scholarly journals Immobilization of mercury in contaminated soils through the use of new carbon foam amendments

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
I. Janeiro-Tato ◽  
M. A. Lopez-Anton ◽  
D. Baragaño ◽  
C. Antuña-Nieto ◽  
E. Rodríguez ◽  
...  
Keyword(s):  
Contaminated Soils ◽  
Large Scale ◽  
Toxic Elements ◽  
Iron Nanoparticles ◽  
Carbon Foam ◽  
Additional Advantage ◽  
Mining Areas ◽  
Novel Approach ◽  
Carbon Foams ◽  
Environmental Compartments

Abstract Background Mercury (Hg) is recognized as one of the 10 most toxic elements in nature and is much more persistent in soils than in other environmental compartments. However, an effective, environmentally friendly, economical, and large-scale applicable technology for the remediation of soils contaminated by Hg has not yet been established. This study evaluates the feasibility of a new carbon foam-based product for the remediation of three soils contaminated with Hg, and infers the mobilization or immobilization mechanism through a detailed study of Hg speciation. Results Soil treatment with carbon foams, one of them impregnated with goethite, reduced Hg availability by 75–100%. The proportion of mercury associated to humic acids (Hg–HA) determined the mobility and the availability of Hg when soils were treated with carbon foams. The drop of pH promotes changes in the structure of HA, a consequence of which is that Hg–HA becomes part of the unavailable fraction of the soil along with HgS. The carbon foam impregnated with goethite did not mobilize Fe as occurred with zero valence iron nanoparticles. The presence of acidic groups on the surface of the foam (carboxyl, quinone and phenolic groups) can strongly improve the binding of metal cations, enhancing Fe immobilization. Conclusions A novel carbon foam-based amendment was efficient in immobilizing Hg in all the soils studied. The carbon foam impregnated with goethite, in addition to not mobilizing Fe, had the additional advantage of its low effect on the electrical conductivity of the soil. This novel approach could be considered as a potential amendment for other industrial and/or abandoned mining areas contaminated with Hg and/or other metal(loid)s.

scholarly journals Immobilization of mercury in contaminated soils through the use of new carbon foam amendments

2021 ◽  
Author(s):  
Iria Janeiro-Tato ◽  
María Antonia López Antón ◽  
Diego Baragaño ◽  
Cristina Antuña-Nieto ◽  
Elena Rodriguez ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Humic Acid ◽  
Contaminated Soils ◽  
Large Scale ◽  
Toxic Elements ◽  
Carbon Foam ◽  
Additional Advantage ◽  
Novel Approach ◽  
Carbon Foams ◽  
Environmental Compartments

Abstract Background: Mercury (Hg) is recognized as one of the 10 most toxic elements and is much more persistent in soils than in other environmental compartments. However, an effective, environmentally friendly, economical, and applicable at large-scale technology for the remediation of soils contaminated by Hg has not yet been established. This study evaluates the feasibility of a new carbon foam-based product for the remediation of three soils contaminated with Hg, and infers the mobilization or immobilization mechanism through a detailed study of Hg speciation. Results: Soil treatment with the carbon foams, one of them impregnated with goethite, reduced Hg availability by between 75 and 100%. Mercury associated to humic acid (Hg-HA) determined the proportion of mobility and availability of Hg when soils were treated with carbon foams. When the pH dropped, the structure of HA changed causing the Hg-HA to become part of the unavailable fraction of the soil along with HgS. The carbon foam impregnated with goethite did not mobilize Fe as occurred with ZVI nanoparticles. The presence of acid groups on the surface of the foam (carboxyl, quinone and phenolic groups) can bind metal cations strongly improving Fe immobilization. Conclusions: A novel carbon foam-based amendment was efficient in immobilizing Hg in all the soils studied. The carbon foam impregnated with goethite, in addition to not mobilizing Fe, had the additional advantage of its lesser effect on the electrical conductivity of the soil. This novel approach could be considered as a potential amendment for other sites contaminated with Hg and/or other metal(loid)s.

scholarly journals Integration of TanDEM-X and SRTM DEMs and Spectral Imagery to Improve the Large-Scale Detection of Opencast Mining Areas

2020 ◽  
Vol 12 (9) ◽  
pp. 1451
Author(s):  
Qianhan Wu ◽  
Chunqiao Song ◽  
Kai Liu ◽  
Linghong Ke
Keyword(s):  
Large Scale ◽  
Influence Factor ◽  
Mining Area ◽  
Landsat 8 ◽  
Opencast Mining ◽  
Important Indicator ◽  
Mining Areas ◽  
Novel Approach ◽  
The Impact ◽  
Earth’S System

Land use and land cover (LULC) is a key variable of the Earth’s system and has become an important indicator to evaluate the impact of human activities on the Earth’s ecosystems. With the increasing demand of mine resources, widespread opencast mining has led to significant changes in LULC and caused substantial damage to the environment. An efficient approach of detecting mining activities at large scales is of critical importance in mitigating their potential impacts on downstream settlements and in assessing LULC characteristics. In this study, we present a novel approach for enabling large-scale automatic detection of opencast mining areas by integrating multitemporal digital elevation models (DEMs, including the SRTM DEM and the recently released TanDEM-X DEM) and multispectral imagery in object-based image analysis and random forest (RF) algorithms. A sequence of data preparation, image segmentation, threshold analysis, calculation of metrics, and influence factor regulation was developed and tested on the Landsat 8 sample dataset in Inner Mongolia in China, which is a mineral-rich area. Aside from spectral metrics, such as brightness and reflectance value, introduced topographical features enhanced the modeling and classification significantly, and the overall performance is greatly influenced by feature selection (the out-of-bag error rate in the RF algorithm is 7.54% for the integrated DEM method in comparison with 12.70% for the only-optical images method). The integrated use of spectral imagery and multitemporal DEMs reveals that the identified mining area is about 1100 km2 in the study area and period, and the topographic changes of opencast mining in terms of elevation difference is between −258 and 162 m. The results show that the method can map the locations and extents of mining areas automatically from spectral and DEM data and can potentially be applied to larger areas.

