EEG-Oriented Self-Supervised Learning With Triple Information Pathways Network.

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented self-supervised learning methods and a novel deep architecture to learn rich representation, including information about the diverse spectral characteristics of neural oscillations, the spatial properties of electrode sensor distribution, and the temporal patterns of both the global and local viewpoints. Along with the proposed self-supervision strategies and deep architectures, we devise a feature normalization strategy to resolve the intra-/inter-subject variability problem. We demonstrate the validity of our proposed deep learning framework on the four publicly available datasets by conducting comparisons with the state of the art baselines. It is also noteworthy that we exploit the same network architecture for the four different EEG paradigms and outperform the comparison methods, thereby meeting the challenge of the task-dependent network architecture engineering in EEG-based applications.

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: 1) temporal variability; 2) incomplete observations; and 3) temporal geometric characteristics. However, many pioneer deep learning-based approaches addressing data variability and sparsity have yet to consider inherent geometrical properties sufficiently. These properties are integral to modeling as they correlate with brain region size, thickness, volume, and shape in AD progression. The ordinary differential equation-based geometric modeling method (ODE-RGRU) has recently emerged as a promising strategy for modeling time-series data by intertwining a recurrent neural network (RNN) and an ODE in Riemannian space. Despite its achievements, ODE-RGRU encounters limitations when extrapolating positive definite symmetric matrices from incomplete samples, leading to feature reverse occurrences that are particularly problematic, especially within the clinical facet. Therefore, this study proposes a novel geometric learning approach that models longitudinal MRI biomarkers and cognitive scores by combining three modules: topological space shift, ODE-RGRU, and trajectory estimation. We have also developed a training algorithm that integrates the manifold mapping with monotonicity constraints to reflect measurement transition irreversibility. We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings. Furthermore, we thoroughly analyze our proposed framework through an ablation study.

Field application of bio-foam spray to reduce ammonia emission from ammonia-rich swine manure piles.

In this study, the first field-scale application of a bio-foam spray (a mixture of microbes and a surfactant) for the reduction of ammonia emitted from manure was investigated on six field swine manure piles. The objective of this study was to evaluate the odor suppression ability of bio-foam and odor degradation ability of odor-degrading bacteria loaded in the surfactant foam after covering manure piles. The size of field manure piles tested in this study ranged from 27 to 300 m3. Bio-foam spraying completely suppressed the release of the major odor component, ammonia (NH3), and odor-degrading bacteria in the bio-foam aided in the degradation of NH3 in field swine manure piles. On average, 85.7-100% of NH3 was reduced after 24-48 h of serial bio-foam spray application on the swine manure surface, while the control showed 25-42%. The reduction efficiency of NH3 by the bio-foam application was affected by the bio-foam spray frequency, ambient temperature, ventilation of the field facility, and upward airflow to the pile. The reduction in surface emission of NH3 also reduced the ambient air concentration of NH3 at the gate of the compost facility. NH3 gas measurements at a depth of 50 cm indicated that NH3-degrading bacteria infiltrated the manure and were active in biodegradation. Finally, the measured effectiveness of bio-foam application as shown by this study indicates that sprinkling bio-foam via specialized rotating sprinklers may be an efficient and uniform method for the delivery of bio-foam to wide field areas within composting facilities.

Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis.

Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer, a novel transfer learning framework that effectively models 3-D volumetric images as a sequence of 2-D image slices. To improve the high-level representation in 3-D-form empowering spatial relations, we use a multiview approach that leverages information from three planes of the 3-D volume, while providing parameter-efficient training. For building a source model generally applicable to various tasks, we pretrain the model using self-supervised learning (SSL) for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset. Our pretrained model is evaluated on three downstream tasks: 1) brain disease diagnosis; 2) brain age prediction; and 3) brain tumor segmentation, which are widely studied in brain MRI research. Experimental results demonstrate that our Medical Transformer outperforms the state-of-the-art (SOTA) transfer learning methods, efficiently reducing the number of parameters by up to approximately 92% for classification and regression tasks and 97% for segmentation task, and it also achieves good performance in scenarios where only partial training samples are used.

Implications of Below-Ground Allelopathic Interactions of Camelina sativa and Microorganisms for Phosphate Availability and Habitat Maintenance.

Toxic breakdown products of young Camelina sativa (L.) Crantz, glucosinolates can eliminate microorganisms in the soil. Since microorganisms are essential for phosphate cycling, only insensitive microorganisms with phosphate-solubilizing activity can improve C. sativa's phosphate supply. In this study, 33P-labeled phosphate, inductively coupled plasma mass spectrometry and pot experiments unveiled that not only Trichoderma viride and Pseudomonas laurentiana used as phosphate-solubilizing inoculants, but also intrinsic soil microorganisms, including Penicillium aurantiogriseum, and the assemblies of root-colonizing microorganisms solubilized as well phosphate from apatite, trigger off competitive behavior between the organisms. Driving factors in the competitiveness are plant and microbial secondary metabolites, while glucosinolates of Camelina and their breakdown products are regarded as key compounds that inhibit the pathogen P. aurantiogriseum, but also seem to impede root colonization of T. viride. On the other hand, fungal diketopiperazine combined with glucosinolates is fatal to Camelina. The results may contribute to explain the contradictory effects of phosphate-solubilizing microorganisms when used as biofertilizers. Further studies will elucidate impacts of released secondary metabolites on coexisting microorganisms and plants under different environmental conditions.

Changes in the health of metal-contaminated soil before and after stabilization and solidification.

Stabilization is popularly employed to remediate metal-contaminated soils. It involves the absorption and precipitation of heavy metals to reduce their solubility, movement characteristics, or risk and toxicity. This study aimed to conduct a soil health assessment to determine changes in the health of metal-contaminated soil before and after the application of five stabilizers (acid mine drainage sludge (AMDS), coal mine drainage sludge (CMDS), steel slag, lime, and cement). Soil health assessment, including three soil functions, namely soil productivity, soil stability, and soil biodiversity, evaluated the physical, chemical, and biological indicators (total 16 indicators). Soil health index (SHI) of soil function was calculated by multiplying each indicator score by the weighting factor of each indicator. Total SHI was obtained by summing the three soil-function SHI. Total SHI of the stabilized and test soils followed the order as control soil (1.90) > heavy metal-contaminated soil (1.55) > CMDS-stabilized soil (1.29) > steel slag-stabilized soil (1.29) > AMDS-stabilized soil (1.26) > cement-stabilized soil (0.74) > lime-stabilized soil (0.67). Total SHI of the initial heavy metal-contaminated soil was evaluated as 'normal', before the stabilizer was applied; however, most of the stabilized soils became 'bad' after application of the stabilizers. Furthermore, soils stabilized by cement and lime showed very poor soil health. The results implied that changes in physical and chemical soil properties occurred due to the disturbance caused by the mixing of stabilizers, and ions eluted from the stabilizers could deteriorate soil health further. The findings indicated that soil treated with stabilizers is not suitable for agricultural purposes. Overall, the study suggested that stabilized soil from metal-contaminated sites should be covered with clean soil or monitored for some time before deciding its future agricultural use.

Insights into the biodegradation of diesel oil and changes in bacterial communities in diesel-contaminated soil as a consequence of various soil amendments.

Soil amendment is a promising strategy to enhance biodegradation capacity of indigenous bacteria. To assess the consequences of various soil amendments before large-scale implementation, a microcosm study was employed to investigate the effects of nutrients (TN), surfactants (TS), oxidants (TO), biochar (TB), and zero-valent iron nanoparticles (nZVI; TNP) on diesel degradation, bacterial communities, and community-level physiological profiles (CLPPs) of legacy field contaminated soil. The results showed that the TN, TB, TNP, TS, and TO, reduced 75.8%, 63.9%, 62.8%, 49.3%, and 40.1% of total petroleum hydrocarbons (TPH), respectively, within 120 days, while control (TW) reduced only 33.8%. In all soil amendments, TPH reduction was positively correlated with oxidation-reduction potential and heterotrophic and TPH-degrading bacteria, while negatively correlated with total nitrogen and available phosphate. Furthermore, in TW, TB, and TNP microcosms, TPH reduction showed positive association with pH, whereas in TN, TS, and TO, TPH reduction was negatively associated with pH. The bacterial diversity was reduced in all treatments as a function of the soil amendment and remediation time: the enriched potential TPH-degrading bacteria were Dyella, Paraburkholderia, Clavibacter, Arthrobacter, Rhodanobacter, Methylobacterium, and Pandoraea. The average well colour development (AWCD) values in CLPPs were higher in TB, sustained and improved in TN, and markedly lower in TNP, TS, and TO microcosms. Overall, these data demonstrate that nutrients and biochar amendments may be helpful in boosting biodegradation, increasing diesel-degrading bacteria, and improving soil physiological functions. In conclusion, diesel degradation efficiency and bacterial communities are widely affected by both type and duration of soil amendments.

