Methods for quantifying methane (CH) emissions from biogas plants are needed, in order to ensure that emissions are within acceptable levels and to identify options for emission mitigation. Two emission measuring approaches were used at four biogas plants: an on-site approach, whereby emission sources were identified and subsequently quantified one at a time, and a ground-based remote sensing approach, which was applied to measure total CH emissions. The emissions were between 5.5 and 13.5 kg CH h from the four plants, measured using ground-based remote sensing. Even though the measurements were performed on the same days at each facility, the sum of on-site emission rates varied between the remote sensing measurements (up to ∼100%). Several factors may have caused this difference: emission sources not measured using an on-site approach and short-time emission variation. On-site measurements showed that the majority of the emissions often occurred from just a few sources.
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http://dx.doi.org/10.1016/j.biortech.2018.08.080 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
View Article and Find Full Text PDFNat Commun
January 2025
Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Monitoring methane (CH) emissions from terrestrial ecosystems is essential for assessing the relative contributions of natural and anthropogenic factors leading to climate change and shaping global climate goals. Fires are a significant source of atmospheric CH, with the increasing frequency of megafires amplifying their impact. Global fire emissions exhibit large spatiotemporal variations, making the magnitude and dynamics difficult to characterize accurately.
View Article and Find Full Text PDFSci Data
January 2025
Division: Geosciences | Permafrost Research, Alfred Wegener Institute - Helmholtz Center for Polar and Marine Research, Telegrafenberg A45, 14473, Potsdam, Germany.
This study presents a new dataset of remote sensing-derived Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia. We use the Google Earth Engine to obtain TSLA data for approx. 2.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA.
Despite rapid developments of wearable self-powered sensors, it is still elusive to decouple the simultaneously applied multiple input signals. Herein, we report the design and demonstration of stretchable thermoelectric porous graphene foam-based materials via facile laser scribing for self-powered decoupled strain and temperature sensing. The resulting sensor can accurately detect temperature with a resolution of 0.
View Article and Find Full Text PDFBiol Rev Camb Philos Soc
January 2025
School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, Victoria, 3800, Australia.
Techniques for non-invasive sampling of ecophysiological data in wild animals have been developed in response to challenges associated with studying captive animals or using invasive methods. Of these, drones, also known as Unoccupied Aerial Vehicles (UAVs), and their associated sensors, have emerged as a promising tool in the ecophysiology toolkit. In this review, we synthesise research in a scoping review on the use of drones for studying wildlife ecophysiology using the PRISMA-SCr checklist and identify where efforts have been focused and where knowledge gaps remain.
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