Effects of fine particulate matter mass and chemical components on oxidative DNA damage in human early placenta.

The effects of chemical components of ambient fine particulate matter (PM2.5) on human early maternal-fetal interface are unknown. We estimated the associations of PM2.5 and component exposures with placental villi 8-hydroxy-2'-deoxyguanosine (8-OHdG) in 142 normal early pregnancy (NEP) and 142 early pregnancy loss (EPL) from December 2017 to December 2022. We used datasets accessed from the Tracking Air Pollution in China platform to estimate maternal daily PM2.5 and component exposures. Effect of average PM2.5 and component exposures during the post-conception period (i.e., from ovulation to villi collection) on the concentration of villi 8-OHdG were analyzed using multivariable linear regression models. Distributed lag and cumulative effects of PM2.5 and component exposures during the periovulatory period and within ten days before villi collection on villi 8-OHdG were analyzed using distributed lag non-linear models combined with multivariable linear regression models. Per interquartile range increase in average PM2.5, black carbon (BC), and organic matter (OM) exposures during the post-conception period increased villi 8-OHdG in all subjects (ß = 34.48% [95% CI: 9.33%, 65.42%], ß = 35.73% [95% CI: 9.08%, 68.89%], and ß = 54.71% [95% CI: 21.56%, 96.91%], respectively), and in EPL (ß = 63.37% [95% CI: 16.00%, 130.10%], ß = 47.43% [95% CI: 4.30%, 108.39%], and ß = 72.32% [95% CI: 18.20%, 151.21%], respectively), but not in NEP. Specific weekly lag effects of PM2.5, BC, and OM exposures during the periovulatory period increased villi 8-OHdG in all subjects. Ten-day cumulative and lag effects of PM2.5, BC, and OM increased villi 8-OHdG in all subjects and EPL, but not in NEP; and the effects of OM were robust after adjusting for BC, ammonium, nitrate, or sulfate in two-pollutant models. In conclusion, placental oxidative DNA damage in early pregnancy was associated with maternal exposure to PM2.5, especially its chemical components BC and OM.

Sources, trigger points, and effect size of associations between PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and fractional exhaled nitric oxide (FeNO): A panel study with 16 follow-up visits over 4 years.

Exposure to fine particulate matter (PM2.5) is a significant concern for respiratory health. However, the sources, trigger points, and effect size of specific associations between PM2.5 components, particularly polycyclic aromatic hydrocarbons (PAHs) and the airway inflammatory marker fractional exhaled nitric oxide (FeNO) have not been fully explored. In this study, 69 healthy college students were enrolled and followed up 16 times from 2014 to 2018. Individual FeNO was measured and ambient air PM2.5 samples were collected for 7 consecutive days before each follow-up. PAHs were quantified using Gas Chromatography-Mass Spectrometry. Linear mixed-effect regression models were employed to evaluate the associations between PM2.5-bound PAHs and FeNO. Additionally, PMF (Positive Matrix Factorization) was utilized to identify sources of PM2.5-bound PAHs and assess their impact on FeNO. Throughout the study, the average (SD) of ΣPAHs concentrations was 78.50 (128.9) ng/m3. PM2.5 and PM2.5-bound PAHs were significantly associated with FeNO at various lag days. Single-day lag analyses revealed maximum effects of PM2.5 on FeNO, with an increase of 7.71% (95% CI: 4.67%, 10.83%) per interquartile range (IQR) (48.10 µg/m3) increase of PM2.5 at lag2, and ΣPAHs showed a maximum elevation in FeNO of 6.40% (95% CI: 2.33%, 10.63%) at lag4 per IQR (57.39 ng/m3) increase. Individual PAHs exhibited diversity peak effects on FeNO at lag3 (6 of 17), lag4 (9 of 17) in the single-day model, and lag0-5 (8 of 17) (from lag0-1 to lag0-6) in the cumulative model. Source apportionment indicated coal combustion as the primary contributor (accounting for 30.7%). However, a maximum effect on FeNO (an increase of 21.57% (95% CI: 13.58%, 30.13%) per IQR increase) was observed with traffic emissions at lag4. The findings imply that strategic regulation of particular sources of PAHs, like traffic emissions, during specific periods could significantly contribute to safeguarding public health.

