“…The average OC/EC ratio increased during LP, from 3.8 to 5.4, both lower than that in eastern Himalaya, India (4.2 and 5.7 during NP and LP), whereas, higher than the ratio of OC to EC in Guangzhou (2.8 in the winter of 2019–2020) and Delhi (2.3 and 2.3 in 2019 and 2020) ( Chatterjee et al, 2021 ; Huang et al, 2022 ; Sharma et al, 2022 ). The differences in OC/EC ratios between these regions might arise from the differences in source emissions.…”
Section: Resultsmentioning
confidence: 81%
“…In addition, the correlation coefficients between PM 2.5 and EC during NP and LP were 0.81 and 0.83 ( P < 0.01), respectively, revealing the strong correlations between them. Compared to other studies, the mean total carbon (TC) level (12.8 and 12.5 μg/m 3 in the two periods) in Zhengzhou was higher than that in Guangzhou of China from December 24, 2019 to January 7, 2020 (10.9 μg/m 3 ), while lower than that in Shijiazhuang, China (16.5 and 21.0 μg/m 3 in NP and LP) and Delhi of India in 2019 and 2020 with the mean TC concentrations of 22.8 and 20.4 μg/m 3 ( Huang et al, 2022 ; Feng et al, 2022 ; Sharma et al, 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 86%
“…S4), the mixing height of the boundary layer rose, the wind speed increased as well (Fig. S4), facilitating in the dispersion of pollutants and resulting in a fall in SOC concentration ( Huang et al, 2022 ). In the evening, on the one hand, pollutants accumulated up in the evening due to the deteriorating meteorological conditions and the decreased atmospheric boundary layer height; on the other hand, pollutant emissions substantially increased during rush hour, leading to further secondary conversions, and consequently, there was a recurrence of the SOC concentration peak.…”
“…The average OC/EC ratio increased during LP, from 3.8 to 5.4, both lower than that in eastern Himalaya, India (4.2 and 5.7 during NP and LP), whereas, higher than the ratio of OC to EC in Guangzhou (2.8 in the winter of 2019–2020) and Delhi (2.3 and 2.3 in 2019 and 2020) ( Chatterjee et al, 2021 ; Huang et al, 2022 ; Sharma et al, 2022 ). The differences in OC/EC ratios between these regions might arise from the differences in source emissions.…”
Section: Resultsmentioning
confidence: 81%
“…In addition, the correlation coefficients between PM 2.5 and EC during NP and LP were 0.81 and 0.83 ( P < 0.01), respectively, revealing the strong correlations between them. Compared to other studies, the mean total carbon (TC) level (12.8 and 12.5 μg/m 3 in the two periods) in Zhengzhou was higher than that in Guangzhou of China from December 24, 2019 to January 7, 2020 (10.9 μg/m 3 ), while lower than that in Shijiazhuang, China (16.5 and 21.0 μg/m 3 in NP and LP) and Delhi of India in 2019 and 2020 with the mean TC concentrations of 22.8 and 20.4 μg/m 3 ( Huang et al, 2022 ; Feng et al, 2022 ; Sharma et al, 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 86%
“…S4), the mixing height of the boundary layer rose, the wind speed increased as well (Fig. S4), facilitating in the dispersion of pollutants and resulting in a fall in SOC concentration ( Huang et al, 2022 ). In the evening, on the one hand, pollutants accumulated up in the evening due to the deteriorating meteorological conditions and the decreased atmospheric boundary layer height; on the other hand, pollutant emissions substantially increased during rush hour, leading to further secondary conversions, and consequently, there was a recurrence of the SOC concentration peak.…”
“… 8 Generally, BC was emitted by incomplete combustion of fuels in vehicles and industrial activities. 44 In the urban area of Guangzhou, traffic emission was the most important source of carbonaceous aerosols, 45 indicating that promoting clean energy vehicles and relieving traffic congestion might be a key strategy for reducing BC mission and related AR risk.…”
Background:
Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter
in aerodynamic diameter (
)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring.
Objectives:
We aimed to investigate the association of prenatal exposure to
and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs.
Methods:
This prospective birth cohort study involved 657 mother–child pairs from Guangzhou, China. Prenatal exposure to residential
mass and its components [black carbon (BC), organic matter (OM), sulfate (
), nitrate (
), and ammonium (
)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of
components and examine the modification effects of maternal PUFA levels.
Results:
Approximately
and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal
was
.
was positively associated with the risk of developing AR [hazard ratio
; 95% confidence interval (CI): 1.16, 2.96 per
] and its symptoms (
; 95% CI: 1.22, 2.62 per
) after adjustment for confounders. Similar associations were observed between individual
components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to
and its components.
Conclusion:
Prenatal exposure to
and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of
on respiratory allergy.
https://doi.org/10.1289/EHP13524
“…It is worth noting that sources of CA may change significantly with the implementation of control measures. For example, Zhang et al (2015) reported that biomass burning was the main source of OC in Guangzhou [37], while coal combustion and traffic emissions became the main sources of OC in 2020 [38]. Therefore, the timely reanalysis of the sources of CA and their compositional and light-absorbing properties is also necessary.…”
Carbonaceous aerosols (CAs), including elemental carbon (EC) and organic carbon (OC), have become the dominant component in PM2.5 in many Chinese cities, and it is imperative to address their spatiotemporal variations and sources in order to continually improve air quality. In this study, the mass concentrations and light absorption properties of EC and OC in PM2.5 were investigated at diverse sites in Guangzhou, in the winter of 2020 and the autumn of 2021, using the DRI Model 2015 thermal–optical carbon analyzer. The results showed that total EC and organic matter (OM = OC × 1.8) could account for nearly 30% of the PM2.5 mass concentrations. Secondary production was the most important source for OC, with secondary OC (SOC) percentages in the OC as high as 72.8 ± 7.0% in autumn and 68.4 ± 13.1% in winter. Compared to those in 2015, OC and EC concentrations were reduced by 25.4% and 73.4% in 2021, highlighting the effectiveness of control measures in recent years. The absorption coefficient of brown carbon at 405 nm (babs,BrC,405) decreased by over 40%, and the mass absorption coefficient (MAC) at 405 nm of total carbon (TC) decreased by over 30%. EC and OC concentrations and the light absorption of black carbon (babs,BC,405) showed no significant diurnal differences in both autumn and winter mainly because the reduction in anthropogenic emissions at night was compensated by the lowering of the boundary layer. Differentially, babs,BrC,405 was significantly lower during daytime than at night in autumn, probably due to the daytime photobleaching effect. The sources of EC, OC, BC, and BrC were preliminarily diagnosed by their correlation with typical source markers. In autumn, babs,BrC,405 might be related to biomass burning and coal combustion, while babs,BC,405 were largely related to vehicle emissions and coal combustion. In winter, babs,BrC,405 was closely related to coal combustion.
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