scholarly journals Green Remediation And Physiological Responses of Plants To Metal-Contaminated Soils In Jinchang Mining Areas, Western Desert, China

2021 ◽  
Author(s):  
Changming Li ◽  
Xisheng Tai ◽  
Guohua Chang ◽  
Zidong Wan ◽  
Jingwen Fu ◽  
...  
Keyword(s):  
Contaminated Soils ◽  
Early Stage ◽  
Local Environment ◽  
Mining Area ◽  
Western Desert ◽  
Western China ◽  
Atriplex Canescens ◽  
Mining Areas ◽  
Metal Mine ◽  
Green Remediation

Abstract Jinchang mining area, as a metal mine with a long history of arid and semi-arid mining in western China, has brought a great risk of heavy metal pollution to the local environment. In this experiment, tailings, slag and native soil are mixed in equal proportions for the pot experiment settings. 9 species of sand plants unique to the arid region of the western inland were planted to screen the varieties with green remediation value. After six months of plant growth, the concentration of Zn in the plant was the highest, ranging from 103.27 to 467.63mg/kg, followed by Cu, ranging from 25.07 to 200.35mg/kg. In the early stage (2 months), POD was the main defense, and in the later stage (4\6 months), SOD and CAT were the main defense, accompanied by unknown proteins up-regulated. The results of net photosynthetic rate showed that it was inhibited by metal stress in the early stage, and it was high in the late stage to provide material basis for the accumulation and secretion of adaptable and resistant substances. Seven species of plants, such as Suaeda glauca, Bassia scoparia, Halogeton glomeratus, Kalidium foliatum, Medicago falcata, Atriplex canescens, Artemisia desertorum can be used as enrichment materials for Zn and Cu. While, Atriplex canescens has the highest metal enrichment potential, and can be used as a planting variety to enrich Cd, Ni and Pb in a broad spectrum of metals including Zn and Cu. This study lays a solid scientific foundation for green remediation of contaminated soil in arid desert area of western China.

Investigation of Contaminated Soils and Plants by Mn in Manganese Mining Area in Xiushan Autonomous County of Chongqing

2011 ◽  
Vol 414 ◽  
pp. 244-249
Author(s):  
Tao Zhu ◽  
Chang Sheng Jiang ◽  
Qing Ju Hao ◽  
Xiao Juan Huang
Keyword(s):  
Human Health ◽  
Contaminated Soils ◽  
Mining Area ◽  
Mining Areas ◽  
Mining Tailings ◽  
Plant Shoots ◽  
Edible Parts

The manganese contents of soils and dominant plants from the manganese mining areas in Xiushan autonomous county of Chongqing were researched in this paper. The results showed that the Mn pollution of soil in the Mn mining tailings were very serious with high indexes (Igeo>5), and sewage irrigated soil was also contaminated by manganese metal. The uptake of Mn by dominant plants can be classified into three types according to the Mn contents in plant shoots and roots, (1) the accumulator which absorbs a large content of Mn by the roots and transports it to the shoots, (2) the root compartment which also absorbs a large content of Mn but mainly in the roots, and (3) the excluder which absorbs a smaller content of Mn than the accumulator. The edible parts of radishes and peppers growing in the Mn mining tailings and cropland were all seriously polluted by manganese and not safe for human health.

Decision Trees in the Analysis of the Intensity of Damage to Portal Frame Buildings in Mining Areas / Drzewa Decyzyjne W Analizie Intensywności Uszkodzeń Budynków Halowych Na Terenach Górniczych

2015 ◽  
Vol 60 (3) ◽  
pp. 847-857
Author(s):  
Karol Firek ◽  
Janusz Rusek ◽  
Aleksander Wodyński
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Early Stage ◽  
Mining Area ◽  
Technical Condition ◽  
Mining Areas ◽  
Damage Indices ◽  
Frame Buildings ◽  
Portal Frame ◽  
Input Variables

Abstract The article presents a preliminary database analysis regarding the technical condition of 94 portal frame buildings located in the mining area of Legnica-Głogów Copper District (LGOM), using the methodology of decision trees. The scope of the analysis was divided into two stages. The first one included creating a decision tree by a standard CART method, and determining the importance of individual damage indices in the values of the technical wear of buildings. The second one was based on verification of the created decision tree and the importance of these indices in the technical wear of buildings by means of a simulation of individual dendritic models using the method of random forest. The obtained results confirmed the usefulness of decision trees in the early stage of data analysis. This methodology allows to build the initial model to describe the interaction between variables and to infer about the importance of individual input variables.

scholarly journals Multifunctional Activities of Gold Nanoparticles Biosynthesized Using Bacteria Isolated from Mining Areas

