Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor’s relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, “distance from fault” had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.     

Influence of soil conditions on dissolved organic matter leached from forest and wetland soils: a controlled growth chamber study

This study investigated the effects of various soil conditions, including drying-rewetting, nitrogen deposition, and temperature rise, on the quantities and the composition of dissolved organic matter leached from forest and wetland soils. A set of forest and wetland soils with and without the nitrogen deposition were incubated in the growth chambers under three different temperatures. The moisture contents were kept constant, except for two-week drying intervals. Comparisons between the original and the treated samples revealed that drying-rewetting was a crucial environmental factor driving changes in the amount of dissolved organic carbon (DOC). The DOC was also notably increased by the nitrogen deposition to the dry forest soil and was affected by the temperature of the dry wetland soil. A parallel factor (PARAFAC) analysis identified three sub-fractions of the fluorescent dissolved organic matter (FDOM) from the fluorescence excitation–emission matrices (EEMs), and their compositions depended on drying-rewetting. The data as a whole, including the DOC and PARAFAC components and other optical indices, were possibly explained by the two main variables, which were closely related with the PARAFAC components and DOC based on principal component analysis (PCA). Our results suggested that the DOC and PARAFAC component information could provide a comprehensive interpretation of the changes in the soil-leached DOM in response to the different environmental conditions.     

Nitrate reduction by green rusts modified with trace metals

A kinetic study of nitrate reduction by green rust (GR), a group of layered Fe(II)-Fe(III) hydroxide solids, was performed using a batch reactor system. The reduction rate of nitrate by GRs was affected by the anion content in the interlayer of GRs. GR containing F^- (GR-F) showed the fastest reduction rate while GR-SO₄ showed 9 times slower reaction rate than GR-F. The addition of 1 mM Pt or Cu to GR that contained 85 mM Fe(II) improved the reduction kinetics of nitrate by up to 200 times. Pt was an effective activating agent for all GRs. The sequential step reaction model that we proposed appropriately simulated the experimental data. The fastest nitrate reduction by GR-F with Pt was achieved at pH 9 among 7.5 to 11. At that condition, 1 mM nitrate transformed completely into ammonium within 23 min.     

Comparisons of polybrominated diphenyl ethers levels in paired South Korean cord blood, maternal blood, and breast milk samples

Polybrominated diphenyl ethers (PBDEs), commonly used flame retardants, have been reported as potential endocrine disruptor and neurodevelopmental toxicants, thus giving rise to the public health concern. The goal of this study was to investigate the relationship between umbilical cord blood, maternal blood, and breast milk concentrations of PBDEs in South Korean. We assessed PBDE levels in paired samples of umbilical cord blood, maternal blood, and breast milk. The levels of seven PBDE congeners were measured in 21 paired samples collected from the Cheil Woman’s Hospital (Seoul, Korea) in 2008. We also measured thyroid hormones levels in maternal and cord blood to assess the association between PBDEs exposure and thyroid hormone levels. However, there was no correlation between serum thyroxin (T4) and total PBDEs concentrations. The total PBDEs concentrations in the umbilical cord blood, maternal blood, and breast milk were 10.7 ± 5.1 ng g⁻¹ lipid, 7.7 ± 4.2 ng g⁻¹ lipid, and 3.0 ± 1.8 ng g⁻¹ lipid, respectively. The ranges of total PBDE concentrations observed were 2.28-30.94 ng g⁻¹ lipid in umbilical cord blood, 1.8-17.66 ng g⁻¹ lipid in maternal blood, and 1.08-8.66 ng g⁻¹ lipid in breast milk. BDE-47 (45-73% of total PBDEs) was observed to be present dominantly in all samples, followed by BDE-153. A strong correlation was found for major BDE-congeners between breast milk and cord blood or maternal blood and cord blood samples. The measurement of PBDEs concentrations in maternal blood or breast milk may help to determine the concentration of PBDEs in infant.     

Fungal biodegradation of CCA-treated wood and removal of its metal components

In the present study, 5 isolates of brown-rot fungi were used for fungal bioprocessing (FB) of chromated copper arsenate (CCA)-treated wood wastes: Antrodia vaillantii SEL8501, Fomitopsis palustris TYP0507 and TYP6137, and Crustoderma sp. KUC8065 and KUC8611. The isolates showed notable capacity for the degradation of treated wood and removal of CCA components via the American Wood Protection Association soil block test. Among them, Crustoderma sp. KUC8611 effectively decayed the treated wood, causing a mass loss of up to 60%. F. palustris caused extensive leaching of CrO₃ of up to 79% and As₂O₅ of up to 87%, but only moderate leaching of CuO of up to 50%. This high capacity for removal of CrO₃ and As₂O₅ showed a strong logarithmic relationship with the amount of oxalic acid produced in the decayed wood. The majority of metals removed from treated wood during the decay process were deposited in the soil and feeder strip. Further investigation will be required to establish the capability of selected fungi for FB of full-sized lumber treated with CCA.     

In vivo assessment of plant extracts for control of plant diseases: A sesquiterpene ketolactone isolated from Curcuma zedoaria suppresses wheat leaf rust

As an alternative to synthetic pesticides, natural materials such as plant extracts and microbes have been considered to control plant diseases. In this study, methanol extracts of 120 plants were explored for in vivo antifungal activity against Rhizoctonia solani, Botrytis cinerea, Phytophthora infestans, Puccinia triticina, and Blumeria graminis f. sp. hordei. Of the 120 plant extracts, eight plant extracts exhibited a disease control efficacy of more than 90% against at least one of five plant diseases. In particular, a methanol extract of Curcuma zedoaria rhizomes exhibited strong activity against wheat leaf rust caused by P. triticina. When the C. zedoaria methanol extracts were partitioned with various solvents, the layers of n-hexane, methylene chloride, and ethyl acetate showed disease control values of 100, 80, and 43%, respectively, against wheat leaf rust. From the C. zedoaria rhizome extracts, an antifungal substance was isolated and identified as a sesquiterpene ketolactone based on the mass and nuclear magnetic resonance spectral data. The active compound controlled the development of rice sheath blight, wheat leaf rust, and tomato late blight. Considering the in vivo antifungal activities of the sesquiterpene ketolactone and the C. zedoaria extracts, these results suggest that C. zedoaria can be used as a potent fungicide in organic agriculture.