Managing the impacts of mining on Ghana’s water resources from a legal perspective

2018 ◽  
Vol 1 (3) ◽  
pp. 156-165 ◽  
Author(s):  
Nasirudeen Abdul Fatawu
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Environmental Protection Agency ◽  
Large Scale ◽  
Small Scale ◽  
Mining Activities ◽  
Protection Agency ◽  
Environmental Laws ◽  
Mining Areas ◽  
Legal Perspective

Recent floods in Ghana are largely blamed on mining activities. Not only are lives lost through these floods, farms andproperties are destroyed as a result. Water resources are diverted, polluted and impounded upon by both large-scale minersand small-scale miners. Although these activities are largely blamed on behavioural attitudes that need to be changed, thereare legal dimensions that should be addressed as well. Coincidentally, a great proportion of the water resources of Ghana arewithin these mining areas thus the continual pollution of these surface water sources is a serious threat to the environmentand the development of the country as a whole. The environmental laws need to be oriented properly with adequate sanctionsto tackle the impacts mining has on water resources. The Environmental Impact Assessment (EIA) procedure needs to bestreamlined and undertaken by the Environmental Protection Agency (EPA) and not the company itself.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Phyto-fabrication of Iron Nanoparticles: Characterization and Antibacterial Capacity

2020 ◽  
Vol 16 ◽  
Author(s):  
Asma S. Algebaly ◽  
Afrah E. Mohammed ◽  
Mudawi M. Elobeid
Keyword(s):  
Electron Microscopy ◽  
Plant Extracts ◽  
Large Scale ◽  
Iron Nanoparticles ◽  
Ethanolic Extract ◽  
Green Technology ◽  
Resistant Bacteria ◽  
Scale Production ◽  
Bacterial Strains ◽  
Plant Sources

Introduction: Fabrication of iron nanoparticles (FeNPs) has recently gained a great concern for their varied applications in remediation technologies of the environment. Objective: The current study aimed to fabricate iron nanoparticles by green technology approach using different plant sources, Azadirachta indica leaf and Calligonum comosum root following two extraction methods. Methods: Currently, a mixture of FeCl2 and FeCl3 was used to react with the plant extracts which are considered as reducing and stabilizing agents for the generation of FeNPs in one step. Different techniques were used for FeNPs identification. Results: Immediately after mixing of the two reaction components, the color changed to dark brown as an indication of safe conversion of Fe ions to FeNPs, that later confirmed by zeta sizer, transmission electron microscopy (TEM) and scanning electron microscopy (SEM). FeNPs fabricated by C. comosum showed smaller size when compared by those fabricated by A. indica. Using both plant sources, FeNPs fabricated by the aqueous extract had smaller size in relation to those fabricated by ethanolic extract. Furthermore, antibacterial ability against two bacterial strains was approved. Conclusion: The current results indicated that, at room temperature plant extracts fabricated Fe ion to Fe nanoparticles, suggesting its probable usage for large scale production as well as its suitability against bacteria. It could also be recommended for antibiotic resistant bacteria.

scholarly journals Zero-Valent Iron Nanoparticles for Soil and Groundwater Remediation

2020 ◽  
Vol 17 (16) ◽  
pp. 5817
Author(s):  
Alazne Galdames ◽  
Leire Ruiz-Rubio ◽  
Maider Orueta ◽  
Miguel Sánchez-Arzalluz ◽  
José Luis Vilas-Vilela
Keyword(s):  
Contaminated Soils ◽  
Groundwater Remediation ◽  
Iron Nanoparticles ◽  
Environmental Issues ◽  
High Specific Surface Area ◽  
Zero Valent Iron ◽  
Pilot Studies ◽  
High Effectiveness ◽  
Nanosized Materials ◽  
Zero Valent Iron Nanoparticles

Zero-valent iron has been reported as a successful remediation agent for environmental issues, being extensively used in soil and groundwater remediation. The use of zero-valent nanoparticles have been arisen as a highly effective method due to the high specific surface area of zero-valent nanoparticles. Then, the development of nanosized materials in general, and the improvement of the properties of the nano-iron in particular, has facilitated their application in remediation technologies. As the result, highly efficient and versatile nanomaterials have been obtained. Among the possible nanoparticle systems, the reactivity and availability of zero-valent iron nanoparticles (NZVI) have achieved very interesting and promising results make them particularly attractive for the remediation of subsurface contaminants. In fact, a large number of laboratory and pilot studies have reported the high effectiveness of these NZVI-based technologies for the remediation of groundwater and contaminated soils. Although the results are often based on a limited contaminant target, there is a large gap between the amount of contaminants tested with NZVI at the laboratory level and those remediated at the pilot and field level. In this review, the main zero-valent iron nanoparticles and their remediation capacity are summarized, in addition to the pilot and land scale studies reported until date for each kind of nanomaterials.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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