent covariates except for sex). Compared with the participants exposed to the first quartile of ambient PM2.5, participants exposed to the second, third and fourth quartiles of ambient PM2.5 had HRs of 1.28 (95% CI 1.18, 1.39), 1.27 (95% CI 1.17, 1.38) and 1.16 (95% CI 1.07, 1.26), respectively, for the incidence of type 2 diabetes. Participants who drank occasionally or regularly (more than once per week) or who had a lower BMI (

Along with rapid economic development in China in the past twenty years, air pollution has deteriorated significantly and then gradually improved afterwards. Emission changes in response to the economic development and control measures and their impacts to ambient pollution levels are thus of great scientific and policy-making implications. By using the eighteen-year PM10 speciation dataset that have been continuously collected at multiple stations over Hong Kong since 1998, we demonstrated a novel approach to identify long-term trends of local and non-local contributions to ambient PM10 in Hong Kong based on Positive Matrix Factorization source apportionment and backward trajectory cluster analysis. It is found that vehicle exhaust contribution had decreased from 9.9 μg·m−3 in 1998 to 3.4 μg·m−3 in 2015, corresponding to a 66% decrease. However, residual oil contribution increased threefold, which indicated ship emission needs to be primarily addressed in air pollution management in Hong Kong. The mostly non-local sources of secondary sulfate, industry, and coal / biomass burning all showed inverse V-shape trends during the eighteen years, indicating the dual roles of economic development and emission control in shaping the ambient pollution levels. Local contributions dominated for vehicle exhaust and aged sea salt, while non-local ones dominated for secondary sulfate, secondary nitrate, industry, and coal / biomass burning. Although with small fluctuations, non-local contributions consistently accounted for 63–70% of total PM10 during the eighteen years, similar as previous estimates using a different approach. Such a dominant non-local contribution highlights the importance of regional collaboration to reduce PM levels in Hong Kong.

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities.

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

Background Exercise may increase the inhalation and deposition of air pollutants, which may counteract its beneficial effects. We thus examined the combined effects of chronic exposure to fine particulate matter (PM2.5) and habitual exercise on the risk of death from cancer in Taiwan. Patients and methods A total of 384 128 adults (≥18 years of age) were recruited for a medical screening programme between 2001 and 2016, yielding 842 384 medical-examination records. All participants were followed up until 31 May 2019. Vital data were obtained from the National Death Registry of Taiwan and the ambient PM2.5 exposure was estimated using a satellite-based spatiotemporal model. Information on habitual exercise was collected using a standard self-administered questionnaire. The time-dependent Cox-regression model was used to evaluate the combined effects. Results A greater amount of habitual exercise was associated with lower risk of death from cancer, whilst a higher level of PM2.5 exposure was associated with a higher risk of death from cancer. The inverse associations of habitual exercise with death from cancer were not modified by chronic exposure to PM2.5. The participants in the group with a high level of exercise and a low level of PM2.5 exposure exhibited a 35% lower risk of death from cancer than those in the group with a low level of exercise and a high level of PM2.5 exposure (95% confidence interval: 28%, 42%). Conclusions Increased levels of exercise and reduced exposure levels of PM2.5 are associated with a lower risk of death from cancer. Habitual exercise reduces the risk of death from cancer regardless of the levels of chronic PM2.5 exposure. Our results indicate that habitual exercise is a suitable health-promotion strategy even for people who reside in moderately polluted regions.

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, −0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between −9.41 °C and −13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (, Graphical abstract Unlabelled Image, Highlights • A mechanism-based parameterisation scheme was developed for transmission rate. • Scale of population migration from Wuhan was incorporated. • Population density had a non-linear (linearity index, 0.9) impact on disease spread. • Transmission rate shows a negative and exponential temperature dependence. • COVID-19 mitigation in densely populated and cold regions will be a great challenge.