Public attitudes toward medical waste: Experiences from 141 countries.

BACKGROUND: Medical Waste (MW), conceptualized as waste generated in the diagnosis, treatment, or immunization of human beings or animals, posing massive threat to public health. Environment-friendly public attitudes promotes the shaping of pro-environmental behavior. However, the public attitudes of MW and the potential determinants remained scarce. The present study aims to reveal globally public attitudes towards MW and captured the determinants. METHODS: We integrated the crawler technology with sentiment analysis to captured the public attitudes toward MW across 141 specific countries from 3,789,764 related tweets. Multiple cross-national databases were integrated to assess characteristics including risk, resistance, environment, and development. The spatial regression model was taken to counterbalence the potential statistical bias. RESULTS: Overall, the global public attitudes towards MW were positive, and varied significantly across countries. Resilience (ß = 0.78, SD = 0.14, P < 0.01) and development (ß = 1.66, SD = 0.13, P < 0.01) posed positive influence on public attitudes towards MW, meanwhile, risk (ß = -0.1, SD = 0.12, P > 0.05) and environment (ß = 0.09, SD = 0.09, P > 0.05) were irrelated to the shaping of positive MW public attitudes. Several positive moderating influences was also captured. Additionally, the cross-national disparities of the determiants were also captured, more specific, public attitudes towards MW in extremely poor areas were more likely to be negatively affected by risks, resilience and development. CONCLUSIONS: This study focused mainly on the public attitudes as well as captured the potential determinants. Public attitudes towards MW were generally positive, but there were large cross-national disparities. Stakeholders would need to designate targeted strategies to enhance public satisfaction with MW management.

Quantification for photochemical loss of volatile organic compounds upon ozone formation chemistry at an industrial city (Zibo) in North China Plain.

Volatile organic compounds (VOCs) are consumed by photochemical reactions during transport, leading to inaccuracies in estimating the local ozone (O3) formation mechanism and its subsequent strategy for O3 attainment. To comprehensively quantify the deviations in O3 formation mechanism by consumed VOCs (C-VOCs), a 5-month field campaign was conducted in a typical industrial city in Northern China over incorporating a 0-D box model (implemented with MCMv3.3.1). The averaged C-VOCs concentration was 6.8 ppbv during entire period, and Alkenes accounted for 62% dominantly. Without considering C-VOCs, the relative incremental reactivity (RIR) of anthropogenic VOCs (AVOC, overestimated by 68%-75%) and NOx (underestimated by 137%-527%) demonstrated deviations at multiple scenarios, and the RIR deviations for precursors in High-O3-periods (HOP) were lower than Low-O3-periods (LOP). The RIR deviations from individual species involved C-VOCs calculation did not impact the identification for the high-ranking-RIR AVOC species but non-negligible. Monthly comparisons showed that higher C-VOCs concentrations would lead to higher RIR deviations. The daily maximum of net Ox production rate (P(Ox)) and the regional transport Ox (Trans(Ox)) without C-VOCs were underestimated by 56%-194% and 81%-243%, respectively. After considering C-VOCs, the contribution of HO2+NO for Ox gross production (G(Ox)) decreased by 7% (LOP) and 7% (HOP), but OH + NO2 for Ox destruction (D(Ox)) decreased by 16% (LOP) and 23% (HOP), and alkenes + O3 increased for D(Ox) by 12% (LOP) and 22% (HOP). This implies that VOCs-NOx-O3 sensitivity was deviated between with/without C-VOCs, and severe O3 pollution rendered deviations in O3 formation, especially via NOx-driving chemistry. Based on RIR(NOx)/RIR(AVOC) with/without C-VOCs, the sensitivity regime shifted from VOCs-limited (-0.93) to transition (1.38) at LOP, and from VOCs-limited (0.19) to NOx-limited (3.79) at HOP. Our results reflected that the NOx limitation degree was underestimated without constraint C-VOCs, especially HOP, and provided implication to more precise O3 pollution control strategies.