Comparative toxicity of potential leachates from perovskite and silicon solar cells in aquatic ecosystems.

Globally, perovskite solar cells (PSCs) represent a third-generation photovoltaic technology that is being increasingly implemented and commercialized. However, the biological impacts of leachates from PSCs are poorly understood. Therefore, the aim of this study was to investigate the ecotoxicity of PSC leachates compared with that of commercial Si-based solar cell (SBSC) leachates. We performed leaching assessments and aquatic bioassays using internationally recommended test species and measured and compared the ecotoxicity of PSC and SBSC leachates. As a result of the leaching analyses, Si, Pb, and Al were found to be the most leached elements from broken PSCs and SBSCs. The bioassays indicated that polycrystalline SBSC (p-Si) and monocrystalline SBSC (m-Si) leachates were more toxic to fish embryos than the PSC leachates and that water fleas were sensitive to m-Si leachates, but less sensitive to PSC and p-Si leachates. In addition, principle component analyses indicated that the ecotoxicity of solar cell leachates was related to either the Pb or Si content. This is the first comparative study of the potential ecotoxicity of PSC and SBSC leachates in aquatic ecosystems, and the results of which can be used in the environmentally safe commercialization of solar cells.

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.

Microplastics disrupt accurate soil organic carbon measurement based on chemical oxidation method.

Microplastics are widespread contaminants in soils and terrestrial ecosystems in many areas worldwide. In this study, we measured soil organic carbon (SOC) and soil organic matter (SOM) in microplastic-treated soils to determine if the presence of microplastics could affect the accuracy of carbon-based soil quality indicator measurements. Six different sizes and types of microplastics were selected, and six soil samples were used to evaluate the impacts. Treating soil with polyethylene and low-density polyethylene significantly increased SOC (p < 0.05) when measured with the modified Walkley & Black method; microplastic addition (0.01%, v/v) increased SOC by >40% compared to control organic carbon-poor soil (<10.0 g kg-1). We conclude that the microplastics can disrupt the accurate measurement of SOC. Likely, the physicochemical treatment used in the SOC measurement process can cause the organic compounds and/or carbon complexes to be extracted from microplastics, and this can affect the results. Considering that SOC is a main indicator for assessing soil quality and the global carbon cycle, overestimations caused by microplastic contamination should be further discussed to identify appropriate ways to deal with microplastics as a new carbon source in the environment.

Effect of consortium bioaugmentation and biostimulation on remediation efficiency and bacterial diversity of diesel-contaminated aged soil.

This study aimed to evaluate the effects of consortium bioaugmentation (CB) and various biostimulation options on the remediation efficiency and bacterial diversity of diesel-contaminated aged soil. The bacterial consortium was prepared using strains D-46, D-99, D134-1, MSM-2-10-13, and Oil-4, isolated from oil-contaminated soil. The effects of CB and biostimulation were evaluated in various soil microcosms: CT (water), T1 (CB only), T2 (CB + NH4NO3 and KH2PO4, nutrients), T3 (CB + activated charcoal, AC), T4 (CB + nutrients + AC), T5 (AC + water), T6 (CB + nutrients + zero-valent iron nanoparticles, nZVI), T7 (CB + nutrients + AC + nZVI), T8 (CB + activated peroxidase, oxidant), T9 (AC + nZVI), and T10 (CB + nZVI + AC + oxidant). Preliminary evaluation of the bacterial consortium revealed 81.9% diesel degradation in liquid media. After 60 days of treatment, T6 demonstrated the highest total petroleum hydrocarbon (TPH) degradation (99.0%), followed by T1 (97.4%), T2 (97.9%), T4 (96.0%), T7 (96.0%), T8 (94.8%), T3 (93.6%), and T10 (86.2%). The lowest TPH degradation was found in T5 (24.2%), T9 (17.2%), and CT (11.7%). Application of CB and biostimulation to the soil microcosms decreased bacterial diversity, leading to selective enrichment of bacterial communities. T2, T6, and T10 contained Firmicutes (50.06%), Proteobacteria (64.69%), and Actinobacteria (54.36%) as the predominant phyla, respectively. The initial soil exhibited the lowest metabolic activity, which improved after treatment. The study results indicated that biostimulation alone is inadequate for remediation of contaminated soil that lacks indigenous oil degraders, suggesting the need for a holistic approach that includes both CB and biostimulation. Graphical Abstract.

