ALBERT

Description: Interferometric Synthetic Aperture Radar (InSAR) technology has millimeter level measurement accuracy and has great advantages in urban land subsidence monitoring. Meanwhile, multispectral remote sensing technique can also provide a large amount of urban features changes information for analyzing the causes of land subsidence. In this study, SAR and multispectral images are both used to monitor and analyze land subsidence in Fuzhou of China. 115 scenes Sentinel-1 SAR images from May 2017 to May 2021 are used based on the Persistent Scatterers Interferometric (PSI) method to evaluate the land subsidence in Fuzhou, while Sentinel-2 multispectral images are used to evaluate several remote sensing indexes. During SAR data processing, Generic Atmospheric Correction Online Service for InSAR (GACOS) data is used to remove atmospheric errors for higher accuracy land subsidence. In order to analyze the relationship between the land subsidence and land cover changes in urban areas, the Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) and Modified Normalized Difference Water Index (MNDWI) of the main subsidence areas are obtained based on Sentinel-2 multispectral images from 2016 to 2021. In the end, it is found that the land subsidence in some areas exceeded 12 mm/year in Fuzhou. The time series of four areas with severe subsidence were analyzed, and the cumulative subsidence reached about 60 mm. Besides, the spatial distribution and temporal changes of vegetation, buildings and water bodies in these areas were obtained based on the multispectral data, it is found there is very less relationship between the land subsidence and the urban features. It is concluded that that the main causes of the land subsidence are the changes of land internal components such as groundwater and others.

Description: In the present work, the magnetic local time and latitude (MLT and MLat) distributions of ionospheric large-spatial-scale (〉20° MLat) electromagnetic ion cyclotron waves were investigated using high-resolution 50-Hz geomagnetic field data from Swarm A and B satellites. Both parallel and transverse waves were studied in a comparative manner for different geomagnetic activities and seasons. Frequent occurrences of large-spatial-scale waves in the South Atlantic Anomaly and North America, where the parallel waves propagate over the longest distance, were observed. Waves appear mostly in the 02–10 MLT sector wherein the pre-noon parallel waves propagate farthest in latitudinal range. With the enhancement of geomagnetic activity, both transverse and parallel waves increase in occurrence. The dayside occurrence rate is higher during weak geomagnetic activity, whereas the situation is reversed on the nightside and duskside. The dayside waves are located outside the mid-latitude trough, and the nightside waves are located near (inside) the equatorward boundary of the mid-latitude trough. Large-spatial-scale waves tend to occur at the equinoxes when the absolute value of the dipole tilt angle is less than 10°, while the long-distance transmission in the waveguide occurs in the pre-noon in summer. Parallel waves propagate in the region where the electron density is higher than that of the transverse waves. There is a close relationship between EMIC wave and electron density oscillation.

Description: The Xing'an-Mongolian Orogenic Belt (XMOB) is a key area to study the tectonic evolution of northeast Asia, as it has retained the records for the processes of both the closure of the Paleo-Asian Ocean and the Mongol-Okhotsk Ocean and the subduction of the (Paleo-)Pacific plate. We constrained the lithospheric structure across the XMOB by receiver function imaging and shear wave splitting analysis from a dense seismic array. The lithosphere–asthenosphere boundary (LAB) is coherently imaged in different tectonic blocks. The mid-lithospheric discontinuity is also identified at ~75-100 km depth over the deeper LAB (~110-130 km) beneath the western side of the North-South Gravity Lineament (NSGL), roughly at the same depth as the LAB beneath the eastern side of the NSGL. Distinct patterns in seismic anisotropy were identified which are roughly separated by the NSGL. The large variations in lithospheric thickness, seismic anisotropy and crustal structure between the two sides of the NSGL indicate that it might likely caused by different tectonic processes in the two sides, indicating that the NSGL might represent the western boundary influenced by the subduction of the (Paleo-)Pacific Plate. The eastern side of the NSGL is characterized by a thinned lithosphere of less than 100 km and dominant NW-SE fast shear wave polarization direction, which reflects considerable lithospheric deformation probably related to the Mesozoic-Cenozoic subduction of the (Paleo-)Pacific plate. An integration of our results and geological observations suggests that the area in the west of the NSGL may not have been affected by the (Paleo-)Pacific subduction.

Description: Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Description: Typical resistant REE phosphates xenotime and monazite are important REE carriers in various types of rocks but the supergene mobility of REE in these minerals remains controversial. In this study, we hypothesize that microbes drive the natural weathering of these resistant REE phosphates. We conducted room-temperature bio-weathering experiments of xenotime concentrate with a common soil bacterium (Bacillus thuringiensis, Bt) isolated from a regolith-hosted REE deposit. Our results showed that Bt was able to promote the dissolution of xenotime and monazite in the concentrate, and the release of REE was enhanced by up to two orders of magnitude. In the bio-weathering medium buffered at pH = 6, the apparent release rates of total REE were in the range of 10−13–10−12 mol·m−2·s−1, with Y releasing at the fastest rates of ∼10−13 mol·m−2·s−1. Furthermore, the estimated dissolution rate of monazite (∼10−9 g·m−2·s−1) was one order of magnitude higher than that of xenotime (∼10−10 g·m−2·s−1) due to a more refractory nature of xenotime determined by its chemical and mineralogical characteristics. On account of the extremely low solubility of REE phosphates, portions of the released REE could be re-precipitated as meta-stable phosphates during mineral dissolution, resulting in the underestimation of the release of REE from primary minerals. Bt could produce various organic acids and acidify the media, promoting the dissolution of resistant phosphates through proton- and ligand-promoted mechanisms. The results of our study suggest that microbes have a high potential to facilitate REE liberation from resistant xenotime and monazite, posing new insight into the biogeochemical cycling of REE on Earth's surface.

Description: A waste-to-protein system that integrates a range of waste-to-protein upgrading technologies has the potential to converge innovations on zero-waste and protein security to ensure a sustainable protein future. We present a global overview of food-safe and feed-safe waste resource potential and technologies to sort and transform such waste streams with compositional quality characteristics into food-grade or feed-grade protein. The identified streams are rich in carbon and nutrients and absent of pathogens and hazardous contaminants, including food waste streams, lignocellulosic waste from agricultural residues and forestry, and contaminant-free waste from the food and drink industry. A wide range of chemical, physical, and biological treatments can be applied to extract nutrients and convert waste-carbon to fermentable sugars or other platform chemicals for subsequent conversion to protein. Our quantitative analyses suggest that the waste-to-protein system has the potential to maximise recovery of various low-value resources and catalyse the transformative solutions toward a sustainable protein future. However, novel protein regulation processes remain expensive and resource intensive in many countries, with protracted timelines for approval. This poses a significant barrier to market expansion, despite accelerated research and development in waste-to-protein technologies and novel protein sources. Thus, the waste-to-protein system is an important initiative to promote metabolic health across lifespans and tackle the global hunger crisis.

Description: Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations.

Description: A correction to this paper has been published: 10.1140/epjc/s10052-021-09344-w