• Journal of the Korean Geosynthetics Society
• /
• v.11 no.2
• /
• pp.21-30
• /
• 2012

This paper analyzes the map of soil contamination in years of 2009 by actual survey in Incheon. South-east national industrial complex and the US Army base in Bu-Pyung are turned out to be high polluted area because spilling of oil storage facilities, vehicle and glass industries. So, the soil contamination in Incheon Metropolitan area will be getting more attention. To solve this problem, the soil contamination has been predicted by using the visual Sufer and visual Modflow which are analysis program in geotechnique and water flow. The result of analysis is that F and TPH will be retarded after 5 years. However, the contamination diffusion will be increased if there is no proper management of soil contamination.

• Economic and Environmental Geology
• /
• v.56 no.3
• /
• pp.331-341
• /
• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1368-1370
• /
• 2003

Conventional methods investigating soil Hg contamination are time-consuming and expensive. A quicker method is developed to predict soil Hg content with convolved HyMap, ASTER, and TM spectra. The prediction accuracy for each sensor is satisfactory and similar. It suggests that low spectral resolution is not a limitation for predicting soil Hg content. Correlation analysis reveals that Hg -sorption by iron oxides is the mechanism by which to predict spectrally featureless Hg with reflectance spectra. Future study with field measurements and remote sensing data is recommended.

• Environmental Engineering Research
• /
• v.14 no.1
• /
• pp.53-60
• /
• 2009

The main objective was to develop and assess a dynamic fate and transport model for lead in air, soil, sediment, water and vegetation. Daejeon was chosen as the study area for its relatively high contamination and emission levels. The model was assessed by comparing model predictions with measured concentrations in multi-media and atmospheric deposition flux. Given a lead concentration in air, the model could predict the concentrations in water and soil within a factor of five. Sensitivity analysis indicated that effective compartment volumes, rain intensity, scavenging ratio, run off, and foliar uptake were critical to accurate model prediction. Important implications include that restriction of air emission may be necessary in the future to protect the soil quality objective as the contamination level in soil is predicted to steadily increase at the present emission level and that direct discharge of lead into the water body was insignificant as compared to atmospheric deposition fluxes. The results strongly indicated that atmospheric emission governs the quality of the whole environment. Use of the model developed in this study would provide quantitative and integrated understanding of the cross-media characteristics and assessment of the relationships of the contamination levels among the multi-media environment.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.6
• /
• pp.832-840
• /
• 2019

Recently, pollution from gunpowder due to shell shootings at military drilling sites has raised various environmental concerns. The purpose of this study is to predict the contamination level of RDX in the soil area of the firing range zone near Anwol river watershed, the study site, and the intake area, Anwol river and Imjin river, as a function of time and space. In this study, a multimedia model was developed to predict the variation of RDX contamination by rainfall. The range of the medium was limited to soil, water, and sediment, and excluded the atmosphere, taking into account the physical and chemical properties of RDX with low vapor pressure and low Henry's constant. The pollutant levels of the waters of compartments, including the last section of the Imjin River affecting the water intake, was compared with the environmental standard for RDX.

• Korean Journal of Soil Science and Fertilizer
• /
• v.50 no.2
• /
• pp.115-126
• /
• 2017

There is an increasing concern over arsenic (As) contamination in rice. This study was conducted to develope a prediction model for As uptake by rice based on the physico-chemical properties of soil. Soil and brown rice samples were collected from 46 sites in paddy fields near three different areas of closed mines and industrial complexes. Total As concentration, soil pH, Al oxide, available phosphorus (avail-P), organic matter (OM) content, and clay content in the soil samples were determined. Also, 1.0 N HCl, 1.0 M $NH_4NO_3$, 0.01 M $Ca(NO_3)_2$, and Mehlich 3 extractable-As in the soils were measured as phytoavailable As concentration in soil. Total As concentration in brown rice samples was also determined. Relationships among As concentrations in brown rice, total As concentrations in soils, and selected soil properties were as follows: As concentration in brown rice was negatively correlated with soil pH value, where as it was positively correlated with Al oxide concentration, avail-P concentration, and OM content in soil. In addition, the concentration of As in brown rice was statistically correlated only with 1.0 N HCl-extractable As in soil. Also, using multiple stepwise regression analysis, a modelling equation was created to predict As concentration in brown rice as affected by selected soil properties including soil As concentration. Prediction of As uptake by rice was delineated by the model [As in brown rice = 0.352 + $0.00109^*$ HCl extractable As in soil + $0.00002^*$ Al oxide + $0.0097^*$ OM + $0.00061^*$ avail-P - $0.0332^*$ soil pH] ($R=0.714^{***}$). The concentrations of As in brown rice estimated by the modelling equation were statistically acceptable because normalized mean error (NME) and normalized root mean square error (NRMSE) values were -0.055 and 0.2229, respectively, when compared with measured As concentration in the plant.