Reducing Indoor Particulate Air Pollution Improves Student Test Scores: A Randomized Double-Blind Crossover Study.

Short-term exposure to air pollution is associated with a decline in cognitive function. Standardized test scores have been employed to evaluate the effects of air pollution exposure on cognitive performance. Few studies aimed to prove whether air pollution is responsible for reduced test scores; none have implemented a "gold-standard" method for assessing the association such as a randomized, double-blind intervention. This study used a "gold-standard" method─randomized, double-blind crossover─to assess whether reducing short-term indoor particle concentrations results in improved test scores in college students in Tianjin, China. Participants (n = 162) were randomly assigned to one of two similar classrooms and completed a standardized English test on two consecutive weekends. Air purifiers with active or sham (i.e., filter removed) particle filtration were placed in each classroom. The filtration mode was switched between the two test days. Linear mixed-effect models were used to evaluate the effect of the intervention mode on the test scores. The results show that air purification (i.e., reducing PM) was significantly associated with increases in the z score for combined (0.11 [95%CI: 0.02, 0.21]) and reading (0.11 [95%CI: 0.00, 0.22]) components. In conclusion, a short-term reduction in indoor particle concentration led to improved test scores in students, suggesting an improvement in cognitive function.

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China.

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.

Ascertaining sensitive exposure biomarkers of various metal(loid)s to embryo implantation.

Close relationships exist between metal(loid)s exposure and embryo implantation failure (EIF) from animal and epidemiological studies. However, there are still inconsistent results and lacking of sensitive metal(loid) exposure biomarkers associated with EIF risk. We aimed to ascertain sensitive metal(loid) biomarkers to EIF and provide potential biological explanations. Candidate metal(loid) biomarkers were measured in the female hair (FH), female serum (FS), and follicular fluid (FF) with various exposure time periods. An analytical framework was established by integrating epidemiological association results, comprehensive literature searching, and knowledge-based adverse outcome pathway (AOP) networks. The sensitive biomarkers of metal(loid)s along with potential biological pathways to EIF were identified in this framework. Among the concerned 272 candidates, 45 metal(loid)s biomarkers across six time periods and three biomatrix were initially identified by single-metal(loid) analyses. Two biomarkers with counterfactual results according to literature summary results were excluded, and a total of five biomarkers were further determined from 43 remained candidates in mixture models. Finally, four sensitive metal(loid) biomarkers were eventually assessed by overlapping AOP networks information, including Se and Co in FH, and Fe and Zn in FS. AOP networks also identified key GO pathways and proteins involved in regulation of oxygen species biosynthetic, cell proliferation, and inflammatory response. Partial dependence results revealed Fe in FS and Co in FH at their low levels might be potential sensitive exposure levels for EIF. Our study provided a typical framework to screen the crucial metal(loid) biomarkers and ascertain that Se and Co in FH, and Fe and Zn in FS played an important role in embryo implantation.

Ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) associated with pro-thrombotic biomarkers among young healthy adults: A 16 times repeated measurements panel study.

Studies have shown that the cardio/cerebrovascular toxicity of ambient PM2.5 is related to its bound polycyclic aromatic hydrocarbons (PAHs). Currently, only a few studies have reported the relationship between PM2.5-bound PAHs and promoted blood coagulation and thrombosis, but there isn't a consistent conclusion. Therefore, we conducted a prospective panel study to investigate the association. Thirty-three young healthy adults participated in sixteen repeated visits from 2014 to 2018 in Tianjin, China. During each visit, three pro-thrombotic biomarkers: ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin motif 13), D-dimer and Myeloperoxidase (MPO) were measured. Before each visit, ambient PM2.5 samples were daily collected for one week. Sixteen PAHs were determined using Gas Chromatography-Mass Spectrometer, and the positive matrix factorization (PMF) model was applied to identify the sources. Linear mixed-effects models were fitted to investigate the associations between PM2.5-bound PAHs exposure and the biomarkers. Thirteen time-metrics were defined to identify significant time points of PM2.5-bound PAHs' effects. We observed that the increase of PM2.5-bound PAHs exposure was significantly associated with reduced ADAMTS13, elevated D-dimer and MPO. At lag0, each 5.7 ng/m3 increase in Benzo[j]fluoranthene and per 3.4 ng/m3 increase Dibenz[a,h]anthracene could make a maximum change of -19.08 % in ADAMTS13 and 132.60 % in D-dimer. Additionally, per 16.43 ng/m3 increase in Chrysene could lead to a maximum elevation of 32.14 % in MPO at lag4. The PM2.5-bound PAHs often triggered more significant changes at lag 3,4 and 6. The ambient PM2.5-bound PAHs originated from six sources: coal combustion (43.10 %), biomass combustion (20.77 %), diesel emission (14.78 %), gasoline emission (10.95 %), industrial emission (7.58 %), and cooking emission (2.83 %). The greatest contributors to alterations in ADAMTS13, D-dimer and MPO are industrial emission (-48.43 %), biomass combustion (470.32 %) and diesel emission (13.14 %) at lag4. Our findings indicated that short-term exposure to ambient PM2.5-bound PAHs can induce alterations of pro-thrombotic biomarkers among healthy adults.

Critical windows of greenness exposure during preconception and gestational periods in association with birthweight outcomes.

Few studies have examined the association between greenness exposure and birth outcomes. This study aims to identify critical exposure time windows during preconception and pregnancy for the association between greenness exposure and birth weight. A cohort of 13 890 pregnant women and newborns in Shanghai, China from 2016-2019 were included in the study. We assessed greenness exposure using Normalized Difference Vegetation Index (NDVI) during the preconception and gestational periods, and evaluated the association with term birthweight, birthweight z-score, small-for-gestational age, and large-for-gestational age using linear and logistic regressions adjusting for key maternal and newborn covariates. Ambient temperature, relative humidity, ambient levels of fine particles (PM2.5) and nitrogen dioxide (NO2) assessed during the same period were adjusted for as sensitivity analyses. Furthermore, we explored the potential different effects by urbanicity and park accessibility through stratified analysis. We found that higher greenness exposure at the second trimester of pregnancy and averaged exposure during the entire pregnancy were associated with higher birthweight and birthweight Z-score. Specifically, a 0.1 unit increase in second trimester averaged NDVI value was associated with an increase in birthweight of 10.2 g (95% CI: 1.8-18.5 g) and in birthweight Z-score of 0.024 (0.003-0.045). A 0.1 unit increase in an averaged NDVI during the entire pregnancy was associated with 10.1 g (95% CI: 1.0-19.2 g) increase in birthweight and 0.025 (0.001-0.048) increase in birthweight Z-score. Moreover, the associations were larger in effect size among urban residents than suburban residents and among residents without park accessibility within 500 m compared to those with park accessibility within 500 m. Our findings suggest that increased greenness exposure, particularly during the second trimester, may be beneficial to birth weight in a metropolitan area.

Personal Exposure to Source-Specific Particulate Polycyclic Aromatic Hydrocarbons and Systemic Inflammation: A Cross-Sectional Study of Urban-Dwelling Older Adults in China.

Environmental exposure to ambient polycyclic aromatic hydrocarbons (PAHs) can disturb the immune response. However, the evidence on adverse health effects caused by exposure to PAHs emitted from specific sources among different vulnerable subpopulations is limited. In this cross-sectional study, we aimed to evaluate whether exposure to source-specific PAHs could increase systemic inflammation in older adults. The present study included community-dwelling older adults and collected filter samples of personal exposure to PM2.5 during the winter of 2011. Blood samples were collected after the PM2.5 sample collection. We analyzed PM2.5 bound PAHs and serum inflammatory cytokines (interleukin (IL)1ß, IL6, and tumor necrosis factor alpha levels. The Positive Matrix Factorization model was used to identify PAH sources. We used a linear regression model to assess the relative effects of source-specific PM2.5 bound PAHs on the levels of measured inflammatory cytokines. After controlling for confounders, exposure to PAHs emitted from biomass burning or diesel vehicle emission was significantly associated with increased serum inflammatory cytokines and systemic inflammation. These findings highlight the importance of considering exposure sources in epidemiological studies and controlling exposures to organic materials from specific sources.

