Evidence map›Paper›PMID 36429919›Full record

ArticleInternational journal of environmental research and public health2022

Spatio-Temporal Variation and Its Driving Forces of Soil Organic Carbon along an Urban-Rural Gradient: A Case Study of Beijing.

Bingrui Liu, Jiacheng Qian, Ran Zhao, Qijun Yang, Kening Wu, Huafu Zhao, Zhe Feng, Jianhui Dong

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Bingrui LiuSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
Jiacheng QianSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
Ran ZhaoCollege of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.ORCID 0000-0002-6075-6702
Qijun YangSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
Kening WuSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
Huafu ZhaoSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
Zhe FengSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.ORCID 0000-0002-7369-6761
Jianhui DongSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid urbanization has reshaped land cover and the ecological environment, potentially improving or deteriorating soil organic carbon (SOC). However, the response of SOC to urbanization has not yet been fully exploited. Herein, by using the land-use transfer matrix, the Sen & Mann-Kendall tests, the Hurst index, and a geographical and temporal weighted regression (GTWR) model, as well as an urban-rural gradient perspective, we assessed the dynamic response of SOC to Beijing's urbanization from 2001 to2015 and identified the main drivers. The results found that SOC stock decreased by 7651.50 t C during the study period. SOC density varied significantly along an urban-rural gradient, with high value areas mainly being located in remote mountainous rural areas and low value areas mainly being located in urban areas on the plains. There was an uneven variation in SOC density across the urban-rural gradient, with suburban areas (25-40 km away from urban cores) losing the most SOC density while urban areas and rural areas remained relatively unchanged. GTWR model revealed the spatio-temporal non-flat stability of various driving forces. Precipitation, the proportion of forest, the proportion of grassland, the population, distance to the urban center, the slope, and the silt content are the main factors related to SOC stock change. As a result, we suggest policy makers reconceptualize the uneven variation in the SOC between urban and rural areas, emphasize suburban areas as a target for controlling SOC loss, and take into consideration the spatial and temporal heterogeneity of the factors influencing SOC stock when evaluating policies.

Indexed as

CarbonSoilBeijingForestsUrbanizationCarbonSoilBeijing cityclimate changeGTWR modelland usesoil organic carbonurban–rural gradient

Identifiers

PMID36429919
PMCPMC9690215

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.