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Atmospheric methane (CH4) is a potent greenhouse gas, and its mole fraction has more than doubled since the preindustrial era. Fossil fuel extraction and use are among the largest anthropogenic sources of CH4 emissions, but the precise magnitude of these contributions is a subject of debate. Carbon-14 in CH4 (14CH4) can be used to distinguish between fossil (14C-free) CH4 emissions and contemporaneous biogenic sources; however, poorly constrained direct 14CH4 emissions from nuclear reactors have complicated this approach since the middle of the 20th century. Moreover, the partitioning of total fossil CH4 emissions (presently 172 to 195 teragrams CH4 per year) between anthropogenic and natural geological sources (such as seeps and mud volcanoes) is under debate; emission inventories suggest that the latter account for about 40 to 60 teragrams CH4 per year. Geological emissions were less than 15.4 teragrams CH4 per year at the end of the Pleistocene, about 11,600 years ago, but that period is an imperfect analogue for present-day emissions owing to the large terrestrial ice sheet cover, lower sea level and extensive permafrost. Here we use preindustrial-era ice core 14CH4 measurements to show that natural geological CH4 emissions to the atmosphere were about 1.6 teragrams CH4 per year, with a maximum of 5.4 teragrams CH4 per year (95 per cent confidence limit)—an order of magnitude lower than the currently used estimates. This result indicates that anthropogenic fossil CH4 emissions are underestimated by about 38 to 58 teragrams CH4 per year, or about 25 to 40 per cent of recent estimates. Our record highlights the human impact on the atmosphere and climate, provides a firm target for inventories of the global CH4 budget, and will help to inform strategies for targeted emission reductions.

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Lo��ve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.

As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But historical records are unreliable and constant re-assessment of active residential PV locations is resource intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning set up based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available dataset, but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids.

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks.

Abstract Building energy consumption is vulnerable to climate change due to the direct relationship between outside temperature and space cooling/heating. This work quantifies how the relationship between climate change and building energy consumption varies across a range of building types at different spatiotemporal scales based on estimates in 925 U.S. locations. Large increases in building energy consumption are found in the summer (e.g., 39% increase in August for the secondary school building), especially during the daytime (e.g., >100% increase for the warehouse building, 5–6 p.m.), while decreases are found in the winter. At the spatial scale of climate-zones, annual energy consumption changes range from −17% to +21%, while at the local scale, changes range from −20% to +24%. Buildings in the warm-humid (Southeast) climate zones show larger changes than those in other regions. The variation of impact within climate zones can be larger than the variation between climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. The large variations found in the relationship between climate change and building energy consumption highlight the importance of assessing climate change impacts at local scales, and the need for adaptation/mitigation strategies tailored to different building types.

Abstract Radiative cooling is a passive cooling method by emitting heat to outer space without energy input. In this work we propose a cost effective double-layer coating embedded with nanoparticles for both nighttime and daytime radiative cooling. The top and bottom layers are acrylic resin embedded with titanium dioxide and carbon black particles, respectively responsible for reflecting the solar irradiation and emitting the heat in the atmospheric transparency window. The carbon black layer is considered as the black substrate. For the top layer, different sizes of titanium dioxide particles are examined, and 0.2 μm radius is found to give the best cooling performance. More than 90% of the solar irradiation can be reflected, and the average emissivity in the atmospheric transparency window is larger than 0.9 in most directions. A daytime net cooling power over 100 W/m2 is predicted at the ambient temperature. The cooling effect persists even if significant conduction and convection heat exchange is considered.

Symmetric flow cell cycling of a soluble phenothiazine.

Lithium-ion batteries (LIBs) are currently one of the most important electrochemical energy storage devices, powering electronic mobile devices and electric vehicles alike. However, there is a remarkable difference between their rate of production and rate of recycling. At the end of their lifecycle, only a limited number of LIBs undergo any recycling treatment, with the majority go to landfills or being hoarded in households. Further losses of LIB components occur because the the state-of-the-art LIB recycling processes are limited to components with high economic value, e.g., Co, Cu, Fe, and Al. With the increasing popularity of concepts such as “circular economy” (CE), new LIB recycling systems have been proposed that target a wider spectrum of compounds, thus reducing the environmental impact associated with LIB production. This review work presents a discussion of the current practices and some of the most promising emerging technologies for recycling LIBs. While other authoritative reviews have focused on the description of recycling processes, the aim of the present was is to offer an analysis of recycling technologies from a CE perspective. Consequently, the discussion is based on the ability of each technology to recover every component in LIBs. The gathered data depicted a direct relationship between process complexity and the variety and usability of the recovered fractions. Indeed, only processes employing a combination of mechanical processing, and hydro- and pyrometallurgical steps seemed able to obtain materials suitable for LIB (re)manufacture. On the other hand, processes relying on pyrometallurgical steps are robust, but only capable of recovering metallic components.