Identifying critical windows of air pollution exposure during preconception and gestational period on birthweight: a prospective cohort study.

BACKGROUND: Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. METHODS: Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016-2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children's sex-specific associations were explored. RESULTS: After controlling for covariates, one standard deviation (SD) (11.5 µg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 - 26%) and 14% (95% CI: 1 - 29%) increase in SGA, respectively; and one SD (9.6 µg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 - 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22-32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. CONCLUSION: Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.

[Photochemical Mechanism and Control Strategy Optimization for Summertime Ozone Pollution in an Industrial City in the North China Plain].

To investigate the characteristics and formation mechanism of ozone (O3) pollution in an industrial city, an extensive one-month field campaign focusing on O3 and its precursors (e.g., volatile organic compounds[VOC] and nitrogen oxides[NOx]) was conducted in Zibo City, a highly industrializd city in the North China Plain, in June 2021. The 0-D box model incorporating the latest explicit chemical mechanism (MCMv3.3.1) was applied using an observation dataset (e.g., VOC, NOx, HONO, and PAN) as model contraints to explore the optimal reduction strategy for O3 and its precursors. The results showed that ① during high-O3 episodes, stagnant weather conditions with high temperature and solar radiation as well as low relative humidity were observed, and oxygenated VOCs and alkenes from anthropogenic VOCs contributed the most to the total ozone formation potential and OH reactivity (k·OH). ② The in-situ O3 variation was primarily affected by local photochemical production and export process horizontal to downwind areas or vertical to the upper layer. The reduction in local emissions was essential to alleviate O3 pollution in this region. ③ During high-O3 episodes, high concentrations of ·OH (10×106 cm-3) and HO2· (14×108 cm-3) radical drove and generated a high O3 production rate (daytime peak value reached 36×10-9 h-1). The reaction pathways of HO2·+NO and ·OH+NO2 contributed the most to the in-situ gross Ox photochemical production (63%) and photochemical destruction (50%), respectively. ④ Compared to those during low-O3 episodes, the photochemical regimes during high-O3 episodes were more inclined to be considered as the NOx-limited regime. Detailed mechanism modeling based on multiple scenarios further suggested that the synergic emission reduction strategy of NOx and VOC, while focusing on NOx emission alleviation, would be practical options for controlling local O3 pollutions. This method could also provide policy-related guidance for the precise O3 pollution prevention and control in other industrialized Chinese cities.

Integrating ambient carbonyl compounds provides insight into the constrained ozone formation chemistry in Zibo city of the North China Plain.

Quantifying the impact of carbonyl compounds (carbonyls) on ozone (O3) photochemical formation is crucial to formulating targeted O3 mitigation strategies. To investigate the emission source of ambient carbonyls and their integrated observational constraint on the impact of O3 formation chemistry, a field campaign was conducted in an industrial city (Zibo) of the North China Plain from August to September 2020. The site-to-site variations of OH reactivity for carbonyls were in accordance with the sequence of Beijiao (BJ, urban, 4.4 s-1) > Xindian (XD, suburban, 4.2 s-1) > Tianzhen (TZ, suburban, 1.6 s-1). A 0-D box model (MCMv3.3.1) was applied to assess the O3-precursor relationship influenced by measured carbonyls. It was found that without carbonyls constraint, the O3 photochemical production of the three sites was underestimated to varying degrees, and the biases of overestimating the VOC-limited degree were also identified through a sensitivity test to NOx emission changes, which may be associated with the reactivity of carbonyls. In addition, the results of the positive matrix factorization (PMF) model indicated that the main source of aldehydes and ketones was secondary formation and background (81.6% for aldehydes, 76.8% for ketones), followed by traffic emission (11.0% for aldehydes, 14.0% for ketones). Incorporated with the box model, we found that biogenic emission contributed the most to the O3 production at the three sites, followed by traffic emission as well as industry and solvent usage. Meanwhile, the relative incremental reactivity (RIR) values of O3 precursor groups from diverse VOC emission sources featured consistencies and differences at the three sites, which further highlights the importance of the synergetic mitigation of target O3 precursors at regional and local scales. This study will help to provide targeted policy-guiding O3 control strategies for other regions.

