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.

Spatial distribution of heavy metals in the West Dongting Lake floodplain, China

2020 ◽  
Vol 22 (5) ◽  
pp. 1256-1265 ◽  
Author(s):  
Dong Peng ◽  
Ziyu Liu ◽  
Xinyue Su ◽  
Yaqian Xiao ◽  
Yuechen Wang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Dongting Lake ◽  
Migration Route ◽  
The West ◽  
Rare And Endangered ◽  
And Migration

The protection of Dongting Lake is important because it is an overwintering and migration route for many rare and endangered birds of East Asia and Australasia, but an assessment of heavy metal contamination in West Dongting Lake is lacking.

Feasibility of using environmental covariates and machine learning to predict the spatial variability of selected heavy metals in soils

2020 ◽  
Author(s):  
Mojtaba Zeraatpisheh ◽  
Rouhollah Mirzaei ◽  
Younes Garosi ◽  
Ming Xu ◽  
Gerard B.M. Heuvelink ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Random Forest ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Prediction Performance ◽  
Anthropogenic Sources ◽  
Environmental Covariates

<p>Heavy metal contamination in soil is a major environmental issue intensified by rapid industrial and population growth. Understanding the spatial distribution of soil contamination by heavy metals in the ecosystem is a necessary precondition to monitor soil health and to assess the ecological risks. The main sources of heavy metals in soil are natural and anthropogenic sources. Natural sources are typically released of heavy metals from rock by weathering and atmospheric precipitation. Anthropogenic sources are related to industrialization, rapid urbanization, agricultural practices, and military activities. We analyzed a total of 358 topsoil samples (0–30 cm) collected in Golestan province in the northeast of Iran based on a regular square grid networks with 1,700 squares each sized 2.5 km²(random sampling within the grid). From these samples, we determined the spatial distribution of Cd, Cu, Ni, Zn, and Pb using random forest (RF). A multi-spectral image (Landsat 8), and environmental derivatives calculated from terrain attributes, climatic parameters, parent material, land use maps, distances to mine sectors, main roads, industrial sites, and rivers were used as covariates to predict the spatial distribution of concentrations of heavy metals. The multi-collinearity of the predictors was examined by the variance inflation factor (VIF), and a feature selection process (genetic algorithm) was applied to avoid noise and optimize the selected input variables for the final model. The predictive accuracy of RF model was assessed by the mean prediction error (ME), root mean squared error (RMSE), and coefficient of determination (R<sup>2</sup>) using 5-fold cross-validation technique. The results showed that the concentration levels (mg kg<sup>-1</sup>) of Cd, Cu, Pb, Ni, and Zn varied from 0.02 to 2.75, 9.70 to 93.70, 6.80 to 114.20, 9.50 to 93.20, and 25.10 to 417.4, respectively. The best prediction performance was for Ni (RMSE=9.9 mg kg<sup>-1 </sup>and R<sup>2</sup>=56.6%), and the lowest prediction performance for Cd (RMSE=0.4 mg kg<sup>-1 </sup>and R<sup>2</sup>=28.0%). Environmental covariates that control soil moisture and water flow along with climatic factors were the most important variables to define the spatial distribution of soil heavy metals. We conclude that the RF model using easily accessible environmental covariates is a promising, cost-effective and fast approach to monitor the spatial distribution of heavy metal contamination in soils.</p><p><strong>Keywords:</strong> Heavy metals; digital soil mapping; machine learning; random forest; spatial variation; soil pollution.</p>

A brief Survey of Assessment models to Predict stress Level of Heavy Metals in Soils

2020 ◽  
Vol 13 ◽  
Author(s):  
Sangeetha Annam ◽  
Anshu Singla
Keyword(s):  
Economic Growth ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Ecological Risk ◽  
Stress Level ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Human Life ◽  
High Stress ◽  
Heavy Metal Stress

Abstract: Soil is a major and important natural resource, which not only supports human life but also furnish commodities for ecological and economic growth. Ecological risk has posed a serious threat to the ecosystem by the degradation of soil. The high-stress level of heavy metals like chromium, copper, cadmium, etc. produce ecological risks which include: decrease in the fertility of the soil; reduction in crop yield & degradation of metabolism of living beings, and hence ecological health. The ecological risk associated, demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological and hence economic growth. This study reviews the efficient assessment models to predict soil heavy metal contamination.

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.

scholarly journals TINGKAT BIOKONSENTRASI LOGAM BERAT DAN GAMBARAN HISTOPATOLOGI IKAN MUJAIR (Oreochromis Mossambicus) YANG HIDUP DI PERAIRAN TUKAD BADUNG KOTA DENPASAR

2015 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Made Rahayu Kusumadewi ◽  
I Wayan Budiarsa Suyasa ◽  
I Ketut Berata
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Hexavalent Chromium ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Quality Standards ◽  
Oreochromis Mossambicus ◽  
Metal Exposure ◽  
Histopathological Changes ◽  
Food Ingredient

Tukad Badung River is one of the potential contamination of heavy metal sare very highin the city of Denpasar. Tilapia (Oreochromis mossambicus) isa commonspecies of fish found in the river and became the object of fishing by the public. The fish is usually consume das a food ingredient forever yangler. Fish can be used as bio-indicators of chemical contamination in the aquatic environment. Determination of heavy metal bioconcentration and analysis of liver histopathology gills organs and muscles is performed to determine the content of heavy metals Pb, Cd, and Cr+6, and the influence of heavy metal exposure to changes in organ histopathology Tilapia that live in Tukad Badung. In this observational study examined the levels of heavy metal contamination include Pb, Cd and Cr+6 in Tilapia meat with AAS method (Atomic Absorption Spectrofotometric), and observe the histopathological changes in organ preparations gills, liver, and muscle were stained with HE staining (hematoxylin eosin). Low Pb content of the fish that live in Tukad Badung 0.8385 mg/kg and high of 20.2600 mg/kg. The content of heavy metals Pb is above the quality standards specified in ISO 7378 : 2009 in the amount of 0.3 mg / kg. The content of Cr+6 low of 1.1402 mg / kg and the highest Cr+6 is 6.2214 mg / kg. The content of Cr+6 is above the quality standards established in the FAO Fish Circular 764 is equal to 1.0 mg / kg. In fish with Pb bioconcentration of 0.8385 mg / kg and Cr+6 of 1.1402 mg / kg was found that histopathological changes gill hyperplasia and fusion, the liver was found degeneration, necrosis, and fibrosis, and in muscle atrophy found. Histopathologicalchangessuch asedema and necrosis ofthe liveris foundin fishwith Pb bioconcentration of 4.5225mg/kg and Cr+6 amounted to2.5163mg/kg. Bio concentration of heavy metal contamination of lead (Pb) and hexavalent chromium (Cr+6) on Tilapia ( Oreochromis mossambicus ) who lives in Tukad Badung river waters exceed the applicable standard. Histopathological changes occur in organs gills, liver, and muscle as a result of exposure to heavy metals lead and hexavalent chromium. Advised the people not to eat Tilapia that live in Tukad Badung

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