Molecular Imaging Reveals Two Distinct Mixing States of PM2.5 Particles Sampled in a Typical Beijing Winter Pollution Case.

Mixing states of aerosol particles are crucial for understanding the role of aerosols in influencing air quality and climate. However, a fundamental understanding of the complex mixing states is still lacking because most traditional analysis techniques only reveal bulk chemical and physical properties with limited surface and 3-D information. In this research, 3-D molecular imaging enabled by ToF-SIMS was used to elucidate the mixing states of PM2.5 samples obtained from a typical Beijing winter haze event. In light pollution cases, a thin organic layer covers separated inorganic particles; while in serious pollution cases, ion exchange and an organic-inorganic mixing surface on large-area particles were observed. The new results provide key 3-D molecular information of mixing states, which is highly potential for reducing uncertainty and bias in representing aerosol-cloud interactions in current Earth System Models and improving the understanding of aerosols on air quality and human health.

Association of long-term air pollution exposure with the risk of prediabetes and diabetes: Systematic perspective from inflammatory mechanisms, glucose homeostasis pathway to preventive strategies.

BACKGROUND: Limited evidence suggests the association of air pollutants with a series of diabetic cascades including inflammatory pathways, glucose homeostasis disorder, and prediabetes and diabetes. Subclinical strategies for preventing such pollutants-induced effects remain unknown. METHODS: We conducted a cross-sectional study in two typically air-polluted Chinese cities in 2018-2020. One-year average PM1, PM2.5, PM10, SO2, NO2, and O3 were calculated according to participants' residence. GAM multinomial logistic regression was performed to investigate the association of air pollutants with diabetes status. GAM and quantile g-computation were respectively performed to investigate individual and joint effects of air pollutants on glucose homeostasis markers (glucose, insulin, HbA1c, HOMA-IR, HOMA-B and HOMA-S). Complement C3 and hsCRP were analyzed as potential mediators. The ABCS criteria and hemoglobin glycation index (HGI) were examined for their potential in preventive strategy. RESULTS: Long-term air pollutants exposure was associated with the risk of prediabetes [Prevalence ratio for O3 (PR_O3) = 1.96 (95% CI: 1.24, 3.03)] and diabetes [PR_PM1 = 1.18 (95% CI: 1.05, 1.32); PR_PM2.5 = 1.08 (95% CI: 1.00, 1.16); PR_O3 = 1.35 (95% CI: 1.03, 1.74)]. PM1, PM10, SO2 or O3 exposure was associated with glucose-homeostasis disorder. For example, O3 exposure was associated with increased levels of glucose [7.67% (95% CI: 1.75, 13.92)], insulin [19.98% (95% CI: 4.53, 37.72)], HOMA-IR [34.88% (95% CI: 13.81, 59.84)], and decreased levels of HOMA-S [-25.88% (95% CI: -37.46, -12.16)]. Complement C3 and hsCRP played mediating roles in these relationships with proportion mediated ranging from 6.95% to 60.64%. Participants with HGI ≤ -0.53 were protected from the adverse effects of air pollutants. CONCLUSION: Our study provides comprehensive insights into air pollutant-associated diabetic cascade and suggests subclinical preventive strategies.

Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China.

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people's activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples' density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people's desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens' activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens' activities of daily living.

Associations and burdens of relative humidity with cause-specific mortality in three Chinese cities.

