Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principle components analysis and geo statistical analysis

2018 ◽  
Vol 159 ◽  
pp. 354-362 ◽  
Author(s):  
Xiaowen Zhang ◽  
Shuai Wei ◽  
Qianqian Sun ◽  
Syed Abdul Wadood ◽  
Boli Guo
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Statistical Analysis ◽  
Matrix Factorization ◽  
Agricultural Soil ◽  
Positive Matrix ◽  
Principle Components Analysis ◽  
Industrial Estate ◽  
Factorization Model ◽  
Components Analysis

scholarly journals Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 614
Author(s):  
Muhammad Faisal ◽  
Zening Wu ◽  
Huiliang Wang ◽  
Zafar Hussain ◽  
Chenyang Shen
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Contamination Factor ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Prevention Measures ◽  
Positive Matrix ◽  
The City

Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (Igeo), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PIN). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. Igeo showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PIN was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PIN indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.

Spatial Distribution of Heavy Metals in Soybean Plantation Soils in Central Sanjiang Plain

2011 ◽  
Vol 71-78 ◽  
pp. 3174-3178
Author(s):  
Jia Tian Niu ◽  
Qin Kui Guo ◽  
Mei Guo ◽  
Yi Li ◽  
Qun Hui Wang
Keyword(s):  
Heavy Metals ◽  
Information System ◽  
Spatial Distribution ◽  
Statistical Analysis ◽  
Spatial Correlation ◽  
Industrial Pollution ◽  
Sanjiang Plain ◽  
Parent Material ◽  
External Factors ◽  
Internal Factors

Monitoring and analysis of the content of five heavy metals (i.e., Cd, Hg, As, Pb and Cr) in soybean plantation soils in central Sanjiang plain were conducted using the geographic information system (GIS). Statistical analysis indicated, strong intensities of spatial correlation among As, Cr, and Pb, whereas moderate intensities of spatial correlation were observed between Cd and Hg. Therefore, the content and the spatial distribution of the heavy metals are primarily affected by the parent material, topography, soil properties, and other internal factors. However, the effect of external factors, such as industrial pollution, farming methods, as well as the application of pesticide and fertilizer, should also be investigated.

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