Degradation of petroleum hydrocarbons in soil via advanced oxidation process using peroxymonosulfate activated by nanoscale zero-valent iron.

Recently, the use of nanoscale zero-valent iron (nZVI) for removal of organic contaminants from aqueous and soil system has increased. In this study, we employ nZVI to activate peroxymonosulfate (PMS) for the degradation of total petroleum hydrocarbons (TPHs) in aged diesel-contaminated soil. Upon PMS activation by nZVI, PMS produces more highly reactive oxygen species (ROS) in both aqueous solution and soil compared to other compounds (PMS/Co(II)), as determined by electron paramagnetic resonance spectroscopy. Thus, nZVI is an effective catalyst for PMS activation, leading to the efficient degradation of diesel oil in soil compared to other catalysts and oxidants. The optimal concentrations of PMS and nZVI were found to be 3 and 0.2%, respectively, showing the best degradation efficiency (61.2% in 2 h). The observed TPH degradation was retarded (up to 19.1-37% efficiency) in the presence of radical scavengers, such as tert-butyl alcohol, nitrobenzene, ethyl alcohol, and isopropyl alcohol. These results also demonstrate that ROS (hydroxyl and sulfate free radicals) are generated via PMS activation by nZVI. Moreover, more than 96% of TPH can be degraded by sequential applications of PMS/nZVI. Factors affecting TPH degradation, namely PMS/nZVI concentration, soil:solution ratio, soil pH, activators, and oxidants, are also analyzed. The results demonstrate that TPH is degraded to below the residential soil quality limit using PMS/nZVI based on the advanced oxidation process (AOP), which is therefore an effective option for chemical remediation of diesel-contaminated soils over a wide range of pH.

Toward Sustainable Environmental Quality: Priority Research Questions for Asia.

Environmental and human health challenges are pronounced in Asia, an exceptionally diverse and complex region where influences of global megatrends are extensive and numerous stresses to environmental quality exist. Identifying priorities necessary to engage grand challenges can be facilitated through horizon scanning exercises, and to this end we identified and examined 23 priority research questions needed to advance toward more sustainable environmental quality in Asia, as part of the Global Horizon Scanning Project. Advances in environmental toxicology, environmental chemistry, biological monitoring, and risk-assessment methodologies are necessary to address the adverse impacts of environmental stressors on ecosystem services and biodiversity, with Asia being home to numerous biodiversity hotspots. Intersections of the food-energy-water nexus are profound in Asia; innovative and aggressive technologies are necessary to provide clean water, ensure food safety, and stimulate energy efficiency, while improving ecological integrity and addressing legacy and emerging threats to public health and the environment, particularly with increased aquaculture production. Asia is the largest chemical-producing continent globally. Accordingly, sustainable and green chemistry and engineering present decided opportunities to stimulate innovation and realize a number of the United Nations Sustainable Development Goals. Engaging the priority research questions identified herein will require transdisciplinary coordination through existing and nontraditional partnerships within and among countries and sectors. Answering these questions will not be easy but is necessary to achieve more sustainable environmental quality in Asia. Environ Toxicol Chem 2020;39:1485-1505. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Biodegradation and post-oxidation of fuel-weathered field soil.

