Aman Fang

Dr. Aman Fang

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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AFAman Fang
Aman Fang
JDJihong Dong
Jihong Dong
Zhiguo CaoZhiguo Cao
Zhiguo Cao
FZFeng Zhang
Feng Zhang
YLYongfeng Li
Yongfeng Li
Vegetation in eastern Inner Mongolia grasslands plays an important role in preventing desertification, but mineral exploration has negative effects on the vegetation of these regions. In this study, the changing trend types of vegetation in eastern Inner Mongolia were analyzed using the normalized difference vegetation index (NDVI) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI 3g dataset from 1982 to 2015. Meanwhile, changing trend and influencing factors of 25 large-scale mining areas before and after mining were explored with the methods of trend line, residual calculation, and correlation analysis. The vegetation coverage towards increasing in eastern Inner Mongolia decreased in the order of Tongliao > Hinggan League > Chifeng > Hulunbuir > Xilingol over the past 34 years. Vegetation showed a decreasing tendency in 40% mining areas, but an increasing tendency in 60% mining areas after mining. Vegetation change in Shengli No. 1 had a significant correlation with precipitation and human activities after mining. Except Shengli No. 1, an obvious correlation was found between vegetation change and precipitation in 45.83% mining areas after mining. Human activities had significant positive effects on vegetation growth in 25% mining areas. Significant negative effects of human activities were found in 8.34% mining areas, causing the vegetation degradation. However, there were 20.83% mining areas with vegetation changes not affected by precipitation and human activities.

23 April 2021 in Natural Resources Research
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Journal Article
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AFAman Fang
Aman Fang
MBMenghe Bao
Menghe Bao
WCWeiqiang Chen
Weiqiang Chen
JDJihong Dong
Jihong Dong

27 March 2019 in Sustainability
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Journal Article
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AFAman Fang
Aman Fang
JDJihong Dong
Jihong Dong
YAYingli An
Yingli An
Xuzhou, as a mining city in China, has been experiencing 130 years of coal mining and processing. To explore the spatial distribution characteristics and pollution status of soil heavy metals (Cr, Cd, As, Hg, Zn, and Pb) under different land-use types, a total of 2697 topsoil samples were collected in all of the areas (except for water) of Xuzhou in 2016. Overall, the mean concentrations of Cr (70.266 mg/kg), Cd (0.141 mg/kg), As (10.375 mg/kg), Hg (0.036 mg/kg), Zn (64.788 mg/kg), and Pb (24.84 mg/kg) in Xuzhou soils were lower than the environmental quality standard for soils (GB15618-1995). However, the mean concentrations of Cr, Hg, and Pb exceeded their corresponding background values, with the mean concentration of Hg being almost three times its background value. For different land-use types, the highest mean concentration of Cr was concentrated in grassland soils. The mean concentrations of Cd, As, Zn, and Pb in mining area soils were higher than those in the other soils. The mean concentration of Hg was the highest in the built-up area soils. Based on the potential ecological risk assessment, the forestland, garden land, grassland, and others were at low and moderate risk levels, the farmland and mining area were at low, moderate, and high risk levels, and the built-up area was at various risk levels in Xuzhou. There was a significant positive correlation between Cr, Pb, and Hg concentrations and the corresponding organic carbon contents in the farmland, built-up area, garden land, forestland, and other soils ( p < 0.01 ). A high degree of correlation was found between Cr and Hg concentrations, as well as organic carbon contents in grassland soils, with values of p < 0.05 and p < 0.01 , respectively. An obvious correlation could be seen between Hg concentrations and organic carbon contents in mining area soils ( p < 0.01 ).

8 July 2019 in Polish Journal of Environmental Studies
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Journal Article
Closed access
JDJihong Dong
Jihong Dong
LMLingran Meng
Lingran Meng
ZBZhengfu Bian
Zhengfu Bian
AFAman Fang
Aman Fang
The exploitation and utilization of mineral resources bring changes of the regional material cycle and energy flow, resulting in degradation and negative evolution of the local ecosystem. Many countries are committed to researching ecological environment issues and have proposed a series of techniques and methods in recent years, but the comprehensive study of mining area ecosystems and restoration modes is not enough. To further clarify the mining area ecosystem and global restoration, this research analyzed the characteristics and functions of the ecosystem in mining areas, explored its evolution principles, types and processes, and put forward four typical ecological restoration modes. The mining area ecosystem features open, artificial and complex characteristics and special energy and material flows. Its functions mainly refer to the input and output of materials, energy, information and stream of people in the internal and external systems. The ecosystem evolution of mining areas consists of negative evolution and positive evolution in arid and semi-arid areas and humid and semi-humid areas. Overall, four typical restoration modes of mining area ecosystems are featured, including land engineering reclamation, soil improvement and vegetation restoration, secondary wetland construction and ecological self-restoration modes.

AFAman Fang
Aman Fang
JDJihong Dong
Jihong Dong
RZRu Zhang
Ru Zhang
Heavy metals in the soil of mining areas have become a primary source of pollution, which could cause deleterious health effects in people exposed through soil-plant systems via multi-pathways. A long-term field experiment under natural conditions was carried out to explore the distribution characteristic and migration law of heavy metals in a soil-wheat system of a mining area in Xuzhou. According to the second level standard of environmental quality standards for soils of China (GB 15618-1995), 30.8 g of CrCl3·6H2O, 8.3 g of Pb(CH3COO)2·3H2O, and 16.5 g of ZnSO4·7H2O were added into the soil of three experimental sites, respectively. The other experimental site with no additional compounds was used as the control site. The Cr, Pb, and Zn concentrations in the soil-wheat system were counted and their corresponding migration models were constructed. From 2014 to 2017, the mean concentrations of Cr (49.09 mg·kg−1), Pb (20.08 mg·kg−1), and Zn (39.11 mg·kg−1) in the soil of the addition sites were higher than that of the control site. The mean concentrations of Cr, Pb, and Zn in wheat of the addition sites were greater than that of the control site with the values of 3.29, 0.06, and 29 mg·kg−1. In comparison, the Cr, Pb, and Zn concentrations in the soil of all experimental sites were lower than the second level standard of environmental quality standards for soils of China (GB 15618-1995), whereas the Cr concentration exceeded its corresponding soil background value of Xuzhou in 2017. The Pb concentration in soil of the addition site was greater than its corresponding background value from 2014 to 2016. The Pb and Zn concentrations in wheat of all experimental sites were lower than the national hygienic standard for grains of China (GB2715-2005) and the national guidelines for cereals of China (NY 861-2004), but the Cr concentration significantly exceeded the national guidelines for cereals of China (NY 861-2004). By constructing the Identical-Discrepant-Contrary (IDC) gray connection models, the result showed that there was a non-linear relationship of Cr, Pb, and Zn concentrations in the soil-wheat system, and the absolute values of most correlation coefficients r were lower than 0.5 and the values of greyness f G ( r ) were more than 0.5. The curvilinear regression models could not reflect the relationship of Cr, Pb, and Zn concentrations in the soil-wheat system with the regression coefficient r 2 values far less than 1. Due to the values of regression coefficient r 2 being close to 1, this study suggested that the allocation estimation models could be used for simulating the Cr, Pb, and Zn migration in the soil-wheat system of a mining area in Xuzhou.