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Vaccination has proven to be highly effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the long-term immunogenicity and the functional preserved immune responses of vaccines are needed to inform evolving evidence-based guidelines for boosting schedules. We enrolled 205 healthcare workers into a cohort study; all had received three doses of BBIBP-CorV (China Sinopharm Bio-Beijing Company, Beijing, China) inactivated vaccine. We assessed SARS-CoV-2 specific binding antibodies, neutralizing antibodies, and peripheral T and B cell responses. We demonstrated that more robust antibody responses to SARS-CoV-2 were elicited by booster immunization compared with primary vaccination. Neutralizing antibody titers to SARS-CoV-2 Omicron BA.1 were also efficiently elevated post-homologous vaccine booster despite being in a lower titer compared with the prototype stain. In addition to S-specific humoral and cellular immunity, BBIBP-CorV also induced N-specific antibody and effector T cell responses. The third-dose vaccination led to further expansion of critical polyfunctional T cell responses, likely an essential element for vaccine protection. In particular, a functional role for Tfh cell subsets in immunity was suggested by the correlation between both CD4+ Tfh and CD8+ Tfh with total antibody, IgG, B cell responses, and neutralizing antibodies. Our study details the humoral and cellular responses generated by the BBIBP-CorV booster vaccination in a seven-month follow-up study. There is a clear immunologic boosting value of homologous inactivated SARS-CoV-2 vaccine boosters, a consideration for future vaccine strategies. [ABSTRACT FROM AUTHOR]

Background: The left atrium (LA) is closely related to left ventricular diastolic function. Two-dimensional speckle tracking strain and strain rate (SR) imaging has been applied in the study of LA function. We intended to explore the relationship between global LA deformation parameters and plasma NTproBNP levels in asymptomatic community residents with normal ejection fraction and normal LA volume.Methods: A cross-sectional sample of Beijing residents underwent comprehensive Doppler echocardiography and medical record review in 2009. Global LA longitudinal strain and SR indexes were obtained in the apical four-chamber view. LA stiffness index (LASI) was calculated as the ratio of early diastolic velocity of transmitral flow/early diastolic mitral annular motion velocity (E/E') to LA reservoir strain.Results: A total of 620 individuals (mean age = 65.8 years, left ventricular ejection fraction = 70.8%, LA volume index = 17.9 ml/m2) were investigated in our study. 117 individuals had increased plasma NTproBNP (≥ 125 pg/ml). LA reservoir and contractile function by LA strain and SR indexes were significantly reduced in the abnormal NTproBNP group compared with the normal NTproBNP group. Multiple regression analysis indicated that LA contractile strain was a negative predictor of plasma NTproBNP in addition to indexed LA volume and E/E'. LASI was higher in the abnormal NTproBNP group and was significantly correlated with NTproBNP (r = 0.342, P < 0.001). The area under ROC analysis for LASI in predicting elevated plasma NTproBNP was 0.690, similar with LA contractile strain, E/E' and LAVI. The cut-off value of LASI was 0.612.Conclusions: LA reservoir and contractile functions demonstrated by LA strain and SR were significantly impaired in the community-based population with increased plasma NTproBNP levels. LA contractile strain adds incremental information in predicting abnormal NTproBNP levels. As a single index, LASI showed similar diagnostic value with LAVI and E/E' in predicting abnormal NTproBNP. [ABSTRACT FROM AUTHOR]

As the most concentrated area of human activities, cities consume many natural resources and discharge a large amount of waste into the natural environment, which has a huge environmental impact. Most of the ecological and environmental problems, such as environmental pollution, global climate change, and loss of biodiversity, are related to urban systems. How to coordinate urban development with the urban ecological carrying capacity is related to the destiny of the city itself, and also to whether its surrounding areas can successfully achieve the goal of high environmental quality and sustainable development. At present, the theory and methods of urban ecological carrying capacity research are relatively new, which has caused problems for policy makers in practical applications. This paper proposes a theoretical framework for urban ecological carrying capacity assessment based on the analysis of ecosystem services supply and demand. Combined with multi-source spatial data and spatial model methods, the supply and demand of ecosystem services were spatially quantified. The capital city of China, Beijing, was the case study area for this research. The spatial differentiation of the supply–demand relationship of ecosystem services is formed. The priority areas for ecological carrying capacity improvement at pixel scale and at the administrative level are obtained, respectively. The results show that the first priority area is concentrated in the center of the urban area, accounting for 31.11% of the total area of Beijing. According to the secondary zone and the specific ecosystem service type, the ecological carrying capacity improvement strategy of different zones is proposed. This study provides a new perspective for investigating urban ecological carrying capacity and for identifying the priority areas for ecological carrying capacity improvement, and helps the policy-makers to design tailored policy actions. [ABSTRACT FROM AUTHOR]

