COVID?19 Impact on the Concentration and Composition of Submicron Particulate Matter in a Typical City of Northwest China
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ABSTRACT:
SUBMITTER: Xu J
PROVIDER: S-EPMC7536917 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
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Project description:This study presents one of the first long term datasets including a statistical summary of PM2.5 concentrations obtained from one-year monitoring in 190 cities in China. We found only 25 out of 190 cities could meet the National Ambient Air Quality Standards of China, and the population-weighted mean of PM2.5 in Chinese cities are 61 μg/m(3), ~3 times as high as global population-weighted mean, highlighting a high health risk. PM2.5 concentrations are generally higher in north than in south regions due to relative large PM emissions and unfavorable meteorological conditions for pollution dispersion. A remarkable seasonal variability of PM2.5 is observed with the highest during the winter and the lowest during the summer. Due to the enhanced contributions from dust particles and open biomass burning, high PM2.5 abundances are also found in the spring (in Northwest and West Central China) and autumn (in East China), respectively. In addition, we found the lowest and highest PM2.5 often occurs in the afternoon and evening hours, respectively, associated with daily variation of the boundary layer depth and anthropogenic emissions. The diurnal distribution of the PM2.5-to-CO ratio consistently displays a pronounced peak during the afternoon periods, reflecting a significant contribution of secondary PM formation.
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Project description:ImportanceExposure to particulate matter (PM) has been associated with childhood asthma and wheeze. However, the specific associations between asthma and PM with an aerodynamic equivalent diameter of 1 μm or less (ie, PM1), which is a contributor to PM2.5 and potentially more toxic than PM2.5, remain unclear.ObjectiveTo investigate the association of early-life (prenatal and first year) exposure to size-segregated PM, including PM1, PM1-2.5, PM2.5, PM2.5-10, and PM10, with childhood asthma and wheeze.Design, setting, and participantsThis cross-sectional study was based on a questionnaire administered between June 2019 and June 2020 to caregivers of children aged 3 to 6 years in 7 Chinese cities (Wuhan, Changsha, Taiyuan, Nanjing, Shanghai, Chongqing, and Urumqi) as the second phase of the China, Children, Homes, Health study.ExposuresExposure to PM1, PM1-2.5, PM2.5, PM2.5-10, and PM10 during the prenatal period and first year of life.Main outcomes and measuresThe main outcomes were caregiver-reported childhood asthma and wheeze. A machine learning-based space-time model was applied to estimate early-life PM1, PM2.5, and PM10 exposure at 1 × 1-km resolution. Concentrations of PM1-2.5 and PM2.5-10 were calculated by subtracting PM1 from PM2.5 and PM2.5 from PM10, respectively. Multilevel (city and child) logistic regression models were applied to assess associations.ResultsOf 29 418 children whose caregivers completed the survey (15 320 boys [52.1%]; mean [SD] age, 4.9 [0.9] years), 2524 (8.6%) ever had wheeze and 1161 (3.9%) were diagnosed with asthma. Among all children, 18 514 (62.9%) were breastfed for more than 6 months and 787 (2.7%) had parental history of atopy. A total of 22 250 children (75.6%) had a mother with an educational level of university or above. Of the 25 422 children for whom information about cigarette smoking exposure was collected, 576 (2.3%) had a mother who was a current or former smoker during pregnancy and 7525 (29.7%) had passive household cigarette smoke exposure in early life. Early-life PM1, PM2.5, and PM10 exposure were significantly associated with increased risk of childhood asthma, with higher estimates per 10-μg/m3 increase in PM1 (OR, 1.55; 95% CI, 1.27-1.89) than in PM2.5 (OR, 1.14; 95% CI, 1.03-1.26) and PM10 (OR, 1.11; 95% CI, 1.02-1.20). No association was observed between asthma and PM1-2.5 exposure, suggesting that PM1 rather than PM1-2.5 contributed to the association between PM2.5 and childhood asthma. There were significant associations between childhood wheeze and early-life PM1 exposure (OR, 1.23; 95% CI, 1.07-1.41) and PM2.5 exposure (OR, 1.08; 95% CI, 1.01-1.16) per 10-μg/m3 increase in PM1 and PM2.5, respectively.Conclusions and relevanceIn this cross-sectional study, higher estimates were observed for the association between PM with smaller particles, such as PM1, vs PM with larger particles and childhood asthma. The results suggest that the association between PM2.5 and childhood asthma was mainly attributable to PM1.
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Project description:BackgroundCoronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China.MethodsWe obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 cases.ResultsWe found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m3 increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 μg/m3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively.ConclusionsPopulation mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.
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Project description:Sixty percent of the world ocean by area is contained in oligotrophic gyres [Longhurst A (1995) Prog Oceanog 36:77-16], the biomass of which is dominated by picophytoplankton, including cyanobacteria and picoeukaryotic algae, as well as picoheterotrophs. Despite their recognized importance in carbon cycling in the surface ocean, the role of small cells and their detrital remains in the transfer of particulate organic matter (POM) to the deep ocean remains disputed. Because oligotrophic marine conditions are projected to expand under current climate trends, a better understanding of the role of small particles in the global carbon cycle is a timely goal. Here we use the lipid profiles, radiocarbon, and stable carbon isotopic signatures of lipids from the North Pacific Subtropical Gyre to show that in the surface ocean, lipids from submicron POM (here called extra-small POM) are distinct from larger classes of suspended POM. Remarkably, this distinct extra-small POM signature dominates the total lipids collected at mesopelagic depth, suggesting that the lipid component of mesopelagic POM primarily contains the exported remains of small particles. Transfer of submicron material to mesopelagic depths in this location is consistent with model results that claim the biological origin of exported carbon should be proportional to the distribution of cell types in the surface community, irrespective of cell size [Richardson TL, Jackson GA (2007) Science 315:838-840]. Our data suggest that the submicron component of exported POM is an important contributor to the global biological pump, especially in oligotrophic waters.
