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

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.

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

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

scholarly journals Temporal and spatial variability of heavy metals in Marudu Bay, Malaysia

2016 ◽  
Vol 45 (3) ◽  
Author(s):  
Tan Kar Soon ◽  
Delta Jenetty Denil ◽  
Julian Ransangan
Keyword(s):  
Heavy Metals ◽  
Surface Sediment ◽  
Environmental Parameters ◽  
Organic Content ◽  
Terrestrial Organic Matter ◽  
Total N ◽  
Anthropogenic Inputs ◽  
Baseline Concentration ◽  
Temporal And Spatial Variability ◽  
Geochemical Normalization

AbstractThe current study was conducted to estimate the baseline concentration of heavy metals in the surface sediment of Marudu Bay. Environmental parameters were measured at the seafloor and samples of the surface sediment were collected at monthly intervals for the period of 12 months. The organic content, total N, total P and concentration of 16 trace metals in the surface sediment were analyzed. The baseline concentration of metals was estimated by geochemical normalization. Anthropogenic inputs of metals were then estimated by calculating the enrichment factor for each element. The result demonstrated that the C/N ratio of sediment at Marudu Bay varies from 15 to 342, which indicates the dominance of terrestrial organic matter. The baseline concentration of V, Fe, Mn, Zn, Ti, Rb and Sr were 26.74 mg kg

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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