Absorbing aerosols uniquely impact radiation, aerosol transport, and meteorology. This paper quantifies black carbon core and sulphate shell size and mass using decadal measurements of multi‐spectral aerosol optical depth, single scattering albedo, and angstrom exponent from Aerosol Robotic Network stations located throughout East, Southeast, and South Asia, in connection with a MIE model. All sites are uniquely characterized into four types: urban, biomass burning, long‐range transport, and clean. Unique size and mass probability distributions of both the core and shell are calculated within each classification. Well known urban, biomass burning, and clean sites are all properly identified. Furthermore, two unique sites previously thought to not have multiple characteristics are identified, with urban and biomass burning significant in Beijing and long‐range transport significant in the otherwise clean South China Sea at Taiping Island. It is hoped that these results will allow for advances in attribution and radiative forcing studies. Plain Language Summary: Black Carbon strongly absorbs visible radiation, leading to unique impacts on atmospheric radiation, climate, the water cycle, and PM2.5. This work attributes different aerosol source characteristics, and further specifies the size distribution and concentration of aerosol black carbon cores and refractive shells. This work uses measurements of aerosol optical depth, single scatter albedo, and angstrom exponent, across multiple different wavelengths of light, in combination with statistics and a MIE model (physical model of aerosol/radiation interaction) using a Core‐Shell approximation. The results show that aerosols observed in East, Southeast, and South Asia can be uniquely classified into four types: urban, biomass burning, long‐range transport, and clean. These results are consistent in terms of aerosol size and mass at each site within each type of characterization. Furthermore, two unique sites are identified in which a second characteristic occurs some significant fraction of every year, which otherwise was not known or previously identified in the literature. These results are expected to help enhance the understanding of attribution of aerosols, as well as provide specific size and mass details of the aerosols useful to improve radiative forcing models and aerosol impacts on climate change. Key Points: Aerosols are categorized into biomass burning, urban, and long‐range types over Asia using decadal long multi‐spectral measurementsBased on multiple Aerosol Robotic Network Single Scatter Albedo measurements and a MIE model, physical characteristics of different aerosol types are deducedMost aerosols are found to be mixed, with two sites having different characteristics during different times of the year [ABSTRACT FROM AUTHOR]

Seasonal dependence of the columnar aerosol optical and chemical properties on regional transport in Beijing over 10 years (from January 2005 to December 2014) were analyzed by using the ground-based remote sensing combined with backward trajectory analysis. Daily air mass backward trajectories terminated in Beijing were computed with HYSPLIT-4 model and were categorized into five clusters. The columnar mass concentrations of black carbon (BC), brown carbon (BrC), dust (DU), aerosol water content (AW), and ammonium sulfate like aerosol (AS) of each cluster were retrieved from the optical data obtained from the Aerosol Robotic NETwork (AERONET) with five-component model. It was found that the columnar aerosol properties in different seasons were changed, and they were related to the air mass origins. In spring, aerosol was dominated by coarse particles. Summer was characterized by higher single scattering albedo (SSA), lower real part of complex refractive index ( n ), and obvious hygroscopic growth due to humid air from the south. During autumn and winter, there was an observable increase in absorption aerosol optical thickness (AAOT) and the imaginary part of complex refraction ( k ), with high levels of retrieved BC and BrC. However, concentrations of BC showed less dependence on the clusters during the two seasons owing to the widely spread coal heating in north China. [ABSTRACT FROM AUTHOR]