This study aimed to investigate the association between relative humidity (RH) and various cause of mortality, and then quantify the RH-related mortality fraction of low and high RH under the assumption that causal effects exist. Daily cause-specific mortality counts from 2008 to 2011, and contemporaneous meteorological data in three Chinese cities were collected. Distributed lag nonlinear models were adopted to quantify the nonlinear and delayed effects of RH on mortality risk. Low and high RH were defined as RH lower or higher than the minimum mortality risk RH (MMRH), respectively. Corresponding RH-related mortality fractions were calculated in the explanatory analysis. From the three cities, 736,301 deaths were collected. RH (mean ± standard deviation) were 50.9 ± 20.0 for Beijing, 75.5 ± 8.6 for Chengdu, and 70.8 ± 14.6 for Nanjing. We found that low RH in Beijing and high RH (about 80-90%) in Chengdu was associated with increased all-cause mortality risk. Both low and high RH may increase the CVD mortality risk in Beijing. Both low and high (about 80-85%) RH may increase the COPD mortality risk in Chengdu. Low RH (about < 45%) was associated with increased diabetes mortality risk in Nanjing. Effects of extreme low and extreme high RH were delayed in these cities, except that extreme low effects on COPD mortality appeared immediately in Chengdu. The effects of extreme low RH are higher than that of the extreme high RH in Beijing and Nanjing, while contrary in Chengdu. Finally, under the causal effect assumption, 6.80% (95% eCI: 2.90, 10.73) all-cause mortality and 12.48% (95% eCI: 7.17, 16.80) CVD deaths in Beijing, 9.59% (95% eCI: 1.38, 16.88) COPD deaths in Chengdu, and 23.79% (95% eCI: 0.92, 387.93) diabetes mortality in Nanjing were attributable to RH. Our study provided insights into RH-mortality risk, helped draw relative intervention policies, and is also significant for future predictions of climate change effects under different scenarios.

Quantitative Evaluation of Psychological Tolerance under the Haze: A Case Study of Typical Provinces and Cities in China with Severe Haze.

The interplay of specific weather conditions and human activity results due to haze. When the haze arrives, individuals will use microblogs to communicate their concerns and feelings. It will be easier for municipal administrators to alter public communication and resource allocation under the haze if we can master the emotions of netizens. Psychological tolerance is the ability to cope with and adjust to psychological stress and unpleasant emotions brought on by adversity, and it can guide human conduct to some extent. Although haze has a significant impact on human health, environment, transportation, and other factors, its impact on human mental health is concealed, indirect, and frequently underestimated. In this study, psychological tolerance was developed as a psychological impact evaluation index to quantify the impact of haze on human mental health. To begin, data from microblogs in China's significantly haze-affected districts were collected from 2013 to 2019. The emotion score was then calculated using SnowNLP, and the subject index was calculated using the co-word network approach, both of which were used as social media evaluation indicators. Finally, utilizing ecological and socioeconomic factors, psychological tolerance was assessed at the provincial and prefecture level. The findings suggest that psychological tolerance differs greatly between areas. Psychological tolerance has a spatio-temporal trajectory in the timeseries as well. The findings offer a fresh viewpoint on haze's mental effects.

Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing.

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 µg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 µg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

Associations between air pollutant exposure and renal function: A prospective study of older adults without chronic kidney disease.

We used real-world exposure scenarios to evaluate the effect of six ambient air pollutant (PM2.5, PM10, NO2, SO2, CO, and O3) exposure on renal function among older adults without chronic kidney disease (CKD). We recruited 169 older adults without CKD in Beijing, China, for a longitudinal study from 2016 to 2018. The Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (EPI) equations were employed to derive the estimated glomerular filtration rate (eGFR). A linear mixed-effects model with random intercepts for participants was employed to determine the effects of air pollutants on renal function evaluated on the basis of eGFR and urinary albumin/creatinine ratio at different exposure windows (1-, 2-, 3-, 5-, 7-, 14-, 28-, 45-, and 60-days moving averages). An interquartile range (IQR) increase in NO2 for was associated with significant decreases of in eGFR (MDRD equation) [percentage changes: -4.49 (95% confidence interval: -8.44, -0.37), -5.51 (-10.43, -0.33), -2.26 (-4.38, -0.08), -3.71 (-6.67, -0.65), -5.44 (-9.58, -1.11), -5.50 (-10.24, -0.51), -6.15 (-10.73, -1.33), and -6.34 (-11.17, -1.25) for 1-, 2-, 5-, 7-, 14-, 28-, 45-, and 60-days moving averages, respectively] and in eGFR (EPI equation) [percentage changes: -5.04 (-7.09, -2.94), -6.25 (-8.81, -3.62), -5.16 (-7.34, -2.92), -5.10 (-7.85, -2.28), -5.83 (-8.23, -3.36), -6.04 (-8.55, -3.47) for 1-, 2-, 14-, 28-, 45-, and 60-days moving averages, respectively]. In two-pollutant model, only the association of NO2 exposure with eGFR remained robust after adjustment for any other pollutant. This association was stronger for individuals with hypertension for the EPI equation or BMI <25 kg/m2 for the MDRD equation at lags 1 and 1-2. Our findings suggest that NO2 exposure is associated with eGFR reduction among older adults without CKD for short (1-, 2-days) and medium (14-, 28-, 45-, 60-days) term exposure periods; thus, NO2 exposure may contribute to renal impairment.

