[Spatial Distribution, Influencing Factors, Risk Assessment, and Source Analysis of Heavy Metals in Soil Profile of Farmland in the Guanzhong Plain].

In order to understand the spatial distribution, influencing factors, ecological risk characteristics, and sources of heavy metals in the soil profile (0-1 m) of farmland in the Guanzhong Plain, soil samples of 0-1 m were collected from 124 sites in the Guanzhong Plain. The contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in soil were analyzed at different depths (0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm, and 60-100 cm). The study generated the spatial distribution characteristics of soil heavy metals based on geostatistics and investigated the influencing factors of heavy metals based on structural equation modeling (SEM). In addition, the ecological environmental risks of soil heavy metals were evaluated using the methods of index of geo-accumulation (<i>I</i><sub>geo</sub>), single factor contaminant index (<i>P<sub>i</sub></i>), and potential ecological risk index (<i>E<sub>i</sub></i>). Finally, the sources of soil heavy metals were analyzed using the PMF model. The results showed that the average heavy metal content of <i>ω</i>(As), <i>ω</i>(Cd), <i>ω</i>(Cr), <i>ω</i>(Cu), <i>ω</i>(Ni), <i>ω</i>(Pb), and <i>ω</i>(Zn) in the 0-10 cm were 19.57, 0.71, 69.65, 21.91, 28.67, 17.54, and 73.77 mg·kg<sup>-1</sup>, respectively. The average values of As, Cd, Cr, Pb, and Zn exceeded the background values. With the increasing soil depth, all heavy metals showed a decreasing trend except for Cd and Ni. The spatial distribution indicated that different heavy metals in the same soil layer had different spatial distributions, and the spatial distribution of heavy metals also varied at different depths. The structural equation model showed that the factors affecting the distribution of heavy metals at different depths were deeply dependent. The ecological risk assessment showed that Cd at different depths had higher ecological risks, while the rest were considered minor risks. The sources of 0-40 cm soil layer were consistent, and the main sources were pesticides and herbicides, organic fertilizers and fertilizers, natural sources, and traffic-induced atmospheric subsidence and industrial sources; the sources of 40-100 cm were consistent, and the main sources were pesticides and herbicides, organic fertilizers and fertilizers, natural sources, and traffic-induced atmospheric subsidence.

Authors

Institutions

Publication Details

Journal
PubMed
Published
2025-04-08
DOI
https://doi.org/10.13227/j.hjkx.202403016
Primary Topic
Regional Development and Environment
Type
article
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

[Spatial Distribution, Influencing Factors, Risk Assessment, and Source Analysis of Heavy Metals in Soil Profile of Farmland in the Guanzhong Plain].

Jiangbo Qiao, Shuyi Li, Mingan Shao, Shu Zhu et al.
PubMed
Regional Development and Environment
article

[Spatial Distribution, Influencing Factors, Risk Assessment, and Source Analysis of Heavy Metals in Soil Profile of Farmland in the Guanzhong Plain].

Jiangbo Qiao, Shuyi Li, Mingan Shao, Shu Zhu, Yang Yu, Yuanjun Zhu
article en

Abstract

In order to understand the spatial distribution, influencing factors, ecological risk characteristics, and sources of heavy metals in the soil profile (0-1 m) of farmland in the Guanzhong Plain, soil samples of 0-1 m were collected from 124 sites in the Guanzhong Plain. The contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in soil were analyzed at different depths (0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm, and 60-100 cm). The study generated the spatial distribution characteristics of soil heavy metals based on geostatistics and investigated the influencing factors of heavy metals based on structural equation modeling (SEM). In addition, the ecological environmental risks of soil heavy metals were evaluated using the methods of index of geo-accumulation (<i>I</i><sub>geo</sub>), single factor contaminant index (<i>P<sub>i</sub></i>), and potential ecological risk index (<i>E<sub>i</sub></i>). Finally, the sources of soil heavy metals were analyzed using the PMF model. The results showed that the average heavy metal content of <i>ω</i>(As), <i>ω</i>(Cd), <i>ω</i>(Cr), <i>ω</i>(Cu), <i>ω</i>(Ni), <i>ω</i>(Pb), and <i>ω</i>(Zn) in the 0-10 cm were 19.57, 0.71, 69.65, 21.91, 28.67, 17.54, and 73.77 mg·kg<sup>-1</sup>, respectively. The average values of As, Cd, Cr, Pb, and Zn exceeded the background values. With the increasing soil depth, all heavy metals showed a decreasing trend except for Cd and Ni. The spatial distribution indicated that different heavy metals in the same soil layer had different spatial distributions, and the spatial distribution of heavy metals also varied at different depths. The structural equation model showed that the factors affecting the distribution of heavy metals at different depths were deeply dependent. The ecological risk assessment showed that Cd at different depths had higher ecological risks, while the rest were considered minor risks. The sources of 0-40 cm soil layer were consistent, and the main sources were pesticides and herbicides, organic fertilizers and fertilizers, natural sources, and traffic-induced atmospheric subsidence and industrial sources; the sources of 40-100 cm were consistent, and the main sources were pesticides and herbicides, organic fertilizers and fertilizers, natural sources, and traffic-induced atmospheric subsidence.

PubMedVol. 46(4)
Northwest A&F University (CN)
Regional Development and Environment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.