Phthalates are environmental endocrine disruptors with thyroid-disrupting properties; however, the association between phthalate exposure and subclinical hypothyroidism (SCH) during pregnancy is unknown. We recruited a study population from a cohort of pregnant women in Beijing, China, and conducted the present pilot case-control study of 42 SCH cases and 84 non-SCH controls matched with age and body mass index (BMI). Serum levels of thyroid peroxidase antibody, free thyroxine (FT 4), thyroid-stimulating hormone (TSH), and urinary levels of ten phthalate metabolites during early pregnancy were measured. Urinary monoethyl phthalate (MEP) levels in SCH cases were observably higher than those in controls (p = 0.01). Conditional logistic regression analysis revealed that mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP), MEP, mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), and di-(2-ethylhexyl) phthalate (ΣDEHP) were significantly associated with a higher risk of SCH during early pregnancy (adjusted odds ratios = 1.89, 1.42, 1.81, and 1.92, respectively). Concomitantly, multiple linear regression analysis showed that MECPP, MEOHP, and ΣDEHP were positively associated with TSH and FT 4 × TSH in the entire study population. Bayesian kernel machine regression analysis and stratified analysis by BMI revealed upward tendencies in the serum levels of TSH and FT 4 × TSH. In summary, exposure to phthalates, especially DEHP, may be associated with a higher risk of SCH during early pregnancy, and a possible mechanism is the disruption of the hypothalamus-pituitary-thyroid axis. [Display omitted] • DEP exposure levels of SCH cases were observably higher than those of controls. • Phthalate exposure during early pregnancy may be related to a higher risk of SCH. • MECPP, MEOHP and ΣDEHP were positively associated with TSH and FT 4 × TSH. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5 ) air pollution outbreaks have recently occurred frequently in China. However, evidence of the associations between short-term exposure to PM 2.5 and cardiovascular morbidity is still limited in China. This study aimed to evaluate the associations between PM 2.5 and hospital emergency room visits (ERVs) for cardiovascular diseases in urban areas in Beijing. Daily counts of cardiovascular ERVs were collected from ten large general hospitals from Jan 1 to Dec 31, 2013. Air pollution data were obtained from the Beijing Environmental Protection Bureau including 17 monitoring stations. A generalized additive Poisson model was used to examine the associations between PM 2.5 and cardiovascular ERVs after controlling for seasonality, day of the week, public holidays, influenza outbreaks, and weather conditions. In total, there were 56,221 cardiovascular ERVs during the study period. The daily mean PM 2.5 concentration was 102.1 μg/m 3 , ranging from 6.7 μg/m 3 to 508.5 μg/m 3 . Per 10 μg/m 3 increase in PM 2.5 was associated with a 0.14% (95% confidence interval [ CI ]: 0.01%–0.27%) increase in cardiovascular ERVs at lag 3 . Cumulative delayed estimates were greatest at lag 0–5 (0.30%, 95% CI : 0.09%–0.52%). The estimates of percentage change in daily ERVs per 10 μg/m 3 increase in PM 2.5 were 0.56% ( 95%CI : 0.16%–0.95%) for ischemic heart disease (IHD) at lag 0–1 , 0.81% ( 95%CI : 0.05%–1.57%) for heart rhythm disturbances (HRD) at lag 0–1 and 1.21% ( 95%CI : 0.27%–2.15%) for heart failure (HF) at lag 0 , respectively. The effects of PM 2.5 on IHD ERVs during high temperature days (>11.01 °C) were significantly higher than that on low temperature days (≤11.01 °C) at lag 0 , lag 0–1 , lag 0–3 and lag 0–5 ( P < 0.05). The study suggests that PM 2.5 has acute impacts on cardiovascular ERVs in Beijing, especially on IHD, HRD and HF. The effects of PM 2.5 on IHD ERVs vary by temperature. [ABSTRACT FROM AUTHOR]

Phthalates are a class of environmental endocrine disruptors. Previous studies have demonstrated that phthalate exposure can affect thyroid function; however, limited studies have assessed the associations between phthalate exposure and thyroid function, especially thyroid autoimmunity in pregnant women during the first trimester. We recruited participants from a cohort of pregnant women in Beijing, China, and collected urine samples to measure ten phthalate metabolites, serum samples to measure free thyroxine (FT 4), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb) during the first trimester. We included 325 pregnant women without thyroid diseases or dysfunction in this study. Associations between phthalate metabolites and thyroid function parameters were assessed with the Bayesian kernel machine regression (BKMR) model, multiple linear regression model, and restricted cubic spline. In the BKMR model analysis, compared to the 50th percentile, total urinary phthalate metabolites levels were negatively associated with serum TPOAb levels when phthalate metabolites were at or below the 40th percentile. Stratifying by body mass index, total urinary phthalate metabolites levels were negatively associated with serum TPOAb levels in normal weight women when phthalate metabolites were at or below the 45th percentile. However, total urinary phthalate metabolites levels were positively associated with serum TPOAb levels in underweight women when phthalate metabolites were at or below the 30th percentile. In restricted cubic spline analysis, L -shaped nonlinear associations of mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), di-(2-ethylhexyl) phthalate (ΣDEHP), and inverted S-shaped nonlinear association of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) with TPOAb were observed. In conclusion, our findings suggest that phthalate exposure may affect thyroid autoimmunity in underweight pregnant women during early pregnancy, and the potential effects of phthalate exposure on thyroid autoimmunity may be nonlinear. [Display omitted] • The joint effects of phthalate exposure were assessed using the BKMR model. • Urinary phthalate metabolites were mainly associated with serum TPOAb levels. • Phthalates may affect thyroid autoimmunity in underweight pregnant women. [ABSTRACT FROM AUTHOR]