2021 ◽  
Vol 11 (8) ◽  
pp. 3670
Author(s):  
Chih-Yu Chen ◽  
Yung-Chu Chang ◽  
Teh-Hua Tsai ◽  
Man-Hai Liu ◽  
Ying-Chien Chung
Keyword(s):  
Gold Nanoparticles ◽  
Mining Area ◽  
Synthesis Process ◽  
Average Particle ◽  
Bifidobacterium Lactis ◽  
E Coli ◽  
Mining Areas ◽  
Skin Cells ◽  
Study Results ◽  
Antityrosinase Activity

Research on gold nanoparticles (AuNPs) has often focused on their physical, chemical, and crystalline characteristics. Commercial AuNPs have been applied in the diverse fields of biomedicine, catalysis, photovoltaics, and sensing. In this study, we explored the various activities of AuNPs to widen their applicability. This paper presents a simple and rapid synthesis process of AuNPs with bacteria isolated from a gold mining area. We also investigated the optimization of reaction parameters for AuNP synthesis. The study results revealed that among the isolated strains, Bifidobacterium lactis and Escherichia coli demonstrated the highest capabilities of AuNP synthesis. The optimal pH values for AuNP synthesis by B. lactis (BLAuNPs) and E. coli (ECAuNPs) were 5.0 for 72 h of incubation and 8.0 for 24 h of incubation. The average particle sizes of ECAuNPs and BLAuNPs were 4.2 and 5.6 nm, respectively. Furthermore, these biogenic AuNPs were found to be stable with no aggregation after 3 months of storage. BLAuNPs and ECAuNPs exhibited high levels of antimicrobial, antioxidant, photocatalytic, and antityrosinase activity. Moreover, they were noncytotoxic to skin cells even at 100% melanin inhibitory concentrations. Considering the demonstrated multifunctional activities of AuNPs, BLAuNPs and ECAuNPs have promising potential for commercialization.

scholarly journals Distribution of Contaminated Soils along Transversal and Longitudinal Gradients in Dynamic Fluvial Environment (Southern Québec, Canada)

2015 ◽  
Vol 8 ◽  
pp. ASWR.S22465 ◽  
Author(s):  
Diane Saint-Laurent ◽  
Francis Baril ◽  
Ilias Bazier ◽  
Vernhar Gervais-Beaulac ◽  
Camille Chapados
Keyword(s):  
Spatial Distribution ◽  
Contaminated Soils ◽  
Soil Samples ◽  
Copper Mining ◽  
Mining Sites ◽  
Mining Areas ◽  
Pb Contamination ◽  
Flood Zone ◽  
Longitudinal Gradients ◽  
Mine Sites

This research combines a hydrological and pedological approach to better understand the spatial distribution of contaminated soils along the Massawippi River (southern Québec, Canada). This river crosses through former mines, which were some of the largest copper mining areas in North America from 1865 to 1939. To determine the spatial distribution and concentration of the metal elements, soil samples were taken in each flood recurrence zone appearing on official flood zone maps. The maximum values obtained for Cu and Pb are 380 and 200 mg kg−1, respectively, for the soils in the frequent flood zones (FFzs), while the values for soils in the moderate flood zones (MFzs) range from 700 to 540 (Cu) and 580 to 460 mg kg−1 (Pb). Contamination extends through several kilometers of the former mining sites (Eustis and Capleton), and concentration of metals in alluvial soils is slightly higher near the mine sites.

scholarly journals Heavy metal composition of maize and tomato grown on contaminated soils

2018 ◽  
Vol 3 (1) ◽  
pp. 414-426
Author(s):  
A.O. Adekiya ◽  
A.P. Oloruntoba ◽  
S.O. Ojeniyi ◽  
B.S. Ewulo
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Contaminated Soils ◽  
Heavy Metal Contamination ◽  
Mining Area ◽  
Atomic Absorption Spectrophotometry ◽  
Toxic Heavy Metals ◽  
Mining Site ◽  
Solanum Lycopersicum L ◽  
Sources Of Pollution

Abstract The study investigated the level of heavy metal contamination in plants {maize (Zea mays) and tomato (Solanum lycopersicum L.)} from thirty soil samples of three locations (Epe, Igun and Ijana) in the Ilesha gold mining area, Osun State, Nigeria. Total concentrations of As, Cd, Co, Cr, Cu, Ni, Pb and Zn were determined using atomic absorption spectrophotometry. Spatial variations were observed for all metals across the locations which was adduced to pH and the clay contents of the soils of each location. The results showed that heavy metals are more concentrated in the areas that are closer to the mining site and the concentrations in soil and plants (maize and tomato) decreased with increasing perpendicular distance from the mining site, indicating that the gold mine was the main sources of pollution. The mean concentrations of heavy metals in plants (tomato and maize) samples were considered to be contaminated as As, Cd and Pb respectively ranged from 0.6 - 2.04 mg kg-1, 0.8 - 5.2 mg kg-1, 0.8 - 3.04 mg kg-1 for tomato and respectively 0.60 - 2.00 mg kg-1, 1.50 - 4.60 mg kg-1 and 0.90 - 2.50 mg kg-1 for maize. These levels exceeded the maximum permissible limits set by FAO/WHO for vegetables. In conclusion, monitoring of crops for toxic heavy metals is essential for food safety in Nigeria.