Ambient fine particulate matter exposures and oxidative protein damage in early pregnant women.

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM2.5) is unknown. We estimated the effect of PM2.5 exposures within seven days before blood collection on serum 3-nitrotyrosine (3-NT) and advanced oxidation protein products (AOPP) in 100 women with normal early pregnancy (NEP) and 100 women with clinically recognized early pregnancy loss (CREPL). Temporally-adjusted land use regression model was applied for estimation of maternal daily PM2.5 exposure. Daily nitrogen dioxide (NO2) exposure of each participant was estimated using city-level concentrations of NO2. Single-day lag effect of PM2.5 was analyzed using multivariable linear regression model. Net cumulative effect and distributed lag effect of PM2.5 and NO2 within seven days were analyzed using distributed lag non-linear model. In all 200 subjects, the serum 3-NT were significantly increased with the single-day lag effects (4.72%-8.04% increased at lag 0-2), distributed lag effects (2.32%-3.49% increased at lag 0-2), and cumulative effect within seven days (16.91% increased). The single-day lag effects (7.41%-10.48% increased at lag 0-1), distributed lag effects (3.42%-5.52% increased at lag 0-2), and cumulative effect within seven days (24.51% increased) of PM2.5 significantly increased serum 3-NT in CREPL group but not in NEP group. The distributed lag effects (2.62%-4.54% increased at lag 0-2) and cumulative effect within seven days (20.25% increased) of PM2.5 significantly increased serum AOPP in early pregnant women before the coronavirus disease (COVID-19) pandemic but not after that, similarly to the effects of NO2 exposures. In conclusion, PM2.5 exposures were associated with oxidative stress to protein in pregnant women in the first trimester, especially in CREPL women. Analysis of NO2 exposures suggested that combustion PM2.5 was the crucial PM2.5 component. Wearing masks may be potentially preventive in PM2.5 exposure and its related oxidative protein damage.

Ambient fine particulate matter exposures and human early placental inflammation.

The effect of fine particulate matter (PM2.5) on human early maternal-fetal interface is unknown. We explored the association between maternal exposure to ambient PM2.5 and inflammation in placental villus of 114 women with clinically recognized early pregnancy loss (CREPL) and 114 women with normal early pregnancy (NEP). Temporally-adjusted land use regression models were used to estimate maternal daily PM2.5 exposure during pregnancy. Villus interleukin-1beta (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured using multiplex cytokines detection platform. Single-day lag effect of PM2.5 exposure within ten days before early placental villus collection was estimated using multivariable linear regression model. Distributed lag and net cumulative effects of PM2.5 exposures within ten and 30 days before villus collection, as well as five single weeks during the periovulatory period, were estimated using distributed lag non-linear models. In all 228 subjects, after adjusting for group (CREPL or NEP), temporal confounders, and demographic characteristics, both single-day and distributed lag effects of PM2.5 exposure at lag 8 significantly increased villus IL-6; distributed lag effects of PM2.5 exposure in the first and second weeks before ovulation increased IL-1ß, and PM2.5 exposure in the third week after ovulation increased IL-6 and TNF-α. In CREPL, single-day lag effect significantly increased IL-1ß (at lag 1), IL-6 (at lag 8), and TNF-α (at lag 5); distributed lag effect increased IL-6 (at lag 4-lag 8) and TNF-α (at lag 4-lag 6); and cumulative effect within ten days before villus collection increased IL-6. There was no statistically significant cumulative effect in NEP. In summary, maternal PM2.5 exposure was associated with placental inflammation in human early pregnancy, particularly with increased villus IL-6 in CREPL. Whether maternal-fetal interface inflammation related to PM2.5 exposure during the periovulatory period or later contributes to CREPL or other adverse pregnancy outcomes requires further study.

[Size Distribution and Source Appointment of Road Particles During Winter in Tianjin].

The significant role of traffic emissions mixed from various sources in urban air pollution has been widely recognized. However, the corresponding contributions to the roadside particle distribution are poorly understood due to the mixed impacts of various sources. Particle number concentrations of different sizes at the roadside in Nankai District of Tianjin were continuously monitored using a portable aerosol particle spectrometer during the morning rush hour (07:30-09:20) from Nov. 9, 2018 to Jan. 6, 2019. Characteristic and influencing factors of particle size distributions were discussed combined with temperature and relative humidity data, while potential sources of particles at the roadside were identified based on size distribution analysis. The results showed that the average total particle number concentrations were 502 cm-3, and the concentrations of the accumulation mode and coarse mode were 500 cm-3 and 2 cm-3, respectively. The distribution of number concentrations at the roadside was unimodal and primarily concentrated at 0.25-0.50 µm, with peak sizes at 0.28-0.30 µm. The same distribution trend of particle number concentration and difference in the concentration in the same segment size were observed at different periods. Vehicle activity level was the main influencing factor of road particulate matter concentration on different weekdays; the probability of the high value of road particulate matter concentration was reduced by a reasonable combination of the vehicle tail numbers. Temperature and relative humidity were both found to be positively correlated with the number concentration of particles. With the increase in temperature and relative humidity, the total and peak particle number concentration showed an overall upward trend. In addition, the peak particle size increased from 0.28-0.30 µm to 0.35-0.40 µm when relative humidity was higher than 80%. Three sources, including road dust, brake and tire wear, and the aging particles from vehicle exhaust, were identified using positive matrix factorization in this study. Road dust contributed 8.6% of the total number concentration, which mainly consisted of particles with sizes above 5.00 µm. Brake and tire wear contributed 2.8% of the total number concentration of particles with a size range of 0.80-4.00 µm. The aging particles from vehicle exhaust contributed the most (88.5%), with a peak at 0.25-0.65 µm. The sources of roadside particles were mainly related to vehicle activity, whereas temperature and relative humidity also affected the particle number size distribution.

Short-term effects of the toxic component of traffic-related air pollution (TRAP) on lung function in healthy adults using a powered air purifying respirator (PAPR).

Short-term exposure to traffic-related air pollution (TRAP) are associated with reduced lung function. However, TRAP is a mixture of various gaseous pollutants and particulate matter (PM), and therefore it is unknown that which components of TRAP are responsible for the respiratory toxicity. Using a powered air-purifying respirator (PAPR), we conducted a randomized, double-blind, crossover trial in which 40 adults were exposed to TRAP for 2 h at the sidewalk of a busy road. During the exposure, the participants wore the PAPR fitted with a PM filter, a PM and volatile organic compounds (VOCs) filter, or a sham filter (no filtration, Sham mode). The participants were blinded to the type of filter in their PAPR, and experienced three exposures, once for each intervention mode in random order. We measured two lung function measures (forced expiratory volume in 1 s [FEV1] and forced vital capacity [FVC]) and an airway inflammation marker (fraction of exhaled nitric oxide [FENO]) before and immediately after each exposure, and further measured them at different time periods after exposure. We applied linear mixed effect models to estimate the effects of the interventions on the changes of lung function from baseline values after controlling for other covariates. Compared to baseline, exposing to TRAP decreased FEV1 and FVC, and increased FEV1/FVC and FENO in all three intervention modes. The mixed models showed that with the sham mode as reference, lung function and airway inflammation post exposure were significantly improved by filtering both PM and VOCs, but marginally affected by filtering only PM. In conclusion, the VOCs component of TRAP is responsible for the reduction in lung function caused by short-term exposure to TRAP. However, the result needs to be interpreted cautiously before further verified by laboratory experiment using purely isolated component(s) of TRAP.