Background: Heavy fine particulate matter (PM2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing. Methods: Daily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender. Results: A total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM2.5 concentration was 102.1±73.6 μg/m3. Every 10 μg/m3 increase in PM2.5 concentration at lag0 was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM2.5 concentration at lag0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO2, O3, CO and NO2. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM2.5 exposure. Conclusion: PM2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM2.5 pollution varied by gender and age. [ABSTRACT FROM AUTHOR]

The effects of urban green spaces on stormwater runoff have increasingly attracted attention because of climate change and rapid urbanization conditions. This study investigates the spatial–temporal changes of urban green spaces in Beijing and estimates their effects on rainwater runoff reduction based on an empirical model. Results indicate that green spaces in Beijing decreased by 199 km 2 from 2000 to 2010 and that landscape patches became increasingly isolated and fragmented. The volume of rainwater runoff controlled by urban green spaces first increased and then decreased with the increase in summer rainfall. The runoff reduction rate continuously decreased from 23% in 2000 to 17% in 2010, which is mainly attributed to the composition changes in urban green spaces. In addition, an immense regional difference that is closely related to the changes in the largest patch and aggregation indices of urban green spaces are observed in different regions. This study recommends an optimal landscape pattern of urban green spaces for the planning and management of green spaces in highly urbanized areas. [ABSTRACT FROM AUTHOR]

The insufficient information on water conservation dynamics and potential in ecological engineering areas often restrict the selection and implementation of ecological restoration measures in arid and semiarid climates. Based on the regional water balance equation and GIS spatial analysis tools, this article assessed the spatial-temporal changes in water conservation in Xilin Gol League, a representative region of the Beijing–Tianjin Sandstorm Source Control Project Area. The results indicated that the yearly amount of water conservation in Xilin Gol League reached 1.27 billion cubic meters, and annual precipitation of approximately 6.36 mm was retained per hectare. The water conservation capacity in Xilin Gol League showed a notably increasing tendency during the period of 2000–2015 and increased from west to east. The northeast of East Ujimqin Banner had the highest capacity, contributing 16.19% of the total water conservation amount. Compared to 2000, approximately 65.86% of Xilin Gol League presented water conservation capacity increases in 2015, 15.25% of which were significant. The water conservation in Xilin Gol League correlated with the changes in precipitation, temperature, and vegetation coverage, and unfavorable changes in these factors led to a significant decline in regional water conservation capacity. Therefore, multiple ecological restoration strategies are proposed for the improvement of water conservation capacity in the low-efficiency areas. • Dynamic changes in water conservation in ecological engineering area were quantified. • The water conservation capacity notably increased from west to east during 2000–2015. • The water conserved increased in 66% of the study area and decreased in western area. • Rainfall, temperature, and vegetation determine the changes in water conservation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Women with normal glucose tolerance exhibit reduced C-peptide levels in the second trimester. • Fasting C-peptide levels have a positive correlation with gestational diabetes mellitus risk. • C-peptide monitoring serves as a predictor for gestational diabetes mellitus and its subtypes. To examine the association of serum connecting peptide (C-peptide) concentrations with gestational diabetes mellitus (GDM) risk among Chinese women. A nested case-control study was conducted on 436 reproductive-aged women, involving 218 GDM cases and 218 controls matched at 1:1 by maternal age, in Beijing, China between January 2016 and December 2017. Fasting serum C-peptide were successively determined at 10–14 and 15–20 weeks of gestation. Restricted cubic spline and logistic regression analyses were utilized, and receiver operating characteristic (ROC) curves were generated to evaluate the predictive capacity of C-peptide for GDM. Fasting serum C-peptide concentrations exhibited a significant decrease from the initial to the subsequent trimester in females with normal glucose tolerance (NGT). For each 1 log ng/mL increase of fasting serum C-peptide during the first and second trimesters, GDM risk increased by 2.38-fold [odds ratio (OR): 2.38, 95% confidence intervals (95%CI): 1.33–4.40] and 3.07-fold (OR: 3.07, 95%CI: 1.49–6.62), respectively. The areas under the ROC curves for the first- and second-trimester C-peptide were 80.4% and 82.4%. Our findings revealed a positive correlation between fasting serum C-peptide during the first and second trimesters and the risk of GDM or its subtypes, underscoring the potential of C-peptide as a predictor for GDM development. [ABSTRACT FROM AUTHOR]

