4.7 Article

Source identification and spatial distribution of metals in soils in a typical area of the lower Yellow River, eastern China

期刊

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
卷 25, 期 21, 页码 21106-21117

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-018-2256-z

关键词

Metals; Soil; Multivariate statistical analysis; Geostatistics; Sources identification; Spatial distribution

资金

  1. National Natural Science Foundation of China [41601549, 41701604]
  2. Natural Science Foundation of Shandong Province [ZR2016DQ11]
  3. Open Foundation of Estuarine and Coastal State Key Laboratory [SKLEC-KF201710]

向作者/读者索取更多资源

In this study, 234 soil samples were recently collected from Gaoqing County (a typical area of the lower Yellow River) to determine the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. Multivariate statistical analyses such as correlation analysis, principal components analysis, and one-way ANOVA were applied to identify the source of metals in the soil. Geostatistical methods were used to analyze the spatial structure and distribution of the metals. The results indicated that the mean contents of all metals exceeded the background value of the lower Yellow River, especially for As, Cu, and Hg (1.23, 1.20, and 1.29 times that of the BV, respectively), indicating that these metals were enriched in the study area to different degrees. The results derived from multivariate analysis suggested that As, Cd, Cr, Cu, Ni, Pb, and Zn were mainly controlled by the combination of human activities and soil parent material, and the human activities included industrial emissions, traffic emissions, and agricultural practices. In addition, Hg mainly originated from anthropogenic inputs, such as textile printing, plastics processing, and petrochemical engineering. The contents of metals in different types of land use and parent materials are clearly different. The mean content for eight elements in urban construction land was significantly higher than that of the other land use types; in addition to Hg, the mean content of the other elements was the highest in the lacustrine deposit. The elements of As, Cd, Cr, Cu, Ni, Pb, and Zn had similar hotspots in the urban area, indicating the significant human influence. In addition, these seven metals showed high values in the southeast lacustrine deposit area. The high-value areas of Hg were concentrated in the southwest and northeast study area, which were consistent with the spatial pattern of the industrial sites.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Biodiversity Conservation

Identifying the sources, spatial distributions, and pollution status of heavy metals in soils from the southern coast of Laizhou Bay, eastern China

Jianshu Lv, Qing Xia, Tang Yan, Manlin Zhang, Zheng Wang, Linyu Zhu

HUMAN AND ECOLOGICAL RISK ASSESSMENT (2019)

Article Environmental Sciences

Multi-scale analysis of heavy metals sources in soils of Jiangsu Coast, Eastern China

Jianshu Lv, Yameng Wang

CHEMOSPHERE (2018)

Article Environmental Sciences

An integrated approach to identify quantitative sources and hazardous areas of heavy metals in soils

Jianshu Lv, Yang Liu

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

Article Green & Sustainable Science & Technology

Future Impacts of Climate Change and Land Use on Multiple Ecosystem Services in a Rapidly Urbanizing Agricultural Basin, China

Yang Liu, Jun Bi, Jianshu Lv

SUSTAINABILITY (2018)

Article Biodiversity Conservation

Source apportionment and health risk quantification for heavy metal sources in soils near aluminum-plastic manufacturing facilities in northeast China

Jun Li, Xibo Xu, Jianshu Lv, Quanyuan Wu, Mengyan Ren, Jianfei Cao, Peiyuan Liu

HUMAN AND ECOLOGICAL RISK ASSESSMENT (2020)

Article Spectroscopy

Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine

Xibo Xu, Mengyan Ren, Jianfei Cao, Quanyuan Wu, Peiyuan Liu, Jianshu Lv

SPECTROSCOPY LETTERS (2020)

Article Environmental Sciences

Pollution status and ecological risk of heavy metals in the soils of five land-use types in a typical sewage irrigation area, eastern China

Chunfang Li, Jianfei Cao, Lei Yao, Quanyuan Wu, Jianshu Lv

ENVIRONMENTAL MONITORING AND ASSESSMENT (2020)

Article Engineering, Environmental

The application of geostatistical analysis and receptor model for the spatial distribution and sources of potentially toxic elements in soils

Zhao Jin, Lixia Zhang, Jianshu Lv, Xuefei Sun

Summary: This study used geostatistical analysis to identify the spatial patterns, sources, and risk probabilities of potentially toxic elements in soil. The results revealed that geological factors and urban industrial emissions were the main contributors to soil contamination by potentially toxic elements.

ENVIRONMENTAL GEOCHEMISTRY AND HEALTH (2021)

Article Environmental Sciences

Combining finite mixture distribution, receptor model, and geostatistical simulation to evaluate heavy metals pollution in soils: Source and spatial pattern

Linyu Zhu, Lixia Zhang, Jining Wang, Jianshu Lv

Summary: This study proposed a combined method using FMDM, PMF, and geostatistical independent simulation for investigating the land degradation due to HMs pollution, which proved valid in identifying sources and spatial patterns of soil HMs.

LAND DEGRADATION & DEVELOPMENT (2021)

Article Environmental Sciences

Source apportionment of potentially toxic elements in soils of the Yellow River Delta Nature Reserve, China: The application of three receptor models and geostatistical independent simulation

Mengna Zhang, Jianshu Lv

Summary: The study conducted soil sampling and analysis in the Yellow River Delta Nature Reserve to identify the sources and spatial distribution of potentially toxic elements (PTEs). Different receptor models and geostatistical simulation were used to determine the pollution sources and hazardous areas of PTEs, providing important references for soil pollution assessment and management.

ENVIRONMENTAL POLLUTION (2021)

暂无数据