Spatial Prediction of Heavy Metal Pollution for Soils in Peri-Urban Beijing, China Based on Fuzzy Set Theory

Pedosphere ◽  
2006 ◽  
Vol 16 (5) ◽  
pp. 545-554 ◽  
Author(s):  
Man-Zhi TAN ◽  
Fang-Ming XU ◽  
Jie CHEN ◽  
Xue-Lei ZHANG ◽  
Jing-Zhong CHEN
Keyword(s):  
Heavy Metal ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Spatial Prediction ◽  
Beijing China

Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China

Chemosphere ◽  
2005 ◽  
Vol 60 (4) ◽  
pp. 542-551 ◽  
Author(s):  
Tong-Bin Chen ◽  
Yuan-Ming Zheng ◽  
Mei Lei ◽  
Ze-Chun Huang ◽  
Hong-Tao Wu ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Urban Parks ◽  
Surface Soils ◽  
Beijing China

Application of Rough Set Theory in the Evaluation of Heavy Metal Pollution

2014 ◽  
Vol 1049-1050 ◽  
pp. 1403-1406
Author(s):  
Hong Feng Ma ◽  
Cong Hua Lan ◽  
Xu Hua Miao
Keyword(s):  
Heavy Metal ◽  
Set Theory ◽  
Rough Set ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Rough Set Theory ◽  
Objective Evaluation ◽  
Evaluation Index ◽  
Relational Data Model ◽  
Index Weight

Lead, Mercury and Cadmium etc as the main evaluation index of heavy metal pollution established relational data model. The rough set theory is introduced, use the existing algorithm (Combinatorial Completer) to fill the missing value. After data preprocessing, use the DBMAS algorithm proposed in the paper to calculate the important degree of heavy metal pollution factors, in order to provides a more objective evaluation index weight for evaluation of heavy metal pollution.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

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