The effect of ambient ozone on glucose-homoeostasis: A prospective study of non-diabetic older adults in Beijing.

OBJECTIVE: To investigate potential effects of short- and medium-term exposure to low levels of ozone (O3) on glucose-homeostasis in non-diabetic older adults. METHODS: 166 non-diabetic, older participants in Beijing were deemed eligible to partake in this longitudinal population-based study. Observations were recorded on three separate occasions from November 2016 up until January 2018. Concentrations of outdoor O3 were monitored throughout the study period. Biomarkers indicative of glucose-homeostasis, including fasting blood glucose, insulin, HbAlc, glycated albumin percentage (glycated albumin/albumin), HOMA-IR and HOMA-B were measured at 3 sessions. A linear mixed effects model with random effects was adopted to quantify the effect of O3 across a comprehensive set of glucose-homeostasis markers. RESULTS: Short-term O3 exposure positively associated with increased fasting blood glucose, insulin, HOMA-IR and HOMA-B. The effect on glucose occurred at 3-, 5-, 6- and 7-days, although the largest effect manifested on 6-days (5.6%, 95% CI: 1.4, 9.9). Significant associations with both insulin and HOMA-IR were observed on the 3- and 4-days. For HOMA-B, positive associations were identified from 3- to 7-days with estimates ranging from 40.0% (95% CI: 2.3, 91.5) to 83.1% (95% CI: 25.3, 167.5). Stratification suggests that women may be more susceptible to short-term O3 exposure. There does not appear to be a significant association between O3 and glucose-homeostasis in medium-term exposures. CONCLUSIONS: In this study, we found that O3 exposure is at least partially associated with type II diabetes in older adults with no prior history of this condition. O3 therefore appears to be a potential risk factor, which is a particular concern when we consider the rise in global concentrations. Evidence also suggests that women may be more susceptible to short-term O3 exposure although we are not quite sure why. Future research may look to investigate this phenomenon further.

Polycyclic aromatic hydrocarbons in the soils of the Yangtze River Delta Urban Agglomeration, China: Influence of land cover types and urbanization.

With the development of urbanization, urban areas have become the main sources and sinks of polycyclic aromatic hydrocarbons (PAHs). The effects of human activities on the behaviors of PAHs in urban agglomerations have attracted significant attention. We collected soil samples (n = 330) to investigate the distribution, composition, and sources of 16 PAHs in the Yangtze River Delta Urban Agglomeration using the land resolution of 24 km × 24 km. The concentrations of Σ16PAHs ranged from 21 to 2034 ng/g, with a median value of 124 ± 338 ng/g. The concentrations of PAHs were highest in impervious surfaces (350 ± 352 ng/g), followed by grassland (259 ± 322 ng/g), cropland (254 ± 341 ng/g), forest (190 ± 303 ng/g), and water (68 ± 34 ng/g). PAHs were dominated by medium-molecular-weight components (4 rings PAHs), followed by PAHs with high-molecular-weight (5-6 rings PAHs) and low-molecular-weight (2-3 rings PAHs) components. Fluoranthene, benzo[a]anthracene and chrysene are three major pollutants in YRDUA. A positive matrix factorization model indicated that fossil fuel combustion, coal combustion and volatilization, vehicle emission, and biomass burning were the main sources of PAHs, contributing 36%, 29%, 22%, and 12% of PAH sources, respectively. Urbanization parameters were positively correlated with PAH concentrations. A land use regression (LUR) model integrated with urbanization parameters showed evidence of the strong relationship between measured PAHs and predicted PAHs. These findings together highlighted that land cover types and human activities intensively influenced the PAHs pollution in the highly urbanized zones.