Effective prediction of PM 2.5 long-term concentration can help reduce exposure risks, but few current studies based on machine learning have been able to credibly predict concentration changing trends after 12 h. To address this issue, this study employed the complementary ensemble empirical mode decomposition (CEEMD) method combined with sample entropy (SE) index, as well as bidirectional long short-term memory (BiLSTM) model stacked denoising auto-encoder (AE) method for better decomposition and prediction of long-term trends in PM 2.5 time series data. The new CEEMD-AE-BiLSTM model has the best long-term prediction results at different sites, and the R2 value is 0.871, and the RMSE is 9.28 μg/m3 at T+12 moment in the 1002A site. The new model effectively captures a significant portion of the long-term concentration trends characteristically and has been proven to be an effective tool for long-term predictions. This is attributed to the robust noise resistance of the AE method and the appropriate parameter setting of the CEEMD method. • The new model can predict the long-term trend in PM 2.5 concentration. • The CEEMD method can better decompose the time series data of PM 2.5 concentration. • The AE method can effectively resist the noise of component and enhance the accuracy of the prediction results. • The SE index can effectively couple decomposition method and time series prediction model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hybrid graph neural network can realize 72 h real-time reliable prediction. • AOD data can provide extra constraint information for the prediction model. • Quantifying domain spatial information can reduce redundant information. • Expanding the modeling area in different directions is necessary for 72 h prediction. • The GNN_LSTM model can dynamic forecast the "source"-receptor relationship. The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM 2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R2 increases from 0.6 to 0.79), particularly for polluted episodes (PM 2.5 concentration > 55 µg/m3) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM 2.5 over the sites where the AOD can inform additional aloft PM 2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM 2.5 forecast is demonstrated by the increased performance in predicting PM 2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are playing a key role in supporting transportation electrification and reducing air pollution and greenhouse gas emissions. The increased number of EVs may also bring about some issues concerning energy system structure optimization and efficiency enhancement. User behavior analysis and simulation is an important method to solve these issues. A stochastic model for describing the usage of vehicle is essential to handle simulation models and behavior models. Therefore, a more comprehensive understanding of EV usage patterns is necessary for the model establishment. The paper focuses on the 2,047,222 charging events and 8,382,032 travel events collected from 26,606 battery electric vehicles operating in Beijing, China, in 2018, based on the open lab of National Big Data Alliance of New Energy Vehicles. With the large-scale data resource rather than limited samples, we provide some robust statistical results and some multi-dimensional comparative analysis in the paper, which can be applied in large-scale deployment environments and large population cities. The results can also provide information for charging infrastructures construction, gird management, vehicle charging scheduling, and so forth in Beijing and even other metropolises with similar situations. • Operational data of 26,606 battery electric vehicles in Beijing are collected. • The usage pattern including charging pattern and travel pattern is investigated. • Probability density function is used to analyze usage pattern characteristics. • The characteristics of vehicles in different application scenarios are compared. [ABSTRACT FROM AUTHOR]

Background: Ambient particles have been associated with gestational diabetes mellitus (GDM), however, no study has evaluated the effects of traffic-related ambient particles on the risks of GDM subgroups classified by oral glucose tolerance test (OGTT) values.Methods: A retrospective analysis was conducted among 24,001 pregnant women who underwent regular prenatal care and received OGTT at Haidian Maternal and Child Health Hospital in Beijing, China, 2014-2017. A total of 3,168 (13.2%) pregnant women were diagnosed with GDM, including 1,206 with isolated fasting hyperglycaemia (GDM-IFH). At a fixed-location monitoring station, routinely monitored ambient particles included fine particulate matter (PM2.5), black carbon (BC) and particles in size ranges of 5-560 nm (PNC5-560). Contributions of PNC5-560 sources were apportioned by positive matrix factorization model. Logistic regression model was applied to estimate odds ratio (OR) of ambient particles on GDM risk.Results: Among the 24,001 pregnancy women recruited in this study, 3,168 (13.2%) were diagnosed with GDM, including 1,206 with isolated fasting hyperglycaemia (GDM-IFH) and 1,295 with isolated post-load hyperglycaemia (GDM-IPH). We observed increased GDM-IFH risk with per interquartile range increase in first-trimester exposures to PM2.5 (OR = 1.94; 95% Confidence Intervals: 1.23-3.07), BC (OR = 2.14; 1.73-2.66) and PNC5-560 (OR = 2.46; 1.90-3.19). PNC5-560 originated from diesel and gasoline vehicle emissions were found in associations with increases in GDM-IFH risk, but not in GDM-IPH risk.Conclusion: Our findings suggest that exposure to traffic-related ambient particles may increase GDM risk by exerting adverse effects on fasting glucose levels during pregnancy, and support continuing efforts to reduce traffic emissions for protecting vulnerable population who are at greater risk of glucose metabolism disorder. [ABSTRACT FROM AUTHOR]