scholarly journals Geochemical Characteristics and Prospecting Prediction of Sediment in Songwang Area, Bobai County, Guangxi

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Wai Yi
Keyword(s):  
Water System ◽  
Mining Area ◽  
Geochemical Characteristics ◽  
Good Prospect ◽  
Mining Areas ◽  
Geological Characteristics ◽  
Geochemical Anomalies ◽  
Magmatic Rocks ◽  
Prospecting Prediction

The characteristics of strata, structures, magmatic rocks, lithology and mineralization in Dongtao mining area (Songwang area) of Bobai County, Guangxi were summarized. According to the geochemical anomalies of sediment in water system, and the main anomalies are explained and evaluated, combined with the geological characteristics of Dongtao and other mining areas, the prospecting prediction of favorable areas is carried out, and it is considered that there is a good prospect of prospecting.

Variabilities of Surface Water Quality of Degraded Post-mining Areas in Bytom

2021 ◽  
Vol 23 ◽  
pp. 318-331
Author(s):  
Aleksandra Czajkowska ◽  
Łukasz Gawor
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Ph Value ◽  
Surface Water Quality ◽  
Water Hardness ◽  
Total Content ◽  
Mining Area ◽  
Physicochemical Analysis ◽  
High Concentration ◽  
Mining Areas

In the paper there is presented an evaluation of variability of surface water quality (reservoirs and watercourses), on the area of degraded post-mining area in Bytom. The physicochemical analysis of water and compared with archival data obtained in 2009 and 2014. There were done analysis of following parameters: reaction, total content of substances dissolved in water, water hardness and the content of: Cl-, SO42-, HCO3-, Ca2+, Mg2+, K+, Na+, NH4+, NO3-, NO2-, PO4- ions as well as Fe and Mn. The examined surface waters were characterised by high content of solutes. Anions were dominated by chlorides, the sodium proved to be the dominating cation, the examined water samples were characterised by high concentration of sulphates. In all analyzed reservoirs, permissible concentrations of chlorides and sulphates were exceeded. In all sample points there was observed a decrease of pH value in long term period, the concentration of chlorides lowered, however concentrations of sulphates increased in the majority of sampling points.

scholarly journals Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1938 ◽  
Author(s):  
Asmau M. Ahmed ◽  
Olga Duran ◽  
Yahya Zweiri ◽  
Mike Smith
Keyword(s):  
Deep Learning ◽  
Contaminated Soils ◽  
Early Stage ◽  
Imaging Techniques ◽  
Rapid Identification ◽  
Hyperspectral Data ◽  
Learning Approach ◽  
Backpropagation Algorithm ◽  
Average Mean Square Error ◽  
Deep Learning Neural Network

Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~ 2 . 2 × 10 - 4 . The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

scholarly journals Dust Dispersion and Its Effect on Vegetation Spectra at Canopy and Pixel Scales in an Open-Pit Mining Area

2020 ◽  
Vol 12 (22) ◽  
pp. 3759
Author(s):  
Baodong Ma ◽  
Xuexin Li ◽  
Ziwei Jiang ◽  
Ruiliang Pu ◽  
Aiman Liang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Vegetation Canopy ◽  
Dust Pollution ◽  
Mining Areas ◽  
Dust Effect ◽  
Dust Dispersion ◽  
Pixel Scale

Dust pollution is severe in some mining areas in China due to rapid industrial development. Dust deposited on the vegetation canopy may change its spectra. However, a relationship between canopy spectra and dust amount has not been quantitatively studied, and a pixel-scale condition for remote sensing application has not been considered yet. In this study, the dust dispersion characteristics in an iron mining area were investigated using the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) regulatory model (AERMOD). Further, based on the three-dimensional discrete anisotropic radiative transfer (DART) model, the spectral characteristics of vegetation canopy under the dusty condition were simulated, and the influence of dustfall on vegetation canopy spectra was studied. Finally, the dust effect on vegetation spectra at the canopy scale was extended to a pixel scale, and the response of dust effect on vegetation spectra at the pixel scale was determined under different fractional vegetation covers (FVCs). The experimental results show that the dust pollution along a haul road was more severe and extensive than that in a stope. Taking dust dispersion along the road as an example, the variation of vegetation canopy spectra increased with the height of dust deposited on the vegetation canopy. At the pixel scale, a lower vegetation FVC would weaken the influence of dust on the spectra. The results derived from simulation spectral data were tested using satellite remote sensing images. The tested result indicates that the influence of dust retention on the pixel spectra with different FVCs was consistent with that created with the simulated data. The finding could be beneficial for those making decisions on monitoring vegetation under dusty conditions and reducing dust pollution in mining areas using remote sensing technology.

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