• Journal of Soil and Groundwater Environment
• /
• v.23 no.5
• /
• pp.37-44
• /
• 2018

Soil trace elements and their bioaccumulation in agricultural products have attracted widespread concerns, yet the crop uptake characteristics of trace elements in different soil-plants systems have been rarely investigated. Experiments were carried out to investigate the effect of soil properties on trace element concentrations in cabbage and radish. Soil pH and total organic matter were major factors influencing trace elements transfer from soil to vegetables. Inclusion of other soil properties in the stepwise regression analysis improved the regression models for predicting trace element concentrations. Consideration of other soil properties should be taken into account for more precise prediction of trace element concentrations in the two vegetables, which could help quantitatively evaluate the ecologic risk of toxic trace elements accumulation in crops.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.245-245
• /
• 2017

Heavy metal pollution of agricultural soils affects land productivity and has impact on the quality of surrounding ecosystem. Soil microbial community parameters are used as reliable indices for assessing quality of agricultural lands under metal stress. This study investigated bacterial community structure of polluted and undisturbed paddy soils to elucidate soil factors that are related to alteration of bacterial communities under conditions of metal pollution. No obvious differences in the richness or diversity of bacterial communities were observed between samples from polluted and control areas. The bacterial communities of three locations were distinct from one another, and each location possessed distinctive set of bacterial phylotypes. The abundances of several phyla and genera differed significantly between study locations. Variation of bacterial community was mostly related to soil general properties at phylum level while at finer taxonomic levels concentrations of arsenic and lead were significant factors. According to results of bacterial community functional prediction, the soil bacterial communities of metal polluted locations were characterized by more abundant DNA replication and repair, translation, transcription and nucleotide metabolism pathway enzymes while amino acid and lipid metabolism as well as xenobiotic biodegradation potential was reduced.Our results suggest that the soil microbial communities had adapted to the elevated metal concentrations in the polluted soils as evidenced by changes in relative abundances of particular groups of microorganisms at different taxonomic resolution levels, and by altered functional potential of the microbial communities.

• Economic and Environmental Geology
• /
• v.37 no.1
• /
• pp.35-48
• /
• 2004

This paper provides a comprehensive overview of geochemical approaches for investigating and assessing heavy metal contamination in abandoned mine sites. Major sources of contaminants at the abandoned mine sites are mine water, waste rocks, tailings, and chemicals used in beneficiation and mineral processing. Soil, sediment, surface and ground water, and ecological system can be contaminated by heavy metals, which are transported due to erosion of mine waste piles, discharge of acid mine drainage and processed water, and dispersion of dust from waste rocks and tailings. The abandoned mine sites should be characterized using various methods including chemical analysis, mineralogical analysis, acid generation prediction tests, leaching/extraction tests, and field tests. Potential and practical environmental impacts from the abandoned mines should be assessed based on the site characterization.

• Journal of Soil and Groundwater Environment
• /
• v.20 no.2
• /
• pp.1-9
• /
• 2015

Groundwater has been a very precious resource for human life and economic development in the world. With increasing population and food demand, the groundwater use especially for agriculture is largely elevated worldwide. The very much large groundwater use results in depletion of major aquifers, land subsidences in many large cities, anthropogenic groundwater contamination, seawater intrusion in coastal areas and accompanying severe conflicts for water security. Furthermore, with the advent of changing climate, securing freshwater supply including groundwater becomes a pressing and critical issue for sustainable societal development in every country because prediction of precipitation is more difficult, its uneven distribution is aggravating, weather extremes are more frequent, and rising sea level is also threatening the freshwater resource. Under these difficulties, can groundwater be sustaining its role as essential element for human and society in the near future? We have to focus our efforts and wisdom on answering the question. Korean government should increase its investment in securing groundwater resources for changing climate.