Organic Sulphur Transfers in Coke Oven Gas via Noncatalytic Partial Oxidation

The organic sulfur transformation was studied during coke oven gas to produce syngas via noncatalytic partial oxidation. The concentration of CS2 and thiophene was examined in syngas by sulfide detector. For comparison, the sulfur transfer was also studied in coke oven gas under dry and hydrous conditions. When the ratio of O2 / Gas was 0.32, complete thiophene and about 83% of CS2 in feed gas could be transformed via noncatalytic partial oxidation in the dry condition. It was mainly because of burner nozzle unique structure forming local hyperthemia, which benefited OH, O free radical and active atoms. During steam transforming to produce syngas, the ratio of water to carbon was less than 3, a higher ratio of O2/Gas favored sulfur transformation. However, compared to dry feed, transforming rate of CS2 and thiophene was decreased. This indicates that the steam added was disadvantageous to the transformation of organic sulphur during the production of syngas by noncatalytic partial oxidation, steam and mass H2S in feed gas, resulting in the decrease of local hyperthermia temperature and the formation of organic sulfu

Mesoporous High‐Surface‐Area Copper–Tin Mixed‐Oxide Nanorods: Remarkable for Carbon Monoxide Oxidation

Mesoporous, high‐surface‐area Cu–Sn mixed‐oxide nanorods were fabricated for the first time by nanocasting with the use of mesoporous KIT‐6 silica as the hard template. The Cu–Sn nanorods are significantly more active than 1 % Pd/SnO2 for the oxidation of CO and possesses long‐term durability and potent water resistance; they thus have the potential to replace noble metal catalysts for emission‐control processes.In rod we trust: Mesoporous, high‐surface‐area Cu–Sn nanorods are successfully fabricated for the first time by nanocasting with the use of KIT‐6 silica as the hard template; these nanomaterials are significantly more active than 1 % Pd/SnO2 for the oxidation of CO, and furthermore, they have the potential to replace noble metal catalysts for emission control.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137536/1/cctc201600221.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137536/2/cctc201600221-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137536/3/cctc201600221_am.pd

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.Peer reviewe

Impacts of Land Use and Salinization on Soil Inorganic and Organic Carbon in the Middle-lower Yellow River Delta

ACKNOWLEDGEMENTS This study was financially supported by the National Natural Science Foundation of China (Nos. 41877028 and 41205104). This work also contributes to the activities of N-Circle projects, a UK-China Virtual Joint Centre on Nitrogen, funded by the Newton Fund via Biotechnology and Biological Sciences Research Council (BBSRC) (No. BB/N013484/1Peer reviewedPostprin

Temporal and spatial variations of atmospheric methane concentration and δ13 C-CH4 near the surface on the Millor Peninsula, East Antarctica

During the 22nd Chinese Antarctic Research Expedition (CHINARE222, 2005/06) , the atmospheric gas samples near the surface were taken on the Millor Peninsula and adjacent areas, east Antarctica, using Tedlar gas bags. The methane(CH4) concentration and δ13 C-CH4 in those samples were analyzed in the laboratory. The average value of atmospheric CH4 concentration on this peninsula was (1.87 ± 0.12) × 10(-6) , slightly higher than the global average CH4 concentration. The summertime variation of CH4 concentrations showed a large fluctuation, corresponding to the variation of air temperatures. The average δ13 C-CH4 was ( -38.26 ± 0.52) ‰. CH4 concentrations and δ13 C-CH4 at different sites varied from 1.74 × 10(-6) to 2.56 ×10(-6) and from -39.31‰ to - 31.25‰, respectively. The factors affecting CH4 concentrations and δ13 C-CH4 values have also been discussed in this study

Liquid-Based Cytopathology Test: A Novel Method for Diagnosing Pulmonary Mucormycosis in Bronchial Brushing Samples

Background: Pulmonary mucormycosis, a relatively rare but severe pulmonary fungal disease with a high mortality rate, is difficult to diagnose in immunocompromised patients. Conventional cytopathology (CCP) examination of respiratory samples can help detect Mucorales, but its diagnostic sensitivity is poor. The aim of this study was to assess the first application of liquid-based cytopathology test (LCT) to detect Mucorales.Methods: A total of 33 pairs of bronchial brushing samples from 27 patients diagnosed as pulmonary mucormycosis by fiberoptic bronchoscopy biopsy were prepared as slides using both CCP and LCT. LCT and CCP used the same cytology brush to obtain samples at the same site during the same time as the fiberoptic bronchoscopy biopsy. All samples were stained with Papanicolaou, GMS and PAS. CCP and LCT slides were evaluated from the rate of positive detection, 8 cytomorphological features and 7 background features.Results: LCT-prepared slides showed a higher positive rate of Mucorales detection than CCP-prepared slides for Papanicolaou’s staining [28/33 (84.85%) vs. 15/33 (45.45%), p = 0.001] and for “special staining” with GMS and PAS [29/33 (87.88%) vs. 18/33 (54.55%), p = 0.003]. Clearer smear background and more distinct stereoscopic cytopathological features were observed in LCT. Messy yarn-like necrosis observed in conventionally prepared 75.76% (25/33) samples was cytomorphological suggestive for the diagnosis of mucormycosis.Conclusion: This retrospective study suggests that LCT may be better than CCP to detect Mucorales in bronchial brushing samples from patients with pulmonary mucormycosis

