Long-term Exposure to Ozone and Sleep Disorders in Children: A Multicity Study in China.

Evidence regarding the link between long-term ambient ozone (O3) exposure and childhood sleep disorders is little. This study aims to examine the associations between long-term exposure to O3 and sleep disorders in children. We conducted a population-based cross-sectional survey, including 185,428 children aged 6 to 18 years in 173 schools across 14 Chinese cities during 2012 and 2018. Parents or guardians completed a checklist using Sleep Disturbance Scale for Children, and O3 exposure at residential and school addresses was estimated using a satellite-based spatiotemporal model. We used generalized linear mixed models to test the associations with adjustment for factors including socio-demographic variables, lifestyle, meteorology and multiple pollutants. Mean concentrations of O3, particulate matter with diameters ≤2.5 mm (PM2.5) and nitrogen dioxide (NO2) were 88.9 µg/m3, 42.5 µg/m3 and 34.4 µg/m3, respectively. O3 and NO2 concentrations were similar among provinces, while PM2.5 concentration varied significantly among provinces. Overall, 19.4% of children had at least one sleep disorder. Long-term exposure to O3 was positively associated with odds of sleep disorders for all subtypes. For example, each interquartile increment in home-school O3 concentrations was associated with a higher odds ratio for global sleep disorder, at 1.22 (95% confidence interval: 1.18, 1.26). Similar associations were observed for sleep disorder subtypes. The associations remained similar after adjustment for PM2.5 and NO2. Moreover, these associations were heterogeneous regionally, with more prominent associations among children residing in southeast region than in northeast and northwest regions in China. We concluded that long-term exposure to O3 is positively associated with risks of childhood sleep disorders. These associations varied by geographical region of China.

A novel approach for assessing the spatiotemporal trend of health risk from ambient particulate matter components: Case of Hong Kong.

The spatiotemporal assessment of health risk due to exposure to particulate matter (PM) components should be well studied because of the different toxicity among PM components. However, this research topic has long been overlooked. This study aimed to examine the spatiotemporal variability in ambient respirable PM (PM10) components associated inhalation carcinogenic and non-carcinogenic risk (ICR and INCR) in Hong Kong over 2015-2019. The land-use regression (LUR) approach was adopted to predict the spatial distribution of PM10 component concentrations for the period of 2015-2019, whereas the ICR and INCR values of PM10 components were also estimated using the classic health risk assessment method. Both concentration of PM10 and INCR of PM10 components showed a general decreasing trend, while ICR of PM10 components increased slightly over the study period. LUR-model-based spatial maps at 500 m × 500 m resolution revealed the important spatial variability in PM10 and its eleven components, and their associated ICR and INCR values. High pollution levels and high ICR and INCR of studied PM10 components were generally found in developed urban areas and along the road network. Despite the fact that the PM10 concentrations met the Hong Kong annual PM10 air quality objective of 50 µg/m3, there was still significant potential health risk from the studied PM10 components. This study highlights the importance of taking PM component concentrations and associated inhalation health risk as well as PM mass concentrations into account for the perspective of air quality management and protecting public health.

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research.

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

Associations of Particulate Matter Sizes and Chemical Constituents with Blood Lipids: A Panel Study in Guangzhou, China.

Existing evidence is scarce concerning the various effects of different PM sizes and chemical constituents on blood lipids. A panel study that involved 88 healthy college students with five repeated measurements (440 blood samples in total) was performed. We measured mass concentrations of particulate matter with diameters ≤ 2.5 µm (PM2.5), ≤1.0 µm (PM1.0), and ≤0.5 µm (PM0.5) as well as number concentrations of particulate matter with diameters ≤ 0.2 µm (PN0.2) and ≤0.1 µm (PN0.1). We applied linear mixed-effect models to assess the associations between short-term exposure to different PM size fractions and PM2.5 constituents and seven lipid metrics. We found significant associations of greater concentrations of PM in different size fractions within 5 days before blood collection with lower high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A (ApoA1) levels, higher apolipoprotein B (ApoB) levels, and lower ApoA1/ApoB ratios. Among the PM2.5 constituents, we observed that higher concentrations of tin and lead were significantly associated with decreased HDL-C levels, and higher concentrations of nickel were associated with higher HDL-C levels. Our results suggest that short-term exposure to PM in different sizes was deleteriously associated with blood lipids. Some constituents, especially metals, might be the major contributors to the detrimental effects.

The effect of urbanization and climate change on the mosquito population in the Pearl River Delta region of China.

The rising incidence of mosquito-borne diseases is a global concern. Changes in regional climate, due to urbanization and global greenhouse gas concentrations, may affect the ecology of mosquitoes and mosquito-transmitted pathogens. The effects of changes in climate on mosquito population dynamics are complex but critical and urgent for implementing more effective public health policies. This study quantified the impact of urbanization and global climate change on the population of the mosquito species, Culex quinquefasciatus, in the Pearl River Delta region in southern China-an area that has undergone substantial urbanization and is expected to experience changes in climate. This study employed a mechanistic model to simulate mosquito population dynamics under various greenhouse gas emission and land-cover change scenarios based on climate data provided by a state-of-the-art regional climate model. Our results show a 12.6% decrease in the annual mosquito population in newly urbanized areas and a 5.9% increase in the annual mosquito population in existing urban areas. Furthermore, changing climate conditions are projected to cause a 15-17% reduction in the total annual mosquito population; however, the change will not be uniform throughout the year. Peak months exhibit a reduction in population, whereas non-peak months show a significant increase. These findings suggest mosquito control strategies may need to be adjusted to respond to the impacts of urbanization and climate change on mosquito population dynamics to maximize effectiveness. Region specific, quantitative analyses of environmental impacts on mosquito-borne disease ecology, like this study, are needed to provide policy makers with a scientific reference to guide the formation of effective transmission intervention strategies.

Ambient PM1 air pollution, blood pressure, and hypertension: Insights from the 33 Communities Chinese Health Study.

No evidence exists concerning the association between blood pressure and ambient particles with aerodynamic diameter ≤ 1.0 µm (PM1), a major component of PM2.5 (≤ 2.5 µm) particles, and potentially causing more hazardous health effects than PM2.5. We aimed to examine the associations of blood pressure in adults with both PM1 and PM2.5 in China. In 2009, we randomly selected 24,845 participants aged 18-74 years from 33 communities in China. Using a standardized mercuric-column sphygmomanometer, we measured blood pressure. Long-term exposure (2006-08) to PM1 and PM2.5 were estimated using a spatial statistical model. Generalized linear mixed models were used to evaluate the associations between air pollutants and blood pressure and hypertension prevalence, controlling for multiple covariates. A 10-µg/m3 increase in PM1 was significantly associated with an increase of 0.57 (95% CI 0.31-0.83) mmHg in systolic blood pressure (SBP), 0.19 (95% CI 0.03-0.35) mmHg increase in diastolic blood pressure (DBP), and a 5% (OR=1.05; 95% CI 1.01-1.10) increase in odds for hypertension. Similar associations were detected for PM2.5. Furthermore, PM1-2.5 showed no association with blood pressure or hypertension. In summary, both PM1 and PM2.5 exposures were associated with elevated blood pressure levels and hypertension prevalence in Chinese adults. In addition, most of the pro-hypertensive effects of PM2.5 may come from PM1. Further longitudinal designed studies are warranted to validate our findings.

Source emission contributions to particulate matter and ozone, and their health impacts in Southeast Asia.

Southeast Asia has been experiencing severe air pollution due to its substantial local emissions and transboundary air pollution (TAP), causing significant health impacts. While literature focused on air pollution episodes in Southeast Asia, we have yet to fully understand the contributions of local emission sectors and TAP to air quality in the region annually. Herein we employed air quality modeling with the species tagging method to first assess the contributions of source sectors and locations to fine particulate matter (PM2.5) and ozone (O3) in Southeast Asia and to hence quantify the resultant health impacts. Our results show that air pollutant exposure was associated with âˆ¼ 900 thousand premature mortalities in Southeast Asia every year. Of which, 77 % and 23 % were due to local emissions and TAP in the region, respectively. âˆ¼ 87 % of the premature mortalities due to local emissions were induced by PM2.5 exposure, whereas the remaining were due to O3 exposure. PM2.5-related health impacts were dominated by industrial (45 %) and residential (17 %) emissions, and O3-related impacts were mainly due to biogenic (40 %) and road transport (24 %) emissions. Furthermore, the health impacts of TAP were particularly adverse in Brunei, East Timor, Singapore, Laos, and border regions.

Outdoor artificial light at night and incident cardiovascular disease in adults: A national cohort study across China.

Cardiovascular diseases (CVDs) become a major public health concern. Evidence concerning the effects of outdoor artificial light at night (ALAN) on CVD in adults is scarce. We aimed to investigate the extent to which outdoor ALAN could affect the risk of CVD over a exposure range. Data from the China Health and Retirement Longitudinal Study, a population-based longitudinal study, launched in 2011-2012 and follow up till 2018, covering 28 provinces, autonomous regions and municipalities across mainland China. This study included 14,097 adults aged ≥45 years. Outdoor ALAN exposure (in nanowatts per centimeters squared per steradian) within 500 m of each participant's baseline residence was obtained from satellite image data. CVD was defined from medical diagnosis. The population was divided into three groups based on outdoor ALAN exposure from low to high. Cox regression model was used to estimate the association between outdoor ALAN exposure and incident CVD with hazard ratios (HRs) and 95 % confidence intervals (CIs). The mean (SD) age of the cohort was 57.6 (9.1) years old and 49.3 % were males. Outdoor ALAN exposure of study participants ranged from 0.02 to 39.79 nW/cm2/sr. During 83,033 person-years of follow-up, 2190 (15.5 %) cases of CVD were identified. Both low (HR: 1.21; 95 % CI: 1.02-1.43) and high (HR: 1.23; 95 % CI: 1.04-1.46) levels of outdoor ALAN exposure group were associated with higher risk of CVD compared with intermediate levels of outdoor ALAN exposure group. Body mass index was a significant effect modifier in the association between outdoor ALAN and risk of CVD, with stronger effects among those who was overweight or obese. The findings of this study suggest that low and high outdoor ALAN exposure were associated with a higher risk for CVD. More attention should be given to the cardiovascular effects associated with outdoor ALAN exposure.

Analysis of the air quality in upper atmospheric boundary layer in a high-density city in Asia using 3-year vertical profiles measured by the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS).

Past studies focused on ground-level air quality, whereas air quality in upper atmospheric boundary layer (ABL) remains unclear due to lack of long-term and high time-resolution profile data. This study utilized the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS) to provide vertical profiles of aerosol backscatter coefficients and wind for three years (2019-2021), along with DustTrak to describe and analyze the characteristics of aerosols in the upper ABL in a high-density city in Asia (Hong Kong, China). It is the first study to assess the long-term record and spatial variations of upper-level aerosol in a high-density city using a LiDAR network. Results show an opposite diurnal profile of aerosol comparing between ground-level and upper ABL, which is different with the diurnal pattern observed in ground measurements (higher air pollutant concentration level in daytime and lower in nighttime). The co-location vertical wind measurements provided the explanation of the opposite diurnal patterns. The 3-year vertical profiles also show the significant spatial variation of vertical distribution of aerosol at different locations, whereas the temporal variations can be affected by various factors such as emissions and transboundary air pollution. Our episode analysis clearly demonstrated the capability of 3DREAMS to monitor transboundary air pollution with detailed information of horizontal transport and vertical convection. 3DREAMS is therefore proved as a suitable system for transboundary air pollution monitoring. Our findings provide a critical reference for atmospheric scientists and decisionmakers to understand transboundary air pollution.

The joint association of ambient air pollution and different sleep posture with mild obstructive sleep apnea: A study conducted at Taipei Sleep Center.