Source specific PM2.5 associated with heart rate variability in the elderly with coronary heart disease: A community-based panel study.

There is increasingly concern that PM2.5 constituents play a significant role in PM2.5-related cardiovascular outcomes. However, little is known about the associations between specific constituents of PM2.5 and risk for cardiovascular health. To evaluate the exposure to specific chemicals of PM2.5 from various sources and their cardiac effects, a longitudinal investigation was conducted with four repeated measurements of elderly participants' HRV and PM2.5 species in urban Beijing. Multiple chemicals in PM2.5 (metals, ions and PAHs) were characterized for PM2.5 source apportionment and personalized exposure assessment. Five sources were finally identified with specific chemicals as the indicators: oil combustion (1.1%, V & PAHs), secondary particle (11.3%, SO42- & NO3-), vehicle emission (1.2%, Pd), construction dust (28.7%, Mg & Ca), and coal combustion (57.7%, Se & As). As observed, each IQR increase in exposure to oil combustion (V), vehicle emission (Pd), and coal combustion (Se) significantly decreased rMSSD by 13.1% (95% CI: -25.3%, -1.0%), 27.4% (95% CI: -42.9%, -7.6%) and 24.7% (95% CI: -39.2%, -6.9%), respectively, while those of PM2.5 mass with decreases of rMSSD by 11.1% (95% CI: -19.6%, -1.9%) at lag 0. Elevated exposures to specific sources/constituents of PM2.5 disrupt cardiac autonomic function in elderly and have more adverse effects than PM2.5 mass. In the stratified analysis, medication and gender modify the associations of specific chemicals from variable sources with HRV. The findings of this study provide evidence on the roles of influential constituents of ambient air PM2.5 and their sources in terms of their adverse cardiovascular health effects.

Investigating potential associations between O3 exposure and lipid profiles: A longitudinal study of older adults in Beijing.

BACKGROUND: Little information exists on the lipidemic effects of ozone exposure. Few studies have focused on the different patterns of the association among older adults population, and little attention has been given to comprehensive lipid indices when evaluating the effect of O3 exposure on the metabolism. METHODS: We conducted a longitudinal study involving 201 older adults in Beijing, China between 2016 and 2018. A mixed regression model was applied with random effects to investigate the relationship between O3 and lipid profiles. RESULTS: O3 exposure positively correlated with TC, LDL-C, CRI-I, CRI-II and AC at short-term and medium-term exposure periods. The largest increases in TC, LDL-C, CRI-I and CRI-II were found in the 28-days moving average indicating accumulative effects over prolonged exposure period. A 10 µg/m3 increase of O3 at the 28-days moving average was associated with a significant increase of 3.9% (95% CI: 1.0, 6.9) in TC, 8.2% (95% CI: 4.2, 12.4) in LDL-C, 4.8% (95% CI: 1.1, 8.5) in CRI-I and 7.0% (95% CI: 2.7, 11.5) in CRI-II. Stratification by health status and characteristics revealed different patterns of lipid changes among older adults, lipid status, age, sex and BMI may modify the relationship between O3 exposure and lipid profiles. CONCLUSIONS: Our findings suggest that short-term and medium-term O3 exposure is associated with lipid profiles abnormalities among the older adults. Evidence also suggests there are patterns within population which differ according to both health status and demographic characteristics.

Spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China.

Due to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10 µm in aerodynamic diameter (PM10) and nitrogen dioxide (NO2) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM10 and NO2, and meteorological factors from January 1, 2009, to December 31, 2010, in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM10 and NO2 on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM and the strongest effects were identified for 2-day moving average lag 0-1. For every 10 µg/m3 increases in concentrations of PM10 and NO2, the corresponding daily cardiovascular mortality increases in 0.31% (95%CI 0.15%, 0.46%) and 1.63% (95%CI 1.11%, 2.13%), respectively. The death risk associated with air pollutants varied across different geographic districts in Beijing. We found spatially varied adverse effects of air pollution on cardiovascular deaths in Beijing. But there was insufficient evidence to show the significant spatial heterogeneity in mortality effects of PM10 and NO2 in this study.

The mediated role of complement C3 in PM2.5 exposure and type 2 diabetes mellitus: an elderly panel study in Beijing, China.

Diabetes mellitus (DM) is a common chronic disease worldwide. Ambient air pollution has long been proven to be associated with type 2 diabetes mellitus (T2DM) progression, but the underlying mechanism is not clear yet. In addition, previous studies mainly focused on the prevention of healthy people against the incidence of T2DM. We designed a panel study including two follow-ups and enrolled 39 patients with T2DM living in Beijing. Linear mixed model was fitted to assess the association between two pairs of variables (ambient air pollution exposure and C3 levels, ambient air pollution exposures and T2DM index). Mediation analysis of C3 between ambient air pollution exposure and indicators of T2DM progression was conducted. We found that PM2.5 exposures is are negatively associated with serum complement C3. Given that C3 might act as a protector of pancreas ß cell, PM2.5 exposures could accelerate disease in T2DM populations. No mediation effects were found. This study reveals that exposures to PM2.5 can cause progression of diseases among T2DM populations.

Associations of ambient fine particulate matter and its constituents with serum complement C3 in a panel study of older adults in China.

Epidemiological studies have demonstrated association between the total mass of fine particulate matter (PM2.5) exposures and inflammation. There are few studies exploring the associations between PM2.5 constituents and the biomarkers of inflammation in older adults and the underlying biological mechanisms are not exact. In this study, we examined the associations between PM2.5 and its constituents (organic carbon (OC), elemental carbon (EC), total carbon (TC), polycyclic aromatic hydrocarbons (PAHs) and complement three factor (C3), an important biomarker of inflammation in a repeated panel of 175 older adults in Beijing, China. We have constructed three different linear mixed effect models (single-pollutant model, constituent-PM2.5 joint model, and constituent-residual model) to evaluate the association of PM2.5 and its constituents and complement C3, controlling for concentration of high sensitive C-reactive protein (hs-CRP), day of week, mean temperature, relative humidity, location and potential individual confounders. We found robust positive associations of OC, EC, TC, PAHs and PM2.5 mass concentration with complement C3 at different lag patterns. The cumulative effects of pollutants increased across average of 2-5 days. Individuals aged 65 and above, or with diabetes, or BMI ≥30, or with no-cardiopathy, or with hypertension also exhibited positive associations between PM2.5 and complement C3. The results revealed that short-term exposure to PM2.5 and its constituents could result in a significant increase in serum level of complement C3. These findings suggested a possible involvement of complement C3 in the effect of PM2.5 on inflammatory reaction.

Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model.

Accurate spatial information of PM2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM2.5. However, the predicted PM2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM2.5 in Beijing, and then seasonal PM2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM2.5 concentration at seasonal level (R2 ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R2 ≥ 0.81 in model fitting and testing). PM2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.

The spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China.

Owing to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10 µm in aerodynamic diameter (PM10), nitrogen dioxide (NO2) and carbon monoxide (CO) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM10, NO2 and CO, and meteorological factors from 1 January 2009 to 31 December 2010 in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM10, NO2 and CO on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM, and the strongest effects were identified for 2-day moving average lag 0-1. The interquartile increases in concentrations of PM10, NO2 and CO were associated with 2.46 (95% confidence interval (CI), 1.22-3.72), 4.11 (95%CI, 2.82-5.42) and 2.23 (95%CI, 1.14-3.33) percentage increases in daily cardiovascular mortality by GAMM, respectively. The relative risk of each district compared with reference district was generally statistically significant. The death risk associated with air pollutants varies across different geographic districts in Beijing. The data indicate that the risk is high in suburban areas and rural counties. We found significant and spatially varied adverse effects of air pollution on cardiovascular deaths across the rural and urban areas in Beijing.

Effect modification of the association between temperature variability and daily cardiovascular mortality by air pollutants in three Chinese cities.