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Project description:This study reports the concentrations of PM1 trace elements (As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se) content in highly mobile (F1), mobile (F2), less mobile (F3) and not mobile (F4) fractions in samples that were collected in the surroundings of power plants in southern Poland. It also reports source identification by enrichment factors (EF) and a principal component analysis (PCA). There is limited availability of scientific data concerning the chemical composition of dust, including fractionation analyses of trace elements, in the surroundings of power plants. The present study offers important results in order to fill this data gap. The data collected in this study can be utilized to validate air quality models in this rapidly developing area. They are also crucial for comparisons with datasets from similar areas all over the world. Moreover, the identification of the bioavailability of selected carcinogenic and toxic elements in the future might be used as output data for potential biological and population research on risk assessment. This is important in the context of air pollution being hazardous to human health.
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Project description:The Kathmandu Valley in Nepal experiences poor air quality, especially in the dry winter season. In this study, we investigated the concentration, chemical composition, and sources of fine and coarse particulate matter (PM2.5, PM10, and PM10-2.5) at three sites within or near the Kathmandu Valley during the winter of 2018 as part of the second Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE 2). Daily PM2.5 concentrations were very high throughout the study period, ranging 72-149 μg m-3 at the urban Ratnapark site in Kathmandu, 88-161 μg m-3 at the suburban Lalitpur site, and 40-74 μg m-3 at rural Dhulikhel on the eastern rim of the Kathmandu Valley. Meanwhile, PM10 ranged 194-309, 174-377, and 64-131 μg m-3, respectively. At the Ratnapark site, crustal materials from resuspended soil contributed an average of 11% of PM2.5 and 34% of PM10. PM2.5 was largely comprised of organic carbon (OC, 28-30% by mass) and elemental carbon (EC, 10-14% by mass). As determined by chemical mass balance source apportionment modeling, major PM2.5 OC sources were garbage burning (15-21%), biomass burning (10-17%), and fossil fuel (14-26%). Secondary organic aerosol (SOA) contributions from aromatic volatile organic compounds (13-23% OC) were larger than those from isoprene (0.3-0.5%), monoterpenes (0.9-1.4%), and sesquiterpenes (3.6-4.4%). Nitro-monoaromatic compounds-of interest due to their light-absorbing properties and toxicity-indicate the presence of biomass burning-derived SOA. Knowledge of primary and secondary PM sources can facilitate air quality management in this region.
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Project description:Exposure of small animals to cigarette smoke is widely used as a model to study the pathogenesis of chronic obstructive pulmonary disease. However, protocols and exposure systems utilised vary substantially and it is unclear how these different systems compare. We analysed the gene expression profile of six publically available murine datasets from different cigarette smoke-exposure systems and related the gene signatures to three clinical cohorts. 234 genes significantly regulated by cigarette smoke in at least one model were used to construct a 55-gene network containing 17 clusters. Increasing numbers of differentially regulated clusters were associated with higher total particulate matter concentrations in the different datasets. Low total particulate matter-induced genes mainly related to xenobiotic/detoxification responses, while higher total particulate matter activated immune/inflammatory processes in addition to xenobiotic/detoxification responses. To translate these observations to the clinic, we analysed the regulation of the revealed network in three human cohorts. Similar to mice, we observed marked differences in the number of regulated clusters between the cohorts. These differences were not determined by pack-year. Although none of the experimental models exhibited a complete alignment with any of the human cohorts, some exposure systems showed higher resemblance. Thus, depending on the cohort, clinically observed changes in gene expression may be mirrored more closely by specific cigarette smoke exposure systems. This study emphasises the need for careful validation of animal models.
| S-EPMC5165723 | biostudies-literature
Project description:BackgroundStudies found approximately linear short-term associations between particulate matter (PM) and mortality in Western communities. However, in China, where the urban PM levels are typically considerably higher than in Western communities, some studies suggest nonlinearity in this association. Health impact assessments (HIA) of PM in China have generally not incorporated nonlinearity in the concentration-response (C-R) association, which could result in large discrepancies in estimates of excess deaths if the true association is nonlinear.ObjectivesWe investigated nonlinearity in the C-R associations between with PM with aerodynamic diameter [Formula: see text] ([Formula: see text]) and mortality in Beijing, China, and the sensitivity of HIA to linearity assumptions.MethodsWe modeled the C-R association between [Formula: see text] and cause-specific mortality in Beijing, China (2009-2012), using generalized linear models (GLM). [Formula: see text] was included through either linear, piecewise-linear, or spline functions to investigate evidence of nonlinearity. To determine the sensitivity of HIA to linearity assumptions, we estimated [Formula: see text]-attributable deaths using both linear- and nonlinear-based C-R associations between [Formula: see text] and mortality.ResultsWe found some evidence that, for nonaccidental and circulatory mortality, the shape of the C-R association was relatively flat at lower concentrations of [Formula: see text], but then had a positive slope at higher concentrations, indicating nonlinearity. Conversely, the shape for respiratory mortality was positive and linear at lower concentrations of [Formula: see text], but then leveled off at the higher concentrations. Estimates of excess deaths attributable to short-term [Formula: see text] exposure were, in some cases, very sensitive to the linearity assumption in the association, but in other cases robust to this assumption.ConclusionsOur results demonstrate some evidence of nonlinearity in [Formula: see text]-mortality associations and that an assumption of linearity in this association can influence HIAs, highlighting the importance of understanding potential nonlinearity in the [Formula: see text]-mortality association at the high concentrations of [Formula: see text] in developing megacities like Beijing. https://doi.org/10.1289/EHP4464.
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