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SESAMe v3.0, a spatially explicit multimedia fate model with 50 × 50 km(2) resolution, has been developed for China to predict environmental concentrations of benzo[a]pyrene (BaP) using an atmospheric emission inventory for 2007. Model predictions are compared with environmental monitoring data obtained from an extensive review of the literature. The model performs well in predicting multimedia concentrations and distributions. Predicted concentrations are compared with guideline values; highest values with some exceedances occur mainly in the North China Plain, Mid Inner Mongolia, and parts of three northeast provinces, Xi'an, Shanghai, and south of Jiangsu province, East Sichuan Basin, middle of Guizhou and Guangzhou. Two potential future scenarios have been assessed using SESAMe v3.0 for 2030 as BaP emission is reduced by (1) technological improvement for coal consumption in energy production and industry sectors in Scenario 1 (Sc1) and (2) technological improvement and control of indoor biomass burning for cooking and indoor space heating and prohibition of open burning of biomass in 2030 in Scenario 2 (Sc2). Sc2 is more efficient in reducing the areas with exceedance of guideline values. Use of SESAMe v3.0 provides insights on future research needs and can inform decision making on options for source reduction.

Abstract Accurate estimation of the remaining useful life of lithium-ion batteries is critically important for electronic devices. In the existing literature, the widely applied model-based approaches for remaining useful battery life estimation are limited by the complexity of the electrochemical modeling required. In addition, data-driven approaches for remaining useful battery life estimation commonly define unreliable sliding window sizes empirically and the prediction accuracy of these approaches needs to be improved. To address the above issues, use of a hybrid neural network with the false nearest neighbors method is proposed in this paper. First, the false nearest neighbors method is used to calculate the sliding window size required for prediction. Second, a hybrid neural network that combines the advantages of a convolutional neural network with those of long short-term memory is designed for model training and prediction. Remaining useful life prediction experiments for batteries with various rated capacities are performed to verify the effectiveness of the proposed approach, and the results demonstrate that the proposed approach offers wide generality and reduced errors when compared with the other state-of-the-art methods.

Abstract In the present study, Loblolly pine biomass residue was converted to bio-oil in a two-step process, consisting of 1) fast pyrolysis in the presence of zeolite ZSM-5 as a catalyst to produce pyrolysis oil, 2) hydrogenation of pyrolysis oil using formic acid as the hydrogen source in presence of Ru/activated carbon catalyst. Pyrolysis oils were analyzed by 13C, 31P and HSQC-NMR and the results revealed that the zeolite-induced catalytic fast pyrolysis process led to effective demethoxylation, producing more catechol and p-hydroxy-phenyl hydroxyl groups in the bio-oils, resulting in a decrease in the methoxyl group content by about 85 % and rich aromatic structures in the pyrolysis oils. The properties of pyrolysis oil with and without zeolite were in the bio-oil range. Hydrogenated pyrolysis oil showed that 79 % of the aromatic protons are eliminated and 87 % of protons are aliphatic in nature, with no oxygen attached to the α-carbon.

Cattail-grass-derived porous carbon as high-capacity anode materials were prepared via high-temperature carbonization and activation with KOH. The samples exhibited different structures and morphologies with increasing treatment time. It was found that the cattail grass with activation treatment—1 (CGA-1) sample obtained at 800 °C for 1 h presented excellent electrochemical performance. As an anode material for lithium-ion batteries, CGA-1 showed a high charge–discharge capacity of 814.7 mAh g−1 at the current density of 0.1 A g−1 after 400 cycles, which suggests that it has a great potential for energy storage.

The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha− 1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year− 1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

Abstract Social acceptance of innovative technologies is a key element of an effective transition towards more sustainable energy economies. However, innovative technologies like genetic modification also tend to spark controversy and backlash. So far, efforts to inform the public about any risks and benefits of novel technologies not only have struggled to foster acceptance but also neglect the interdependent foundations of consumer decision-making. Through a controlled experiment with German consumers (N = 322), we examine whether consumer support and rejection of genetic modification in bioenergy crops is influenced by the statements and actions of actors throughout the supply chain. In specific, we show that the decision of energy companies to sell and support GM bioenergy positively impacts consumer decisions to support. To ensure that decision outcomes were specifically impacted by the expressions of corporate actors, we controlled for the content and valence of information by random assignment to one of three treatments in which participants received positive, negative, or balanced (risks and benefits) information. We find that negative messaging diminished support and increased rejection relative to the other treatments. Lastly, the statements and actions of corporate actors also exerted an indirect influence on consumer decisions through their interactions with social trust and labels, e.g. greater support by farmers had a positive influence only for those who are more generally trustworthy. Given these results, we anticipate more attention to the importance of actors such as farmers and energy companies for the social acceptance of novel technologies in the energy sphere.