BACKGROUND: Growing evidence suggests the detrimental impact of supine position and air pollution on obstructive sleep apnea (OSA), as well as the potential benefits of nonsupine positions. However, their interaction effects on OSA remain unclear. OBJECTIVES: To evaluate the interaction effects of air pollution (NO2/PM2.5) and sleep position on OSA on additive and multiplicative scales. METHODS: This study included 3330 individuals. Personal exposure to air pollution was assessed using a spatiotemporal model. OSA was diagnosed through polysomnography. The associations of supine and nonsupine positions and air pollutants with mild-OSA and their interaction effects on mild-OSA. were explored through generalized logistic regression. RESULTS: Supine position and high NO2 level independently increased the risk of mild-OSA, while PM2.5 was not associated with mild-OSA. Significant interactions were observed between supine position and NO2 at different lag periods (0-7 days, 0-1 year, and 0-2 years) (P = 0.042, 0.013, and 0.010, respectively). The relative excess risks due to interactions on the additive scale for 1-week, 1-year, and 2-year NO2 exposure and supine position were 0.63 (95 % CI: 0.10-1.16), 0.56 (95 % CI: 0.13-0.99), and 0.64 (95 % CI: 0.18-1.10); the corresponding odds ratios for interactions on the multiplicative scale were 1.45 (95 % CI: 1.01-2.07), 1.55 (95 % CI: 1.09-2.22), and 1.60 (95 % CI: 1.12-2.28). The positive interactions persisted in men and participants with obesity. No interaction was observed between nonsupine position and NO2 levels; nevertheless, significant interactions were noted on both the negative additive and multiplicative scales in men. CONCLUSION: Prolonged supine sleep significantly increased the risk of mild-OSA, particularly in men and individuals with obesity. Although the benefits of nonsupine position are considerably less than the risks of NO2 exposure, avoiding prolonged supine sleep may reduce the risk of mild-OSA caused by high levels of NO2 in men.

Air pollution exacerbates mild obstructive sleep apnea by disrupting nocturnal changes in lower-limb body composition: a cross-sectional study conducted in urban northern Taiwan.

BACKGROUND: Few studies have explored the role of body composition linking air pollution to obstructive sleep apnea (OSA). OBJECTIVE: To estimate the effects of air pollution on body composition and OSA, and that of body composition on OSA. METHODS: This study included 3550 individuals. A spatiotemporal model estimated personal exposure. Nocturnal changes in body composition were assessed through bioelectric impedance analysis. OSA was diagnosed using polysomnography. A generalized linear model was used to evaluate the absolute nocturnal changes in body composition associated with an interquartile range (IQR) increase in pollutants. A generalized logistic model was used to estimate odds ratios (ORs) of mild-OSA compared to non-OSA. Association between body composition and apnea-hypopnea index (AHI) was investigated through partial least squares (PLS) regression. RESULTS: Nocturnal changes in lower-limb body composition were associated with NO2 and PM2.5 in all patients. In participants with AHI <15, both short- and long-term NO2 exposures affected body composition and mild-OSA, while PM2.5 was not associated with either outcome. In a PLS model incorporating eight NO2-associated lower-limb parameters, the variable importance projection scores (VIP) of left leg impedance (LLIMP), predicted muscle mass (LLPMM), fat-free mass (LLFFM), and right leg impedance (RLIMP) exceeded 1; the corresponding coefficients ranked in the top four for AHI prediction. The adjusted OR (mild vs. non-OSA) was 1.67 (95 % CI: 1.36-2.03) associated with an IQR increase in prediction value estimated from body compositions. Notably, the two-pollutant model investigating the effects of pollutants on body compositions revealed associations of four parameters (LLIMP, LLPMM, LLFFM, and RLIMP) with NO2 in all lags, which indicates their indispensability in the association between NO2 and AHI. CONCLUSIONS: NO2 exacerbates mild-OSA by disrupting nocturnal changes in lower-limb body composition of patients with AHI <15. PM2.5 was associated with nocturnal changes in lower-limb body composition but not with mild-OSA.

Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China.

BACKGROUND: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours. METHODS: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May-September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326-80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed. FINDINGS: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2-4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6-85.3] µg/m3 vs 41.6 [37.3-46.5] µg/m3), kitchen (102.4 [90.4-116.0] µg/m3 vs 52.3 [44.8-61.2] µg/m3) and living room (62.1 [57.3-67.3] µg/m3 vs 41.0 [37.1-45.3] µg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 µg/m3 to 700-1200 µg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64-0.66) and kitchen (0.52-0.59) levels, but only weakly correlated with community levels, especially in summer (0.15-0.34) and among solid fuel users (0.11-0.31). CONCLUSION: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5.

What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018.

Ambient PM2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM2.5, this study quantified the spatiotemporal dynamics of premature mortality associated with PM2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM2.5-attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM2.5-attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM2.5-related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM2.5-caused premature mortality decreased by 54 K, in which changes in PM2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM2.5 pollution in the country.

Association of traffic air pollution with severity of obstructive sleep apnea in urban areas of Northern Taiwan: A cross-sectional study.

BACKGROUND: Although recent studies have indicated an association between obstructive sleep apnea (OSA) and air pollution, they have reported inconsistent results. Moreover, few studies investigated the effects of short-term air pollution exposure. OBJECTIVE: To estimate the health effects of short- and long-term exposure to traffic air pollution on mild OSA in Taipei. METHODS: We collected participants' data from Taipei Sleep Center and air pollution data from Taiwan Environmental Protection Administration. A spatiotemporal model was used to estimate the individual exposure level. Generalized linear models were used to assess the percent change of overall apnea-hypopnea index (AHI), AHI in rapid eye movement period (AHI-REM), AHI in non-REM (AHI-NREM), and oxygen desaturation index (ODI) associated with an interquartile (IQR) increase in personal pollution exposure. A generalized logistic model was used to estimate the ORs of different severities of OSA compared with the reference group. RESULTS: In the patients with AHI of <15, both short- and long-term exposure to NO2 were significantly associated with AHI and ODI increases: an IQR increase in 2-year mean NO2 increased 7.3% of AHI and 8.4% of ODI; these values were the highest among all exposure windows. The effects of NO2 on AHI increase were stronger in the men and younger patients. Moreover, the association between AHI and NO2 in the patients with AHI of <15 was mediated by the REM stage. NO2 exposure was associated with an increased risk of mild OSA that reached up to 24.8% per IQR increase in NO2 averaged over 2 years. PM2.5 exerted no effects on AHI, but an IQR increase in 1-year and 2-year mean PM2.5 was associated with 6.8% and 8.8% increases in ODI, respectively. CONCLUSIONS: Both short- and long-term exposure to traffic air pollution were associated with the risk of mild OSA, which was modified by REM stage.

Association of air pollution exposure with low arousal threshold obstructive sleep apnea: A cross-sectional study in Taipei, Taiwan.

Emerging evidence witnesses the association of air pollution exposure with sleep disorders or the risk of obstructive sleep apnea (OSA); however, the results are not consistent. OSA patients with or without a low arousal threshold (LAT) have different pathology and therapeutic schemes. No study has evaluated the potential diverse effects of air pollution on the phenotypes of OSA. The current study aimed to evaluate the associations of short-term and long-term exposure to air pollution with sleep-disordered measures and OSA phenotypes. This cross-sectional study consisted of 4634 participants from a sleep center in Taipei from January 2015 to April 2019. The personal exposure to ambient PM2.5 and NO2 was assessed by a spatial-temporal model. Overnight polysomnography was used to measure the sleep parameters. According to a developed clinical tool, we defined the low arousal threshold (LAT) and identified the OSA patients with or without LAT. We applied a generalized linear model and multinomial logistic regression model to estimate the change of sleep measures and risk of the OSA phenotypes, respectively, associated with an interquartile range (IQR) increment of personal pollution exposure after adjusting for the essential confounders. In the single-pollutant model, we observed the associations of NO2 with sleep-disordered measures by decreasing the total sleep time, sleep efficiency, extending the time of wake after sleep onset, and the association of NO2 with the increased risk of LAT OSA by around 15%. The two-pollutant model with both long-term and short-term exposures confirmed the most robust associations of long-term NO2 exposure with sleep measures. An IQR increment of NO2 averaged over the past year (6.0 ppb) decreased 3.32 min of total sleep time and 0.85% of sleep efficiency. Mitigating exposure to air pollution may improve sleep quality and reduce the risk of LAT OSA.