Owing to the less volatile and less biodegradable nature of weathered fuel-contaminated soil, it cannot be easily remediated using conventional bioremediation approaches. Therefore, this study was aimed to enhance the landfarming bioremediation process by introducing post-oxidation for the degradation of the residual total petroleum hydrocarbons (TPH) in fuel-contaminated field soil. A laboratory-scale landfarming bioaugmentation process was performed by using oil-degrading microbes, nutrients, and surfactants, followed by chemical oxidation as a post treatment. The results demonstrated that the addition of microbes and nutrients gradually decreased the TPH concentration of the soil (initial TPH = 5932 ± 267 mg/kg) with a removal efficiency of 70-72% (TPH > 800 mg/kg; Korean limit for non-residential sites). However, the use of post-oxidation treatments with 5% KMnO4 decreased the TPH to approximately 401-453 mg/kg (TPH below 500 mg/kg; residential site limit) with an overall efficiency of 92-93% compared to the corresponding value of 13% for the control (water treatment). Performing landfarming through biodegradation followed by chemical oxidation as a post treatment could successfully remove the weathered TPH in soil below the regulatory limits. Furthermore, the post-oxidation treatment may oxidize the less biodegradable portions only after biodegradation, thereby minimizing the oxidant demand and enhancing the soil properties such as the pH, amount of natural substrates and microbial population.

Combined effects of soil particle size with washing time and soil-to-water ratio on removal of total petroleum hydrocarbon from fuel contaminated soil.

In this study, total petroleum hydrocarbon (TPH) removal from fuel-contaminated field soil was investigated. The influence of the washing method (washing before/after sieving), washing time, soil-to-water ratio, and soil particle size on TPH removal efficiency was evaluated under constant stirring speed. Washing the whole contaminated soil is more efficient than separating the soils into particle size fractions and separately washing the fractions. Particles with differing diameters would be more in contact with each other resulting in detachment of contaminants from the soil particle surface. Effects of soil washing time and soil-to-water ratio on TPH removal were not significant in coarse soil particles (greater than 0.15 mm diameter) but significantly affected TPH removal in fine particles (less than 0.15 mm diameter). This study suggests a threshold washing time of 1 h and a threshold soil-to-water ratio of 1:6 for the whole soil in soil washing. However, soil particles less than 0.075 mm (<75 µm) should be separated after washing to meet the Korean soil TPH limit of less than 500 mg/kg. This study demonstrates the importance of finer soils as debrading media and particle size fraction composition of fuel-contaminated soil in soil washing.

Size-dependent effects of polystyrene plastic particles on the nematode Caenorhabditis elegans as related to soil physicochemical properties.

Plastic polymers are widely used in various applications and are thus prevalent in the environment. Over time, these polymers are slowly degraded into nano- and micro-scale particles. In this study, the free-living nematode, Caenorhabditis elegans, was exposed to polystyrene particles of two different sizes (42 and 530 nm) in both liquid and soil media. The number of offspring significantly (p < 0.05) decreased at polystyrene concentrations of 100 mg/L and 10 mg/kg in liquid and soil media, respectively. In soil media, but not liquid media, C. elegans was more sensitive to the larger particles (530 nm) than the smaller particles (42 nm), and the median effective concentration (EC50) values of the 42 and 530 nm-sized particles were found to be > 100 and 14.23 (8.91-22.72) mg/kg, respectively. We performed the same toxicity bioassay on five different field-soil samples with different physicochemical properties and found that the size-dependent effects were intensified in clay-rich soil samples. A principal component analysis showed that the bulk density, cation exchange capacity, clay content, and sand content were the dominant factors influencing the toxicity of the 530 nm-sized polystyrene particles. Therefore, we conclude that the soil composition has a significant effect on the toxicity induced by these 530 nm-sized polystyrene particles.

Degradation of petroleum hydrocarbons in unsaturated soil and effects on subsequent biodegradation by potassium permanganate.

To date, the oxidation of petroleum hydrocarbons using permanganate has been investigated rarely. Only a few studies on the remediation of unsaturated soil using permanganate can be found in the literature. This is, to the best of our knowledge, the first study conducted using permanganate pretreatment to degrade petroleum hydrocarbons in unsaturated soil in combination with subsequent bioaugmentation. The pretreatment of diesel-contaminated unsaturated soil with 0.5-pore-volume (5%) potassium permanganate (PP) by solution pouring and foam spraying (with a surfactant) achieved the total petroleum hydrocarbon (TPH) removal efficiencies of 37% and 72.1%, respectively. The PP foam, when coupled with bioaugmentation foam, further degraded the TPH to a final concentration of 438 mg/kg (92.1% total reduction). The experiment was conducted without soil mixing or disturbance. The relatively high TPH removal efficiency achieved by the PP-bioaugmentation serial foam application may be attributed to an increase in soil pH caused by the PP and effective infiltration of the remediation agent by foaming. The applied PP foam increased the pH of the acidic soil, thus enhancing microbial activity. The first-order biodegradation rate after PP oxidation was calculated to be 0.068 d-1. Furthermore, 94% of the group of relatively persistent hydrocarbons (C18-C22) was removed by PP-bioaugmentation, as verified by chromatogram peaks. Some physicochemical parameters related to contaminant removal efficiency were also evaluated. The results reveal that PP can degrade soil TPH and significantly enhance the biodegradation rate in unsaturated diesel-contaminated soil when combined with bioaugmentation foam.