Association between the variability of non-high-density lipoprotein cholesterol and the neutrophil-to-lymphocyte ratio in patients with coronary heart disease

BackgroundLowering lipid variability may be a potential strategy for improving the inflammatory state in patients with coronary heart disease (CHD). This study investigated the association between the variability of non-high-density lipoprotein cholesterol (non-HDL-C) and the neutrophil-to-lymphocyte ratio (NLR).MethodsThis study enrolled 2,711 CHD patients subjected to percutaneous coronary intervention (PCI). During the 1-year follow-up period after PCI, the variability of non-HDL-C was assessed using standard deviation (SD), coefficient of variation (CV), and variability independent of mean (VIM). NLR was calculated as the ratio of absolute neutrophil count to absolute lymphocyte count. The relationship between the non-HDL-C variability and the average NLR level during follow-ups was examined using a linear regression analysis.ResultsThe mean age of the patients was 64.4 ± 10.8 years, with 72.4% being male. The average NLR level was 2.98 (2.26–4.14) during the follow-up (1 year after PCI). The variability of non-HDL-C was 0.42 (0.26–0.67) for SD, 0.17 (0.11–0.25) for CV, and 0.02 (0.01–0.03) for VIM. A locally weighted scatterplot smoothing curve indicates that the average levels of NLR increased with increasing variability of non-HDL-C. Regardless of the variability assessment method used, non-HDL-C variability was significantly positively associated with the average NLR level during follow-ups: SD [β (95% CI) = 0.681 (0.366–0.996)], CV [β (95% CI) = 2.328 (1.458–3.197)], and VIM [β (95% CI) = 17.124 (10.532–23.715)]. This association remained consistent across subgroups stratified by age, gender, diabetes, and hypertension.ConclusionThe variability of non-HDL-C was positively associated with NLR in patients with CHD, suggesting that reducing non-HDL-C variability may improve the low-grade inflammatory state in CHD patients

Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests : a cross-platform comparison

© The Author(s), 2018. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Science of The Total Environment 644 (2018): 439-451, doi:10.1016/j.scitotenv.2018.06.269.Characterized by the noticeable seasonal patterns of photosynthesis, mid-to-high latitude forests are sensitive to climate change and crucial for understanding the global carbon cycle. To monitor the seasonal cycle of the canopy photosynthesis from space, several remote sensing based indexes, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI), have been implemented within the past decades. Recently, satellite-derived sun-induced fluorescence (SIF) has shown great potentials of providing retrievals that are more related to photosynthesis process. However, the potentials of different canopy measurements have not been thoroughly assessed in the context of recent advances of new satellites and proposals of improved indexes. Here, we present a cross-site intercomparison of one emerging remote sensing based index of phenological index (PI) and two SIF datasets against the conventional indexes of NDVI, EVI and LAI to capture the seasonal cycles of canopy photosynthesis. NDVI, EVI, LAI and PI were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, while SIF were evaluated from Global Ozone Monitoring Experiment-2 (GOME-2) and Orbiting Carbon Observatory-2 (OCO-2) observations. Results indicated that GOME-2 SIF was highly correlated with gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR) during the growing seasons. Key phenological metrics captured by SIF from GOME-2 and OCO-2 matched closely with photosynthesis phenology as inferred by GPP. However, the applications of OCO-2 SIF for phenological studies may be limited only for a small range of sites (at site-level) due to a limited spatial sampling. Among the MODIS estimations, PI and NDVI provided most reliable predictions of start of growing seasons, while no indexes accurately captured the end of growing seasons.This work was supported by the Chinese Arctic and Antarctic Administration, National Natural Science Foundation of China (Grant Nos. 41676176 and 41676182), the Chinese Polar Environment Comprehensive Investigation, Assessment Program (Grant No. 312231103). This work was also supported by the Fundamental Research Funds for the 440 Central Universities2020-07-1

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles