Wongkahn5505

Novel brominated flame retardants (NBFRs) were comprehensively investigated in both gaseous and particle phase samples collected using a high-volume active air sampler (HV-AAS) at the Chinese Great Wall Station in King George Island, West Antarctica from 2011 to 2018. The concentrations of ∑12NBFRs ranged from 0.27 to 3.0 pg m-3, with a mean value of 1.1 ± 0.50 pg m-3 and the levels showed a slightly increasing trend over the eight years. Decabromodiphenyl ethane (DBDPE) was the predominant NBFR with a relative contribution of 50% on average. Most of the studied NBFRs tended to distribute in gaseous phase with an average ratio of 72 ± 16% while NBFRs with higher log KOA values had higher proportions in particle phase. The gas/particle partitioning models were employed to evaluate the environmental behavior of NBFRs. Compared to the equilibrium-state-based model, the steady-state-based model performed much better to predict the gas/particle partitioning of NBFRs in the West Antarctic atmosphere. Additionally, no temperature dependence was found for NBFRs except rac-(1R,2R,5R,6R)-1,2,5,6-tetrabromocyclooctane (β-TBCO). The annual mean concentrations of ∑12NBFRs showed a significantly negative correlation with the frequency of east-southeast (ESE, 112.5°) wind and calm wind (~0 m s-1) (p  less then  0.05), and a significantly positive correlation with the frequency of wind from northwest interval (west to north-northwest, 270° to 337.5°) (p  less then  0.05), suggesting a significant effect of air mass from the ocean area. Furthermore, the chiral signature of NBFRs showed commonly non-racemic residue in the atmosphere. The enantiomer fractions (EF) of rac-(1R,2R)-1,2-dibromo-(4S)-4-((1R)-1,2-dibromoethly)cyclohexane (α-TBECH) and β-TBCO were 0.115-0.962 and 0.281-0.795, revealing secondary sources of NBFRs, e.g., seawater-air exchange and/or non-racemic residue in the source regions. As far as we know, this is one of very few studies on NBFRs in the Antarctic atmosphere. V.Short-term exposure to air pollution has been associated with exacerbation of respiratory diseases such as asthma. Substantial heterogeneity in effect estimates has been observed between previous studies. This study aims to quantify the local burden of daily asthma symptoms in asthmatic children in a medium-sized city. Air pollution exposure was estimated using the nearest sensor in a fine resolution urban air quality sensor network in the city of Eindhoven, the Netherlands. Bayesian estimates of the exposure response function were obtained by updating a priori information from a meta-analysis with data from a panel study using a daily diary. Five children participated in the panel study, resulting in a total of 400 daily diary records. Positive associations between NO2 and lower respiratory symptoms and medication use were observed. The odds ratio for any lower respiratory symptoms was 1.07 (95% C.I. 0.92, 1.28) expressed per 10 μg m-3 for current day NO2 concentration, using data from the panel study only (uninformative prior). Odds ratios for dry cough and phlegm were close to unity. The pattern of associations agreed well with the updated meta-analysis. The meta-analytic random effects summary estimate was 1.05 (1.02, 1.07) for LRS. Credible intervals substantially narrowed when adding prior information from the meta-analysis. The odds ratio for lower respiratory symptoms with an informative prior was 1.06 (0.99, 1.14). Burden of disease maps showed a strong spatial variability in the number of asthmatic symptoms associated with ambient NO2 derived from a regression kriging model. In total, 70 cases of asthmatic symptoms can daily be associated with NO2 exposure in the city of Eindhoven. We conclude that Bayesian estimates are useful in estimation of specific local air pollution effect estimates and subsequent local burden of disease calculations. With the fine resolution air quality network, neighborhood-specific burden of asthmatic symptoms was assessed. Farmland soil contamination of heavy metal(loid)s (HM) derived from smelting activities is a global concern, owing to its potential threat for human health through food chain. This study aims to evaluate total contents and bioavailability of HMs (Pb, Zn, Tl, Cd, Cu, As, Ag, Co, Cr and Ni) in farmland soils distributed over ten different villages from a former artisanal zinc smelting area in the northwest Guizhou province, China. The results showed that most of the studied soils still exhibited exceptionally high enrichment of Pb, Zn, Cd and As. High levels of bioavailable HMs were also observed in some samples, which may enter the human food chain through agricultural activities. Further analyses by Scanning Transmission Electron Microscopy - Energy Dispersive Spectroscopy (STEM-EDS), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) showed the presence of Zn smelting by-products such as Fe oxides, ZnO and PbSO4 even in nanoscale particles retained by the soils. Elemental mapping by EDS confirmed a close association of the studied HMs with the smelting waste particles. All these results signify that high levels of HM-contamination from historical artisanal zinc smelting activities still persist and threaten the health of local residents, despite the fact that the major industrial-derived-contamination period ended >15 years ago. Our findings highlight pivotal concerns in similar artisanal-smelting-affected farmland soils of suspected contamination, due to less-expected toxic elements such as Tl, which may cause high ecological health risks. Pyrintegrin cell line Soil erosion and fine particle transport are two of the major challenges in food security and water quality for the growing global population. Information of the areas prone to erosion is needed to prevent the release of pollutants and the loss of nutrients. Sediment fingerprinting is becoming a widely used tool to tackle this problem, allowing to identify the sources of sediments in a catchment. Methods in fingerprinting techniques are still under discussion with tracer selection at the centre of the debate. We propose a novel method, termed as consensus ranking (CR), that combines the predictions of single-tracer models to identify non-conservative tracers. In this context, a numerical procedure to quantify the predictions of individual tracers is first delivered. The scoring function to rank the tracers is based on several random debates between tracers in which the tracer that prevents consensus is discarded. Based on these results, a conservativeness index (CI) is presented along with a clustering method to identify groups of similar tracers.