[Display omitted] Trace elements have various physiochemical functions in humans and are associated with the occurrence of preterm birth (PB). However, their mixed effects on PB risk have rarely been studied. We aimed to investigate the associations between hair levels of trace elements and PB risk among pregnant women. A nested case-control study with a prospective cohort was conducted in Beijing City, China. We included 82 women who had a PB [total PB (tPB)] as cases [including 40 with a spontaneous PB (SPB)] and 415 who had a term delivery as controls. Hair levels of the concerned trace elements were measured including endocrine disrupting metal(loid)s [EDMs; lead, mercury (Hg), arsenic, and cadmium] and nutritional trace metal(loid)s [NTMs; zinc (Zn), iron (Fe), copper, and selenium]. Logistic regression analysis was performed to estimate the odds ratios (ORs) for PB. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixed exposure to the trace elements and PB risk. Significantly lower maternal hair concentrations of Zn and Fe were observed in the SPB cases than in the controls, whereas no differences for the other trace elements. Single-element modeling results suggested second-quartile Hg maternal hair concentrations, third-quartile Zn concentrations, and fourth-quartile Fe concentrations were associated with a reduced risk of tPB with adjusted ORs of 0.43 [95% confidence interval (CI): 0.21–0.87], 0.38 (95% CI: 0.18–0.82), and 0.48 (95% CI: 0.24–0.98), respectively, compared to first-quartile values. Similar results were obtained for SPB. According to the BKMR models, hair NTMs were significantly, monotonously, and inversely associated with the risk of SPB, after controlling for covariates and levels of the four EDMs. Fe and Zn contributed the most strongly to the association. We concluded that maternal higher levels of NTMs, especially Fe and Zn, may reduce the risk of PB. [ABSTRACT FROM AUTHOR]

• Exposure to environmental endocrine disruptors may cause abnormal glucose metabolism. • Arsenic, cadmium, lead, mercury, and tin are part of persistent organic pollutants. • Mixed exposure to these five metals may promote the development of GDM. Exposure to environmental endocrine disruptors (EDCs) may lead to abnormal glucose metabolism and, potentially, gestational diabetes mellitus (GDM). We investigated the association between five endocrine-disrupting heavy metals (EDHMs), i.e., arsenic (As), cadmium (Cd), lead (Pb), mercury (Hg), and tin (Sn), in maternal hair and the risk of GDM. We conducted a nested case-control study including 335 GDM cases and 343 controls without GDM based on a prospective birth cohort established in Beijing, China. Concentrations of EDHMs were analyzed in maternal hair. Log-binomial regression and multiple linear regression were used to estimate the associations between the hair concentrations of single metals and the risk of GDM, while weighted quantile sum (WQS) regression for their mixed effects. The median concentrations of Hg (0.442 vs. 0.403 μg/g) and Sn (0.171 vs. 0.140 μg/g) in the case group were significantly higher than those in the control group. No differences were found between the two groups for the other three metals. After adjusting for confounders, the prevalence ratio (PR; highest vs. lowest tertile) of GDM risk for Hg was 1.27 (95% confidence interval [CI]: 1.05–1.54), while that for Sn was 1.26 (95% CI: 1.04–1.53). Among women with a body mass index < 24 kg/m2, the PR (highest vs. lowest tertile) of GDM for Sn was 1.38 (95% CI: 1.09–1.75). The effect of exposure to the five EDHMs on the risk of GDM was estimated by WQS regression: Sn and Hg made the largest contributions to the WQS index (40.9% and 40.3%, respectively). High maternal levels of EDHMs, particularly Sn and Hg, may promote the development of GDM. [ABSTRACT FROM AUTHOR]