Concentrations and Bioavailability of Heavy Metals in Sediments of Riverine Wetlands Located in the Huaihe River Watershed, China

2014 ◽  
Vol 43 (6) ◽  
pp. 830-837 ◽  
Author(s):  
Hezhong Yuan ◽  
Wei Pan ◽  
Zhengjie Zhu ◽  
Qifang Geng ◽  
Pengshan Li ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Huaihe River ◽  
Riverine Wetlands ◽  
River Watershed ◽  
The Huaihe River

Spatial distribution characteristics and source identification of heavy metals in river waters of the Huaihe River Basin, China

2018 ◽  
Vol 69 (5) ◽  
pp. 840 ◽  
Author(s):  
Jiqiang Yang ◽  
Yun Wan ◽  
Jingjing Li ◽  
Dawei Zou ◽  
Xin Leng ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
River Basin ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
River Waters ◽  
The Huaihe River

Rapid rates of industrialisation and urbanisation have led to heavy metal contamination of many rivers in China. Identification of the main sources of heavy metal contamination in river waters and description of their spatial distribution are essential for the control of river water pollution. In this study, water samples were collected from 218 sampling sites on rivers of the Huaihe River Basin during summer 2014. Fourteen heavy metals were detected (As, Ba, Co, Cr, Fe, Pb, Mn, Mo, Ni, Zn, Se, Sn, Sr and V). The concentrations of these heavy metals showed significant regional variation and the areas could be divided into four groups based on pollution levels: a pollution-free group (Group C), a low pollution group (Group D), a moderate pollution group (Group A), and a high pollution group (Group B). Pearson correlation coefficients verified the common sources of some of the heavy metals. Further analysis revealed that the release of effluents associated with mining, smelting, welding, fertilisers, pesticides and the chemical and electronics industries are the principal sources of heavy metal contamination in the waters of rivers of the Huaihe River Basin.

Geochemical normalization and assessment of heavy metals (Cu, Pb, Zn, and Ni) in sediments from the Huaihe River, Anhui, China

CATENA ◽  
2015 ◽  
Vol 129 ◽  
pp. 30-38 ◽  
Author(s):  
Jie Wang ◽  
Guijian Liu ◽  
Lanlan Lu ◽  
Jiamei Zhang ◽  
Houqi Liu
Keyword(s):  
Heavy Metals ◽  
Huaihe River ◽  
The Huaihe River ◽  
Geochemical Normalization

scholarly journals Ecological Risk Assessment Of Heavy Metals In Sediments Of A Riverine Wetland, Huaihe River Watershed, China

2015 ◽  
Vol 22 (2) ◽  
pp. 231-242 ◽  
Author(s):  
Hezhong Yuan ◽  
Wei Pan ◽  
Zhengjie Zhu ◽  
Ying Wei ◽  
Qifang Geng ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Ecological Risk ◽  
Sediment Cores ◽  
Principal Component ◽  
Potential Ecological Risk ◽  
Huaihe River ◽  
River Watershed ◽  
Anthropogenic Pollutants ◽  
High Concentrations ◽  
Riverine Wetland

Abstract The concentrations of Zn, Cr, Cu, As, Cd, and Pb in sediment cores collected from a representative riverine wetland located in the Huaihe River watershed, China, dramaticlly increased from the bottom to upper layer of the cores. Application of principal component analysis (PCA) and enrichment factor (EF) suggested that heavy metals might primarily have been derived from the inflow of contaminated water from an industrial park and agricultural region. Component 1 of the PCA was dominated by Zn, Cr, Cu, Cd, and Pb, while Component 2 was dominated by As. Metals’ high concentrations and EF values showed that the anthropogenic pollutants have increased sharply in recent years and reflect the continuous development of industry and agriculture in the region of the wetland, with a corresponding dramatic deterioration of the environment due to constant effluent of pollutants. Cd exerted the highest potential ecological risk of individual metals of sediment cores. Additionally, integrated RI values for all metals indicated that sediments possessed low ecological risk from the bottom to about 6 cm depth of the cores, moderate ecological risk from about 5 cm depth upwards, then considerable ecological risk from 4 cm depth to the top layer of the sediment cores, which demonstrates a continuous deterioration of environmental quality in recent years in this region.

