Evidence map›Paper›PMID 39582012›Full record

ArticleScientific reports2024

Source apportionment and driving factor identification for typical watersheds soil heavy metals of Tibetan Plateau based on receptor models and geodetector.

Cang Gong, Jun Tan, Weiqing Yang, Changhai Tan, Lang Wen, Jiufen Liu, Liming Gan

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Cang GongResearch Center of Applied Geology of China Geological Survey, Chengdu, 610039, China. 360395302@qq.com.
Jun TanResearch Center of Applied Geology of China Geological Survey, Chengdu, 610039, China. tanjun@yeah.net.
Weiqing YangResearch Center of Applied Geology of China Geological Survey, Chengdu, 610039, China.
Changhai TanResearch Center of Applied Geology of China Geological Survey, Chengdu, 610039, China.
Lang WenResearch Center of Applied Geology of China Geological Survey, Chengdu, 610039, China.
Jiufen LiuKey Laboratory of Natural Resource Coupling Process and Effects, Beijing, 100055, China.
Liming GanNatural Resources Comprehensive Survey Command Center of China Geological Survey, Beijing, 100055, China. 393004120@qq.com.

Funding

Geological Survey Project of China Geological Survey DD20242769Geological Survey Project of China Geological Survey DD20243076Geological Survey Project of China Geological Survey DD20243098Geological Survey Project of China Geological Survey DD20243127Open Foundation of the Key Laboratory of Natural Resource Coupling Process and Effects No.2023KFKTB011
6 · The paper itself

Abstract

The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Indexed as

Driving factor identificationGeodetectorReceptor modelsSoil heavy metalsSource apportionmentTibetan plateau

Identifiers

PMID39582012
PMCPMC11586407

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