There is limited evidence showing the mortality effects of temperature variability (TV) on cardiovascular diseases. The joint effects between TV and air pollutants are also less well-established. This study aims to assess the effect modification of TV-cardiovascular mortality by air pollutants in three Chinese cities (Beijing, Nanjing and Chengdu). Data of daily mortality, air pollutants and meteorological factors from 2008 to 2011 was collected from each city. TV was calculated as the standard deviation of daily maximum and minimum temperatures over exposure days. The city-specific effect estimates of TV on cardiovascular mortality were calculated using a quasi-Poisson regression model, adjusting for potential confounders (e.g., seasonality and temperature). An interaction term of TV and a three-level air pollutants stratum indicator was included in the models. Effect modifications by air pollutants were assessed by comparing the estimates of TV's effect between pollutant stratums and calculating the corresponding 95% confidential interval of the differences. Multivariate meta-analysis was conducted to obtain the pooled estimates. The data showed that TV was associated with increased risk of cardiovascular mortality, especially for longer TV exposure days (0-8 days, TV08). This association was still observed after adjusting for air pollutants on current day or the previous two days. Stronger estimates were observed in females, but no significant difference between males and females was detected, indicating the absence of evidence of effect modification by gender. Estimates of TV-cardiovascular mortality varied across two season periods (warm and cool season) and age groups, but the evidence of effect modification by age and seasons was absent. Regarding the effect modification of TV-cardiovascular mortality association by air pollutants, a significant effect modification was identified for PM10, but not for NO2 and SO2 in the whole population for all TV exposure days. This finding also persisted in subgroups, specifically in females and the elderly.

Ambient Fine Particulate Matter Exposure and Risk of Cardiovascular Mortality: Adjustment of the Meteorological Factors.

Few studies have explicitly explored the impacts of the extensive adjustment (with a lag period of more than one week) of temperature and humidity on the association between ambient fine particulate matter (PM2.5) and cardiovascular mortality. In a time stratified case-crossover study, we used a distributed lag nonlinear model to assess the impacts of extensive adjustments of temperature and humidity for longer lag periods (for 7, 14, 21, 28 and 40 days) on effects of PM2.5 on total cardiovascular mortality and mortality of cerebrovascular and ischemic heart disease and corresponding exposure-response relationships in Beijing, China, between 2008 and 2011. Compared with results only controlled for temperature and humidity for 2 days, the estimated effects of PM2.5 were smaller and magnitudes of exposure-response curves were decreased when longer lag periods of temperature and relative humidity were included for adjustments, but these changes varied across subpopulation, with marked decreases occurring in males and the elderly who are more susceptible to PM2.5-related mortalities. Our findings suggest that the adjustment of meteorological factors using lag periods shorter than one week may lead to overestimated effects of PM2.5. The associations of PM2.5 with cardiovascular mortality in susceptible populations were more sensitive to further adjustments for temperature and relative humidity.

A Two-Stage Method to Estimate the Contribution of Road Traffic to PM2.5 Concentrations in Beijing, China.

BACKGROUND: Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM2.5) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile. OBJECTIVE: To develop a simplified and indirect method to estimate the contribution of traffic to PM2.5 concentration in Beijing, China. METHODS: Hourly PM2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM2.5 concentration. The geographical trend of PM2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM2.5 and non-linear relationship between PM2.5 and meteorological conditions were assessed using GAMM. RESULTS: The medians of daily PM2.5 concentrations during 2013-2014 at 35 AQM stations in Beijing ranged from 40 to 92 µg/m³. There was a significant increasing trend of PM2.5 concentration from north to south. The contributions of road traffic to daily PM2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations. CONCLUSIONS: Traffic emissions account for a substantial share of daily total PM2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM2.5 concentrations when there is limited information on vehicle number and types and emission profile.

Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study.

OBJECTIVE: To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. DESIGN: A time-series study using regional death registry between 2009 and 2010. SETTING: 8 districts in a large metropolitan area in Northern China. PARTICIPANTS: 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. MAIN OUTCOME MEASURES: Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. RESULTS: The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (-1.09 to 4.28 vs -1.08 to 3.93) and the PCs-based model (-2.23 to 4.07 vs -2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, -1.12 to 4.85 versus -1.11 versus 4.83. CONCLUSIONS: The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes.