Microbial contribution estimated by clumped isotopologues (13CH3D and 12CH2D2) characteristics in a CO2 enhanced coal bed methane reservoir.

Carbon capture and storage (CCS) of CO2 is a key technology for substantially mitigating global greenhouse gas emissions. Determining the biogeochemical processes in host rocks after CO2 injection informs the viability of carbon storage as a long-term sink for CO2, the complexity of reservoir CH4 cycling, as well as the direct and indirect environmental impacts of this strategy. The doubly substituted ('clumped') isotopologues of methane (13CH3D and 12CH2D2) provide novel insights into methane origins and post-generation processing. Here, we report the chemical compositions of hydrocarbons (C1/C2+ molecular ratios), and methane bulk and clumped isotopes (δ13C, δD, Δ13CH3D and Δ12CH2D2) of a CO2 enhanced coal bed methane recovery (CO2-ECBM) area in Qinshui basin, China and is an analogue for carbon capture and storage. The clumped isotopologue compositions observed in the study area are generally consistent with a range of temperatures spanning 73 to 193 °C. The range in apparent temperature and correlations among clumped and bulk isotopic indices are best explained by mixing between a high maturity thermogenic methane (high in δ13C and δD, with a clumped isotope composition equilibrated near ∼249 °C) and biogenic methane formed or processed in the reservoir (low in δ13C and δD, with a clumped isotope composition equilibrated near 16-27 °C). We hypothesize that the biogenic endmember may result from slow methanogenesis and/or anaerobic oxidation of methane (AOM). This study demonstrates that the potential of methane clumped isotope approach to identify in situ microbial metabolic processes and their association with carbon cycling in CO2-ECBM area, improving our understanding of biogeochemical mechanisms in analogous geological reservoirs.

Spatiotemporal variation in sensitivity of urban vegetation growth and greenness to vegetation water content: Evidence from Chinese megacities.

Understanding the sensitivity of vegetation growth and greenness to vegetation water content change is crucial for elucidating the mechanism of terrestrial ecosystems response to water availability change caused by climate change. Nevertheless, we still have limited knowledge of such aspects in urban in different climatic contexts under the influence of human activities. In this study, we employed Google Earth Engine (GEE), remote sensing satellite imagery, meteorological data, and Vegetation Photosynthesis Model (VPM) to explore the spatiotemporal pattern of vegetation growth and greenness sensitivity to vegetation water content in three megacities (Beijing, Shanghai, and Guangzhou) located in eastern China from 2001 to 2020. We found a significant increase (slope > 0, p < 0.05) in the sensitivity of urban vegetation growth and greenness to vegetation water content (SLSWI). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both SLSWI and the trends of SLSWI, and spatial heterogeneity in SLSWI and the trends of SLSWI was also present among identical vegetation types within the same city. Additionally, both SLSWI of vegetation growth and greenness and the trend of SLSWI showed obvious spatial distribution differences (e.g., standard deviations of trends in SLSWI of open evergreen needle-leaved forest of GPP is 14.36 × 10-2 and standard deviations of trends in SLSWI of open evergreen needle-leaved forest of EVI is 10.16 × 10-2), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.

Polycyclic aromatic hydrocarbons in samples of Ryugu formed in the interstellar medium.

Polycyclic aromatic hydrocarbons (PAHs) contain ≲20% of the carbon in the interstellar medium. They are potentially produced in circumstellar environments (at temperatures ≳1000 kelvin), by reactions within cold (~10 kelvin) interstellar clouds, or by processing of carbon-rich dust grains. We report isotopic properties of PAHs extracted from samples of the asteroid Ryugu and the meteorite Murchison. The doubly-13C substituted compositions (Δ2×13C values) of the PAHs naphthalene, fluoranthene, and pyrene are 9 to 51‰ higher than values expected for a stochastic distribution of isotopes. The Δ2×13C values are higher than expected if the PAHs formed in a circumstellar environment, but consistent with formation in the interstellar medium. By contrast, the PAHs phenanthrene and anthracene in Ryugu samples have Δ2×13C values consistent with formation by higher-temperature reactions.

Kinship Verification Based on Cross-Generation Feature Interaction Learning.

Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification. Particularly, an effective collaborative weighting strategy is constructed to explore the characteristics of cross-generation relations by corporately extracting features of both parents and children image pairs. Specifically, we take parents and children as a whole to extract the expressive local and non-local features. Different from the traditional works measuring similarity by distance, we interpolate the similarity calculations as the interior auxiliary weights into the deep CNN architecture to learn the whole and natural features. These similarity weights not only involve corresponding single points but also excavate the multiple relationships cross points, where local and non-local features are calculated by using these two kinds of distance measurements. Importantly, instead of separately conducting similarity computation and feature extraction, we integrate similarity learning and feature extraction into one unified learning process. The integrated representations deduced from local and non-local features can comprehensively express the informative semantics embedded in images and preserve abundant correlation knowledge from image pairs. Extensive experiments demonstrate the efficiency and superiority of the proposed model compared to some state-of-the-art kinship verification methods.