Characterization of heavy metal pollution in agricultural soils and the driving mechanism of nutrient factors in the intermountain basin area

To clarify the characteristics of heavy metal (Hg, As, Cd, Cu, Zn, Pb, Ni, Cr) contamination in farmland within mountainous basin areas and investigate the influence mechanisms of soil nutrient factors (organic carbon SOC, pH, total nitrogen TN, total phosphorus TP, total potassium TK) and the C:N:P ratio on heavy metal pollution, This study selected a typical mountainous valley area within the Huangshan section of the Xin’anjiang River basin. A total of 300 soil samples were collected. Kriging interpolation was employed to assess the spatial distribution of heavy metal contamination. A positive definite factor matrix model was used for source analysis of soil heavy metals. Combined with redundancy analysis, correlation analysis, and structural equation modeling, the study investigated the influence mechanisms and distribution drivers of soil factors on soil heavy metal contamination. Results indicate: (1) Overall, heavy metal contamination in farmland across the study area was predominantly mild or below (>75%). Contamination was concentrated in the lower-elevation areas east of Xidi Town and west of Lantian Town, with notably elevated Cr, Cu, Zn, and Cd concentrations in these zones; (2) Atmospheric sources contributed most to Hg (72.8%); As was primarily influenced by industrial sources (73.9%); natural sources were the main contributors for Cr, Ni, and Cu, accounting for 52.5%, 51.7%, and 41.8%, respectively; soil Zn primarily originated from natural and transportation sources (31.0% and 42.8%); Agricultural sources dominate Cd contributions (83.9%); Pb is primarily influenced by natural sources and solid waste sources (40.9% and 43.1%, respectively); (3) Redundancy analysis indicates soil nutrient factors explain 21.5% of heavy metal pollution, with TN contributing the most at 63.4%; Both linear regression and structural equation modeling results indicate a significant negative correlation ( p < 0.05) between the C:P ratio and the comprehensive soil heavy metal pollution index. This may be due to the fact that farmland in mountainous basin areas has a higher proportion of P compared to the average C:N:P ratio of subtropical farmland, and P can effectively bind with heavy metals (Cu, Cd, Pb, Ni, Zn) to form a stable state. This study systematically evaluates the heavy metal pollution in the Huangshan section of the Xin’an River from contamination assessment, source analysis, and impact mechanisms, offering a theoretical basis for preventing and controlling heavy metal pollution and ensuring land safety in similar regions.

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Publication Details

Journal
Frontiers in Earth Science
Published
2025-09-25
DOI
https://doi.org/10.3389/feart.2025.1645105
Citations
1
Primary Topic
Heavy metals in environment
Type
article
Field-Weighted Citation Impact
0.50
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Characterization of heavy metal pollution in agricultural soils and the driving mechanism of nutrient factors in the intermountain basin area

Yang Xiao, Huakun Zhou, Zhengyu Yang, Zhen Song et al.
1 citations
Frontiers in Earth Science
Heavy metals in environment
0.50
article

Characterization of heavy metal pollution in agricultural soils and the driving mechanism of nutrient factors in the intermountain basin area

Yang Xiao, Huakun Zhou, Zhengyu Yang, Zhen Song, Yonghong Ma, Yuan Zhao, Baili Geng
article en
1 citations

Abstract

To clarify the characteristics of heavy metal (Hg, As, Cd, Cu, Zn, Pb, Ni, Cr) contamination in farmland within mountainous basin areas and investigate the influence mechanisms of soil nutrient factors (organic carbon SOC, pH, total nitrogen TN, total phosphorus TP, total potassium TK) and the C:N:P ratio on heavy metal pollution, This study selected a typical mountainous valley area within the Huangshan section of the Xin’anjiang River basin. A total of 300 soil samples were collected. Kriging interpolation was employed to assess the spatial distribution of heavy metal contamination. A positive definite factor matrix model was used for source analysis of soil heavy metals. Combined with redundancy analysis, correlation analysis, and structural equation modeling, the study investigated the influence mechanisms and distribution drivers of soil factors on soil heavy metal contamination. Results indicate: (1) Overall, heavy metal contamination in farmland across the study area was predominantly mild or below (>75%). Contamination was concentrated in the lower-elevation areas east of Xidi Town and west of Lantian Town, with notably elevated Cr, Cu, Zn, and Cd concentrations in these zones; (2) Atmospheric sources contributed most to Hg (72.8%); As was primarily influenced by industrial sources (73.9%); natural sources were the main contributors for Cr, Ni, and Cu, accounting for 52.5%, 51.7%, and 41.8%, respectively; soil Zn primarily originated from natural and transportation sources (31.0% and 42.8%); Agricultural sources dominate Cd contributions (83.9%); Pb is primarily influenced by natural sources and solid waste sources (40.9% and 43.1%, respectively); (3) Redundancy analysis indicates soil nutrient factors explain 21.5% of heavy metal pollution, with TN contributing the most at 63.4%; Both linear regression and structural equation modeling results indicate a significant negative correlation ( p < 0.05) between the C:P ratio and the comprehensive soil heavy metal pollution index. This may be due to the fact that farmland in mountainous basin areas has a higher proportion of P compared to the average C:N:P ratio of subtropical farmland, and P can effectively bind with heavy metals (Cu, Cd, Pb, Ni, Zn) to form a stable state. This study systematically evaluates the heavy metal pollution in the Huangshan section of the Xin’an River from contamination assessment, source analysis, and impact mechanisms, offering a theoretical basis for preventing and controlling heavy metal pollution and ensuring land safety in similar regions.

Frontiers in Earth ScienceVol. 13
China University of Political Science and Law (CN), Ministry of Natural Resources (CN), China Geological Survey (CN), Huangshan University (CN), China Nonferrous Metals Changsha Investigation Design Institute (CN)
Openalex Percentile: Top 34%
Heavy metals in environment
0.50
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