Mass Concentration, Source and Health Risk Assessment of Volatile Organic Compounds in Nine Cities of Northeast China.

From April 2008 to July 2009, ambient measurements of 58 volatile organic compounds (VOCs), including alkanes, alkenes, and aromatics, were conducted in nine industrial cities (Shenyang, Fushun, Changchun, Jilin, Harbin, Daqing, Huludao, Anshan and Tianjin) of the Northeast Region, China (NRC). Daqing had the highest concentration of VOCs (519.68 ± 309.88 µg/m3), followed by Changchun (345.01 ± 170.52 µg/m3), Harbin (231.14 ± 46.69 µg/m3), Jilin (221.63 ± 34.32 µg/m3), Huludao (195.92 ± 103.26 µg/m3), Fushun (135.43 ± 46.01 µg/m3), Anshan (109.68 ± 23.27 µg/m3), Tianjin (104.31 ± 46.04 µg/m3), Shenyang (75.2 ± 40.09 µg/m3). Alkanes constituted the largest percentage (>40%) in concentrations of the quantified VOCs in NRC, and the exception was Tianjin dominated by aromatics (about 52.34%). Although alkanes were the most abundant VOCs at the cities, the most important VOCs contributing to ozone formation potential (OFP) were alkenes and aromatics. Changchun had the highest OFP (537.3 µg/m3), Tianjin had the lowest OFP (111.7 µg/m3). The main active species contributing to OFP in the nine cities were C2~C6 alkanes, C7~C8 aromatic hydrocarbons, individual cities (Daqing) contained n-hexane, propane and other alkane species. Correlation between individual hydrocarbons, B/T ratio and principal component analysis model (PCA) were deployed to explore the source contributions. The results showed that the source of vehicle exhausts was one of the primary sources of VOCs in all nine cities. Additionally, individual cities, such as Daqing, petrochemical industry was founded to be an important source of VOCs. The results gained from this study provided a large of useful information for better understanding the characteristics and sources of ambient VOCs incities of NRC. The non-carcinogenic risk values of the nine cities were within the safe range recognized by the U.S. Environmental Protection Agency (HQ < 1), and the lifetime carcinogenic risk values of benzene were 3.82 × 10−5~1.28 × 10−4, which were higher than the safety range specified by the US Environmental Protection Agency (R < 1.00 × 10−6). The results of risk values indicated that there was a risk of cancer in these cities.

Characteristics, Source Contributions, and Source-Specific Health Risks of PM2.5-Bound Polycyclic Aromatic Hydrocarbons for Senior Citizens during the Heating Season in Tianjin, China.

Polycyclic aromatic hydrocarbons (PAHs) have carcinogenic impacts on human health. However, limited studies are available on the characteristics, sources, and source-specific health risks of PM2.5-bound PAHs based on personal exposure data, and comparisons of the contributions of indoor and outdoor sources are also lacking. We recruited 101 senior citizens in the winter of 2011 for personal PM2.5 sample collection. Fourteen PAHs were analyzed, potential sources were apportioned using positive matrix factorization (PMF), and inhalational carcinogenic risks of each source were estimated. Six emission sources were identified, including coal combustion, gasoline emission, diesel emission, biomass burning, cooking, and environmental tobacco smoking (ETS). The contribution to carcinogenic risk of each source occurred in the following sequence: biomass burning > diesel emission > gasoline emission > ETS > coal combustion > cooking. Moreover, the contributions of biomass burning, diesel emission, ETS, and indoor sources (sum of cooking and ETS) to PAH-induced carcinogenic risk were higher than those to the PAH mass concentration, suggesting severe carcinogenic risk per unit contribution. This study revealed the contribution of indoor and outdoor sources to mass concentration and carcinogenic risk of PM2.5-bound PAHs, which could act as a guide to mitigate the exposure level and risk of PM2.5-bound PAHs.

A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China.

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 µm (PM2.5 and PM10) and nitrogen dioxide (NO2) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM2.5 and PM10 were consistently reliable, but the EV for NO2 had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM2.5 and PM10 model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the "Growth trajectories and air pollution" (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.