Assessment of heavy metal pollution in vegetables and relationships with soil heavy metal distribution in Zhejiang province, China

2015 ◽  
Vol 187 (6) ◽  
Author(s):  
Xuezhu Ye ◽  
Wendan Xiao ◽  
Yongzhi Zhang ◽  
Shouping Zhao ◽  
Gangjun Wang ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Zhejiang Province ◽  
Metal Distribution ◽  
Soil Heavy Metal ◽  
Heavy Metal Distribution

Heavy Metal Pollution in City Topsoil Fuzzy Clustering Model

2012 ◽  
Vol 459 ◽  
pp. 199-202
Author(s):  
Xing Wang ◽  
Xue Jiao Li ◽  
Pu Ke ◽  
She Sheng Zhang
Keyword(s):  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Pollution Source ◽  
Metal Distribution ◽  
Dimension Theory ◽  
Surrounding Area ◽  
High Concentration ◽  
Heavy Metal Distribution ◽  
Clustering Model

It is a hot research project to find the heavy metal distribution in city topsoil and the level of pollution in city function region. Based on two dimension theory of Weiersreas, the eight heavy metal distribution is obtained, and the number of function region is calculated. Considering longitude and latitude of the polluted region, the high concentration distribution of heavy metals in area and the surrounding area is get. The pollution domain is found by using reverse Markov chain. The heavy metal pollution source is calculated from Gaussian diffusion model.

Soil Heavy Metal Pollution along Subin River in Kumasi, Ghana; Using X-Ray Fluorescence (XRF) Analysis

2010 ◽  
Author(s):  
K. Kodom ◽  
J. Wiafe-Akenten ◽  
D. Boamah ◽  
Melissa Denecke ◽  
Clive T. Walker
Keyword(s):  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
X Ray ◽  
Soil Heavy Metal ◽  
Xrf Analysis

Risk assessment and source analysis of soil heavy metal pollution from lower reaches of Yellow River irrigation in China

2018 ◽  
Vol 633 ◽  
pp. 1136-1147 ◽  
Author(s):  
Pengyan Zhang ◽  
Chengzhe Qin ◽  
Xin Hong ◽  
Guohua Kang ◽  
Mingzhou Qin ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Yellow River ◽  
Source Analysis ◽  
Soil Heavy Metal

Soil Heavy Metal Pollution Analysis of City Surface Based on Neural Network

2013 ◽  
Vol 340 ◽  
pp. 947-951
Author(s):  
Jia Pei Pei ◽  
Zhang Tai ◽  
Shi Xiao Shuang ◽  
Xia Bin Yu ◽  
Liu Ran
Keyword(s):  
Neural Network ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Degree Distribution ◽  
Heavy Metal Pollution ◽  
Urban Soil ◽  
Soil Heavy Metal ◽  
Network Building ◽  
Synthetic Index ◽  
Analysis System

Identified the urban soil has heavy metal pollution degree and the cause of the contamination of the overall analysis system. Through the mat lab software to realize pollution degree distribution visualization, use numerous evaluation pollution degree synthetic index methods, and then the reference neural network building the knowledge about the cause of the contamination analysis to determine the mechanism of the main causes of the pollution.

scholarly journals Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

2014 ◽  
Vol 14 (6) ◽  
pp. 1599-1610 ◽  
Author(s):  
X. Jiang ◽  
W. X. Lu ◽  
H. Q. Zhao ◽  
Q. C. Yang ◽  
Z. P. Yang
Keyword(s):  
Heavy Metal ◽  
Ecological Risk ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Pearson Correlation ◽  
Risk Index ◽  
Potential Ecological Risk ◽  
Coal Gangue ◽  
Index Method ◽  
Soil Heavy Metal

Abstract. The aim of the present study is to evaluate the potential ecological risk and trend of soil heavy-metal pollution around a coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy-metal pollution. The potential ecological risk in the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

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