To grasp the impact of carbon metabolism on the evolution of "production-living-ecological" (PLE) space due to land use change in the Changsha-Zhuzhou-Xiangtan (CZT) urban agglomeration, this study delves into the temporal and spatial distribution of PLE space carbon metabolism by constructing a carbon flow model. We evaluate the influence of positive and negative carbon flows on carbon metabolism using ecological network analysis and utility assessment. Furthermore, we delve into the driving factors behind carbon metabolism through redundancy analysis (RDA). The findings of this study included mainly the following aspects. (1) From 2000 to 2020, the net carbon flow in the CZT urban agglomeration consistently remained negative, with the primary source of negative carbon flow being the transition from ecological space to production space. (2) Within the ecological utility network, the dominant ecological relationship shifted from a period of control and exploitation relationship (counted for 61.91%) between 2000 and 2005 to one of competition relationship that counted for 83.33% in 2005-2010, 47.62% in 2010-2015, and 66.67% in 2015-2020. Mutualism relationship, present in the 2000-2005 period, completely disappeared in subsequent years. (3) The value of the utility function M was 0.88, 0.36, 0.48, and 0.40 in four stages (all less than 1), which meant that PLE space evolution on regional carbon metabolism was negative. (4) The key drivers influencing carbon metabolism in PLE space were mainly Change in the Comprehensive Land Use Index (CL), Change in the Proportion of Manufacturing Land (CM), Change in the Proportion of Forestland (CF), and Change in the Proportion of Cultivated Land (CC). Carbon metabolism holds a critical role in the urban material and energy cycle. Studying carbon metabolism within PLE space carries great importance for regional carbon cycling, carbon emission and sequestration, efforts to mitigate climate change, and the maintenance of regional sustainable development.
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http://dx.doi.org/10.1007/s11356-023-31206-z | DOI Listing |
Environ Pollut
January 2025
School of Medicine, Taizhou University, Taizhou 318000, China.
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Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent, 9000, Belgium; Bio Base Europe Pilot Plant (BBEPP), Rodenhuizekaai 1, Ghent, 9042, Belgium. Electronic address:
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January 2025
The School of Material Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, 150040, PR China.
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December 2024
NanoBiosensors and Biodevices Lab, School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, 721302, India. Electronic address:
This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma samples using nickel-based electrochemical sensors and machine learning (ML). This strategy enables the detection of a panel of analytes without being limited by the selectivity of the transducer material and leaving accommodation of interference analysis to ML models. Here, we fabricated a non-enzymatic electrochemical sensor for L-lactic acid detection in complex mixtures and human plasma samples using nickel oxide (NiO) nanoparticle-modified glassy carbon electrodes (GCE).
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