Application of a soil quality assessment system using ecotoxicological indicators to evaluate contaminated and remediated soils.

The deterioration of soil quality owing to human activities results in adverse effects on the soil ecosystem. This study developed a systematic method to quantitatively evaluate soil quality based on physical, chemical, biological, and ecotoxicological indicators and proposed the soil quality assessment and management system. This system consists of step-by-step processes, including indicator classification, indicator measurement, scoring and weighting, and soil quality index (SQI) calculation. The novel strategy included the usage of authentic ecotoxicological indicators for realistically interpreting soil quality assessment results. This study used five ecotoxicological indicators, including earthworm survival, enzyme activities, nematode reproduction, plant germination and growth, soil algal biomass, and soil algal photosynthetic capacity. Relatively higher SQI values than those corresponding to the actual soil quality status would be obtained without considering the ecotoxicological indicators. We conclude that the use of ecotoxicological indicator can help in soil quality assessment even under extreme soil quality conditions, such as highly contaminated or physically and chemically remediated soils.

Soil infiltration capacity of chemical oxidants used for risk reduction of soil contamination.

Chemical oxidation has been applied to remove soil contaminants and thereby reduce human and ecological risks from contaminated sites. However, few studies have been conducted on the natural infiltration of oxidant solutions into unsaturated soil. Moreover, the infiltration capacity of oxidant solutions at various concentrations in unsaturated soil has not yet been studied. This study investigated the natural infiltration tendency of oxidant solutions like hydrogen peroxide (H2O2), potassium permanganate (KMnO4), and sodium persulfate (Na2S2O8), in sand and sandy loam. Cumulative infiltration was recorded from a soil column equipped with a Mariotte reservoir. The infiltration rate, sorptivity, and unsaturated hydraulic conductivity were obtained from the cumulative infiltration results. Na2S2O8 showed the highest infiltration rate in both sand and sandy loam, and the infiltration of Na2S2O8 increased as the concentration was increased from 0.05 to 1%. However, the infiltration of KMnO4 and H2O2 solutions was governed more by chemical reaction behavior than by liquid physical properties or soil hydraulic properties. The production of oxides and gas due to reaction induced clogging in flow paths, resulting in less infiltration. Infiltration of H2O2 at concentrations greater than 0.5% was not observed in sand or sandy loam due to gas formation and swelling.

Development of a bacterial consortium comprising oil-degraders and diazotrophic bacteria for elimination of exogenous nitrogen requirement in bioremediation of diesel-contaminated soil.

The purpose of this study was to develop an effective bacterial consortium and determine their ability to overcome nitrogen limitation for the enhanced remediation of diesel-contaminated soils. Towards this, various bacterial consortia were constructed using oil-degrading and nitrogen-fixing microbes. The diesel removal efficiency of various developed consortia was evaluated by delivering the bacterial consortia to the diesel-contaminated soils. The consortium Acinetobacter sp. K-6 + Rhodococcus sp. Y2-2 + NH4NO3 resulted in the highest removal (85.3%) of diesel from the contaminated soil. The consortium containing two different oil-degrading microbes (K-6 + Y2-2) and one nitrogen-fixing microbe Azotobacter vinelandii KCTC 2426 removed 83.1% of the diesel from the soil after 40 days of treatment. The total nitrogen content analysis revealed higher amounts of nitrogen in soil treated with the nitrogen-fixing microbe when compared with that of the soil supplemented with exogenous inorganic nitrogen. The findings in this present study reveal that the consortium containing the nitrogen-fixing microbe degraded similar amounts of diesel to that degraded by the consortium supplemented with exogenous inorganic nitrogen. This suggests that the developed consortium K-6 + Y2-2 + KCTC 2426 compensated for the nitrogen limitation and eliminated the need for exogenous nitrogen in bioremediation of diesel-contaminated soils.