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Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China
Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO(2) concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222844/ https://www.ncbi.nlm.nih.gov/pubmed/35741538 http://dx.doi.org/10.3390/e24060817 |
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author | Ma, Yiran He, Xinyi Wu, Rui Shen, Chenhua |
author_facet | Ma, Yiran He, Xinyi Wu, Rui Shen, Chenhua |
author_sort | Ma, Yiran |
collection | PubMed |
description | Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO(2) concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO(2)) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO(2) data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO(2) concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO(2) concentration. Highlight: (1) An XCO(2) concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO(2) concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed. |
format | Online Article Text |
id | pubmed-9222844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92228442022-06-24 Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China Ma, Yiran He, Xinyi Wu, Rui Shen, Chenhua Entropy (Basel) Article Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO(2) concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO(2)) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO(2) data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO(2) concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO(2) concentration. Highlight: (1) An XCO(2) concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO(2) concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed. MDPI 2022-06-11 /pmc/articles/PMC9222844/ /pubmed/35741538 http://dx.doi.org/10.3390/e24060817 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ma, Yiran He, Xinyi Wu, Rui Shen, Chenhua Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title | Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title_full | Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title_fullStr | Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title_full_unstemmed | Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title_short | Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO(2) Concentration Time Series during 2010–2018 over China |
title_sort | spatial distribution of multi-fractal scaling behaviours of atmospheric xco(2) concentration time series during 2010–2018 over china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222844/ https://www.ncbi.nlm.nih.gov/pubmed/35741538 http://dx.doi.org/10.3390/e24060817 |
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