Inflammatory and oxidative stress responses of healthy elders to solar-assisted large-scale cleaning system (SALSCS) and changes in ambient air pollution: A quasi-interventional study in Xi'an, China.

An outdoor solar assisted large-scale cleaning system (SALSCS) was constructed to mitigate the levels of fine particulate matter (PM2.5) in urban areas of Xi'an China, providing a quasi-experimental opportunity to examine the biologic responses to the changes in pollution level. We conducted this outdoor SALSCS based real-world quasi-interventional study to examine the associations of the SALSCS intervention and changes in air pollution levels with the biomarkers of systemic inflammation and oxidative stress in healthy elders. We measured the levels of 8-hydrox-2-deoxyguanosine (8-OHdG), Interlukin-6 (IL-6), as well as tumor necrosis factor alpha (TNF-α) from urine samples, and IL-6 from saliva samples of 123 healthy retired participants from interventional/control residential areas in two sampling campaigns. We collected daily 24-h PM2.5 samples in two residential areas during the study periods using mini-volume samplers. Data on PM10, gaseous pollutants and weather factors were collected from the nearest national air quality monitoring stations. We used linear mixed-effect models to examine the percent change in each biomarker associated with the SALSCS intervention and air pollution levels, after adjusting for time trend, seasonality, weather factors and personal characteristics. Results showed that the SALSCS intervention was significantly associated with decreases in the geometric mean of biomarkers by 47.6% (95% confidence interval: 16.5-67.2%) for 8-OHdG, 66% (31.0-83.3%) for TNF-α, 41.7% (0.2-65.9%) and 43.4% (13.6-62.9%) for urinary and salivary IL-6, respectively. An inter-quartile range increase of ambient PM2.5 exposure averaged on the day of the collection of bio-samples and the day before (34.1 µg/m3) was associated, albeit non-significantly so, with 22.8%-37.9% increases in the geometric mean of these biomarkers. This study demonstrated that the SALSCS intervention and decreased ambient air pollution exposure results in lower burden of systemic inflammation and oxidative stress in older adults.

Effectiveness of indoor air purification intervention in improving cardiovascular health: A systematic review and meta-analysis of randomized controlled trials.

Indoor air purifiers are increasingly marketed for their health benefits, but their cardiovascular effects remain unclear. We systematically reviewed and meta-analysed randomized controlled trials (RCTs) on the cardiovascular effects of indoor air purification interventions in humans of all ages. We searched Embase, Medline, PubMed, and Web of Science from inception to 22 August 2020. Fourteen cross-over RCTs (18 publications) were included. Systolic blood pressure (SBP) was significantly reduced after intervention (-2.28 (95% CI: -3.92, -0.64) mmHg). There were tendencies of reductions in diastolic blood pressure (-0.35 [-1.52, 0.83] mmHg), pulse pressure (PP) (-0.86 [-2.07, 0.34] mmHg), C-reactive protein (-0.23 [-0.63, 0.18] mg/L), and improvement in reactive hyperaemia index (RHI) (0.10 [-0.04, 0.24]) after indoor air purification, although the effects were not statistically significant. However, when restricting the analyses to RCTs using physical-type purifiers only, significant improvements in PP (-1.56 [-2.98, -0.15] mmHg) and RHI (0.13 [0.01, 0.25]) were observed. This study found potential evidence on the short-term cardiovascular benefits of using indoor air purifiers, especially for SBP, PP and RHI. However, under the Grading of Recommendations Assessment, Development and Evaluation framework, the overall certainty of evidence was very low, which discourage unsubstantiated claims on the cardiovascular benefits of air purifiers. We have also identified several key methodological limitations, including small sample size, short duration of intervention, and the lack of wash-out period. Further RCTs with larger sample size and longer follow-up duration are needed to clarify the cardiovascular benefits of air purification interventions.