Hydrological simulation using multi-sources precipitation estimates in the Huaihe River Basin

2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Mohammad Ilyas Abro ◽  
Dehua Zhu ◽  
Ehsan Elahi ◽  
Asghar Ali Majidano ◽  
Bhai Khan Solangi
Keyword(s):  
River Basin ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Hydrological Simulation ◽  
Precipitation Estimates ◽  
The Huaihe River

Atmospheric-hydrological modeling of severe precipitation and floods in the Huaihe River Basin, China

2006 ◽  
Vol 330 (1-2) ◽  
pp. 249-259 ◽  
Author(s):  
Charles A. Lin ◽  
Lei Wen ◽  
Guihua Lu ◽  
Zhiyong Wu ◽  
Jianyun Zhang ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
The Huaihe River

scholarly journals Improving TIGGE Precipitation Forecasts Using an SVR Ensemble Approach in the Huaihe River Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Chenkai Cai ◽  
Jianqun Wang ◽  
Zhijia Li
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Flood Control ◽  
Ensemble Methods ◽  
New Method ◽  
Support Vector ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Reservoir Regulation ◽  
The Huaihe River

Recently, the use of the numerical rainfall forecast has become a common approach to improve the lead time of streamflow forecasts for flood control and reservoir regulation. The control forecasts of five operational global prediction systems from different centers were evaluated against the observed data by a series of area-weighted verification and classification metrics during May to September 2015–2017 in six subcatchments of the Xixian Catchment in the Huaihe River Basin. According to the demand of flood control safety, four different ensemble methods were adopted to reduce the forecast errors of the datasets, especially the errors of missing alarm (MA), which may be detrimental to reservoir regulation and flood control. The results indicate that the raw forecast datasets have large missing alarm errors (MEs) and cannot be directly applied to the extension of flood forecasting lead time. Although the ensemble methods can improve the performance of rainfall forecasts, the missing alarm error is still large, leading to a huge hazard in flood control. To improve the lead time of the flood forecast, as well as avert the risk from rainfall prediction, a new ensemble method was proposed on the basis of support vector regression (SVR). Compared to the other methods, the new method has a better ability in reducing the ME of the forecasts. More specifically, with the use of the new method, the lead time of flood forecasts can be prolonged to at least 3 d without great risk in flood control, which corresponds to the aim of flood prevention and disaster reduction.

scholarly journals Spatial Pattern and Evolution of Urban System Based on Gravity Model and Whole Network Analysis in the Huaihe River Basin of China

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Fan ◽  
Shengdi Zhang ◽  
Zongyi He ◽  
Biao He ◽  
Haicong Yu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Spatial Pattern ◽  
Gravity Model ◽  
River Basin ◽  
Urban System ◽  
Huaihe River Basin ◽  
Analysis Method ◽  
Huaihe River ◽  
Research Issues ◽  
The Huaihe River

The spatial pattern and evolution of urban system have been hot research issues in the field of urban research. In this paper, the network analysis method based on the gravity model and the related measurements were used to reveal the properties of the spatial pattern and evolution of the urban system in the HRB (Huaihe River Basin) of China. The findings of this study are as follows: During the period from 2006 to 2014, the economic contact between the HRB cities has been strengthened, but the differences between cities have been expanding. In general, the HRB cities have not yet formed a close network structure, and a trend of economic integration has not been found. This paper expresses the spatial pattern and evolution of urban system in an intuitive way and helps to explain the evolution mechanism of urban system. The method was confirmed by empirical research. Because of the operational and visual expression, this method has broad application prospects in the urban system research.

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