Effect of granular activated carbon addition on the effluent properties and fouling potentials of membrane-coupled expanded granular sludge bed process

© 2014 Elsevier Ltd. To mitigate membrane fouling of membrane-coupled anaerobic process, granular activated carbon (GAC: 50. g/L) was added into an expanded granular sludge bed (EGSB). A short-term ultrafiltration test was investigated for analyzing membrane fouling potential and underlying fouling mechanisms. The results showed that adding GAC into the EGSB not only improved the COD removal efficiency, but also alleviated membrane fouling efficiently because GAC could help to reduce soluble microbial products, polysaccharides and proteins by 26.8%, 27.8% and 24.7%, respectively, compared with the control system. Furthermore, excitation emission matrix (EEM) fluorescence spectroscopy analysis revealed that GAC addition mainly reduced tryptophan protein-like, aromatic protein-like and fulvic-like substances. In addition, the resistance distribution analysis demonstrated that adding GAC primarily decreased the cake layer resistance by 53.5%. The classic filtration mode analysis showed that cake filtration was the major fouling mechanism for membrane-coupled EGSB process regardless of the GAC addition

Income-based greenhouse gas emissions of nations

Accounting for greenhouse gas (GHG) emissions of nations is essential to understanding their importance to global climate change and help inform the policymaking on global GHG mitigation. Previous studies have made efforts to evaluate direct GHG emissions of nations (a.k.a. production-based accounting method) and GHG emissions caused by the final consumption of nations (a.k.a. consumption-based accounting method), but overlooked downstream GHG emissions enabled by primary inputs of individual nations and sectors (a.k.a. income-based accounting method). Here we show that the income-based accounting method reveals new GHG emission profiles for nations and sectors. The rapid development of mining industries drives income-based GHG emissions of resource-exporting nations (e.g., Australia, Canada, and Russia) during 1995–2009. Moreover, the rapid development of sectors producing basic materials and providing financial intermediation services drives income-based GHG emissions of developing nations (e.g., China, Indonesia, India, and Brazil) during this period. The income-based accounting can support supply side policy decisions and provide additional information for determining GHG emission quotas based on cumulative emissions of nations and designing policies for shared responsibilities

The influence of net-quarks on the yields and rapidity spectra of identified hadrons

Within a quark combination model, we study systematically the yields and rapidity spectra of various hadrons in central Au+Au collisions at $\sqrt{s_{NN}}= 200$ GeV. We find that considering the difference in rapidity between net-quarks and newborn quarks, the data of multiplicities, rapidity distributions for $\pi^{\pm}$, $K^{\pm}$, $p(\bar{p})$ and, in particular the ratios of charged antihadron to hadron as a function of rapidity, can be well described. The effect of net-quarks on various hadrons is analysed, and the rapidity distributions for $K^{0}_{s}$, $\Lambda(\bar{\Lambda})$, $\Sigma^{+}(\bar{\Sigma}^{_-})$, $\mathrm{\Xi^{-}}$ ($\mathrm{\bar{\Xi}^{_+}}$) and $\mathrm{\Omega^{-}}(\mathrm{\bar{\Omega}}^{_+})$ are predicted. We discuss the rapidity distribution of net-baryon, and find that it reflects exactly the energy loss of colliding nuclei.Comment: 8 pages, 7 figure

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method’s finite- ample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children

Socioeconomic Drivers of Greenhouse Gas Emissions in the United States

Existing studies examined the U.S.’s direct GHG emitters and final consumers driving upstream GHG emissions, but overlooked the U.S.’s primary suppliers enabling downstream GHG emissions and relative contributions of socioeconomic factors to GHG emission changes from the supply side. This study investigates GHG emissions of sectors in the U.S. from production-based (direct emissions), consumption-based (upstream emissions driven by final consumption of products), and income-based (downstream emissions enabled by primary inputs of sectors) viewpoints. We also quantify relative contributions of socioeconomic factors to the US’s GHG emission changes during 1995–2009 from both the consumption and supply sides, using structural decomposition analysis (SDA). Results show that income-based method can identify new critical sectors leading to GHG emissions (e.g., Renting of Machinery & Equipment and Other Business Activities and Financial Intermediation sectors) which are unidentifiable by production-based and consumption-based methods. Moreover, the supply side SDA reveals new factors for GHG emission changes: mainly production output structure representing product allocation pattern and primary input structure indicating sectoral shares in primary inputs. In addition to production-side and consumption-side GHG reduction measures, the U.S. should also pay attention to supply side measures such as influencing the behaviors of product allocation and primary inputs

Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

Co3O4 Nanocrystals on Graphene as a Synergistic Catalyst for Oxygen Reduction Reaction

Catalysts for oxygen reduction and evolution reactions are at the heart of key renewable energy technologies including fuel cells and water splitting. Despite tremendous efforts, developing oxygen electrode catalysts with high activity at low costs remains a grand challenge. Here, we report a hybrid material of Co3O4 nanocrystals grown on reduced graphene oxide (GO) as a high-performance bi-functional catalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). While Co3O4 or graphene oxide alone has little catalytic activity, their hybrid exhibits an unexpected, surprisingly high ORR activity that is further enhanced by nitrogen-doping of graphene. The Co3O4/N-doped graphene hybrid exhibits similar catalytic activity but superior stability to Pt in alkaline solutions. The same hybrid is also highly active for OER, making it a high performance non-precious metal based bi-catalyst for both ORR and OER. The unusual catalytic activity arises from synergetic chemical coupling effects between Co3O4 and graphene.Comment: published in Nature Material

Two-dimensional Transport Induced Linear Magneto-Resistance in Topological Insulator Bi2_2Se3_3 Nanoribbons

We report the study of a novel linear magneto-resistance (MR) under perpendicular magnetic fields in Bi2Se3 nanoribbons. Through angular dependence magneto-transport experiments, we show that this linear MR is purely due to two-dimensional (2D) transport, in agreement with the recently discovered linear MR from 2D topological surface state in bulk Bi2Te3, and the linear MR of other gapless semiconductors and graphene. We further show that the linear MR of Bi2Se3 nanoribbons persists to room temperature, underscoring the potential of exploiting topological insulator nanomaterials for room temperature magneto-electronic applications.Comment: ACS Nano, in pres