Association of cardiorespiratory hospital admissions with ambient volatile organic compounds: Evidence from a time-series study in Taipei, Taiwan.

As important precursors of ozone and secondary organic aerosols, the harmful impact of exposure to ambient volatile organic compounds (VOCs) is of public health interest. However, few studies have investigated the health risks of numerous individual VOC species. This study linked the daily concentrations of 54 C2-C11 VOC species monitored from the Wanhua Photochemical Assessment Monitoring Station and hospital admissions for cardiorespiratory diseases in Taipei, Taiwan, from the National Health Insurance Research Database. A standard time-series approach entailing a series of sensitivity analyses was applied to investigate the short-term health risks of exposure to VOC subgroups and species. Consistent associations of all VOC subgroups and main species with chronic obstructive pulmonary disease (COPD) hospitalizations were demonstrated. In addition, associations of the C5-C6 alkanes, C2-C3 alkenes, toluene, and xylene with asthma hospitalizations were found, as were associations of aromatic hydrocarbons with hospitalizations for heart failure. An interquartile range increase in total VOC exposure at lag0 day (102.6 parts per billion carbon) was associated with increments of 1.84% (95% confidence interval: 0.54%-3.15%), 1.65% (0.71%-2.60%), and 1.21% (0.36%-2.07%) in hospitalizations for asthma, COPD, and heart failure, respectively. The effect estimates were robust with data excluding extreme values, the second pollutant adjustment for PM2.5 and O3, and the Bonferroni correction. The associations of ambient VOC exposure with cardiorespiratory hospitalizations in Taipei serve as a reference for VOC regulations and ozone control strategies.

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods.

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM2.5 concentration in residential households. PM2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R2 and cross-validation R2 values of 0.93 and 0.65, respectively. Outdoor PM2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R2 value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

Association of ambient ozone with pneumonia hospital admissions in Hong Kong and Taipei: A tale of two Southeast Asian cities.

Ozone (O3) is a reactive oxidant exerting both inflammatory and oxidative damages to the respiratory system. With the ground-level O3 progressively increasing in the past decade, the reevaluation of the pneumonia hospitalization risk from exposure to O3 is of public health interest. We conducted an ecological time-series study to examine the city-specific association between short-term O3 exposure and pneumonia hospitalizations in Hong Kong and Taipei, respectively. We linked the daily pneumonia hospitalization count to air pollution concentrations and weather conditions according to the date of admission during 2010-2017. We applied a generalized additive distributed lag model to examine the association while adjusting for time-varying covariates. Stratified analysis by age group and the potential harvesting effect of O3 were evaluated. We observed the harvesting effects of O3 on pneumonia hospitalizations in children in both cities and adults in Taipei. The short-term effect of O3 lasted for around one week. An interquartile range (IQR) increment of daytime 8-hour mean concentration of O3 distributed over 0-6 lag days in Hong Kong (42.4 µg/m3) was associated with a 7.04% (95% CI: 5.35-8.76%) increase in hospital admissions for elderly pneumonia, while the corresponding cumulative excess risk per IQR increment of O3 in Taipei (38.7 µg/m3) was 3.41% (95% CI: 1.63-5.22%). Different O3 metrics, varying degrees of freedom for filtering the temporal trend, and three-pollutant models supported the robustness of the associations. We concluded that short-term O3 exposure was associated with pneumonia hospitalizations in the elderly population. Understanding the pneumonia hospitalization risk of O3 will help to inform public health policies in the planning of ozone control strategies and intervention measures to prevent ozone-related pneumonia in vulnerable elderly populations.