A dataset of heavy metals and nutrients in surface sediments from the Qing-Chuan-Gan section of the upper Yellow River in 2020

<p indent="0mm">The Qinghai-Sichuan-Gansu section of the upper reaches of the Yellow River is situated within the ecologically fragile Tibetan Plateau. As a result of the combined impact of climate change and human activities, the ecological environment in this region is characterized by both diversity and complexity. To study the spatial distribution of heavy metals and nutrients in the surface sediments within the study area of the Yellow River, we established a total of 18 sampling sites were set along the main stream of the Yellow River as well as its major tributaries, including Tao River, Huangshui River, Hei River, and Bai River, from September to October 2020, collecting nearshore surface sediment samples <sc>(0-10 cm)</sc> to measure the concentrations of eight heavy metals (cadmium, chromium, zinc, copper, mercury, nickel, lead and arsenic) and six nutrients (total carbon, total phosphorus, total nitrogen, total sulfur, available phosphorus, and organic carbon). The distribution patterns of these elements within the study area were subsequently examined. The dataset’s accuracy was ensured through laboratory measurements of all parameters according to standard methods, followed by multiple cross-checks for data calibration and review. The establishment of this dataset provides essential foundational data for assessing sediment environmental quality and pollution risk in the upper reaches of the Yellow River, thereby serving as a fundamental basis for scientifically safeguarding ecologically sensitive areas in this region.

Authors

Publication Details

Journal
China Scientific Data
Published
2025-05-26
DOI
https://doi.org/10.11922/11-6035.ncdc.2024.0017.zh
Primary Topic
Heavy metals in environment
Type
article
Field-Weighted Citation Impact
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article

A dataset of heavy metals and nutrients in surface sediments from the Qing-Chuan-Gan section of the upper Yellow River in 2020

Hao Ding, Hualei Yuan, Dongying Song, Xiaoming Gao et al.
China Scientific Data
Heavy metals in environment
article

A dataset of heavy metals and nutrients in surface sediments from the Qing-Chuan-Gan section of the upper Yellow River in 2020

Hao Ding, Hualei Yuan, Dongying Song, Xiaoming Gao, Ying Wei, Changcun Guo, Jiannan Zhao, Xuejun Li, Jing Dong, Jingxiao Zhang, Ya Gao
article en

Abstract

<p indent="0mm">The Qinghai-Sichuan-Gansu section of the upper reaches of the Yellow River is situated within the ecologically fragile Tibetan Plateau. As a result of the combined impact of climate change and human activities, the ecological environment in this region is characterized by both diversity and complexity. To study the spatial distribution of heavy metals and nutrients in the surface sediments within the study area of the Yellow River, we established a total of 18 sampling sites were set along the main stream of the Yellow River as well as its major tributaries, including Tao River, Huangshui River, Hei River, and Bai River, from September to October 2020, collecting nearshore surface sediment samples <sc>(0-10 cm)</sc> to measure the concentrations of eight heavy metals (cadmium, chromium, zinc, copper, mercury, nickel, lead and arsenic) and six nutrients (total carbon, total phosphorus, total nitrogen, total sulfur, available phosphorus, and organic carbon). The distribution patterns of these elements within the study area were subsequently examined. The dataset’s accuracy was ensured through laboratory measurements of all parameters according to standard methods, followed by multiple cross-checks for data calibration and review. The establishment of this dataset provides essential foundational data for assessing sediment environmental quality and pollution risk in the upper reaches of the Yellow River, thereby serving as a fundamental basis for scientifically safeguarding ecologically sensitive areas in this region.

China Scientific DataVol. 10(2)
Life below water
Openalex Percentile: Top 90%
Heavy metals in environment
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