• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.487-496
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• 2019

Objective: The purpose of this study was to create soil contamination maps using QGIS (Quantum Geographic Information System) and suggest selection methods for soil pollution sources for preferential investigation in a soil contamination survey. Method: Data from soil contamination surveys over five years in Gyeonggi-do Province, South Korea (2013-2017) were used for making soil contamination maps and analyzing the density of survey points. By analyzing points exceeding the concern level of soil contamination, soil pollutant sources for priority management were identified and selection methods for preferred survey points were suggested through a study of the model area. Results: A soil contamination survey was conducted at 1,478 points over five years, with the largest number of surveys conducted in industrial complex and factory areas. Soil contamination maps for copper, zinc, nickel, lead, arsenic, fluoride, and total petroleum hydrocarbons were made, and most of the survey points were found to be below concern level 1 for soil contamination. The density of the survey points is similar to that of densely populated areas and factory areas. The analysis results of points exceeding the criteria showed that soil pollutant sources for priority management were areas where ore and scrap metals were used and stored, traffic-related facilities areas, industrial complex and factory areas, and areas associated with waste and recycling. According to the study of the model area, the preferred survey points were traffic-related facilities with 15 years or more since their construction and factories with a score of 10 or more for soil contamination risk. Conclusion: Soil contamination surveys should use GIS for even regional distribution of survey points and for the effective selection of preferred survey points. This study may be used as guidelines to select points for a soil contamination survey.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 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
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• 2003.11a
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• pp.1368-1370
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• 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.

• Journal of Environmental Science International
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• v.25 no.1
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• pp.11-21
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• 2016

The soil contamination by heavy metals in playgrounds of kindergartens in Vilnius city is analysed in this article. The aim of this research is to investigate and evaluate soil contamination by heavy metals in playgrounds of kindergartens in different territories of Vilnius city. Concentrations of heavy metals were measured using Thermo Fisher Scientific Niton$^{(R)}$ XL2 X-ray fluorescence spectrometer. Maximum allowable and background concentrations that are given in Lithuanian hygiene standard and Lithuania geochemical atlas are used to compare and evaluate concentrations of heavy metals. Concentrations of heavy metals and their spatial distribution were analysed in order to exclude the most contaminated areas relating with different functional areas of the city. Geo-statistical analysis and maps of spatial distribution were developed using IDW interpolator in ArcMap software. Detail soil surveys helps to assess the extent of anthropogenic impact in different parts of the city which can be harmful to the soil ecosystem and human health. Such researches can help to change or select different function for city areas in territorial planning process.

• Membrane and Water Treatment
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• v.10 no.4
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• pp.313-320
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• 2019

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

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

• Journal of Soil and Groundwater Environment
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• v.23 no.4
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• pp.26-41
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• 2018

The purpose of this study is to improve the Original DRASTIC Model (ODM) for the assessment of groundwater contamination vulnerability on the GIS platform. Miryang City of urban and rural features was selected for the study area to accomplish the research purpose. Advanced DRASTIC Model (ADM) was developed adding two more DRASTIC factors of lineament density and landuse to ODM. The fuzzy logic was also applied to ODM and ADM to improve their ability in evaluating the groundwater contamination vulnerability. Although the vulnerability map of ADM was a little simpler than that of ODM, it increased the area of the low vulnerability sector. The groundwater vulnerability maps of ODM and ADM using DRASTIC Indices represented the more detailed descriptions than those from the overlap of thematic maps, and their qualities were improved by the application of fuzzy technique. The vulnerability maps of ODM, ADM and FDM was evaluated by NO3-N concentrations in the study area. It was proved that ADM including lineament density and landuse factors produced a more reliable groundwater vulnerability map, and fuzzy ADM (FDM) made the best detailed groundwater vulnerability map with the significant statistical results.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.1
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• pp.37-45
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• 2009

Susceptibility assessment of groundwater contamination is a useful tool for many aspects of regional and local groundwater resources planning and management. It can be used to direct regulatory, monitoring, educational, and policy-making efforts to highly vulnerable areas. In this study, a semi process-based was proposed to evaluate relative susceptibilities to groundwater contamination by nitrate on a regional scale. Numerical simulation based on data from each soil series was done to model water flow within soil profiles that were related to groundwater contamination by nitrate. Relative vulnerability indices for each soil series were produced by manipulation of amount of leaching flux, amount of average water storage in a soil profile, and amount of average water storage change. These indices were designed to convey the trend of leaching flux and to maximize spatial resolution. The resulting vulnerability distribution map was used to locate highly vulnerable sites easily with an appropriate grouping the indices, and was then compared with those from groundwater nitrate concentrations monitored. An excellent agreement was obtained across nitrate concentrations from the highly vulnerable regions and those from the low to stable regions.

• Environmental Engineering Research
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• v.23 no.1
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• pp.84-91
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• 2018

The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment. DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done with GOD and AVI vulnerability models. Model validation was done with $NO_3$, $SO_4$ and Cl concentrations. These maps help to assess the zones of potential risk of contamination to the groundwater resources.

• Journal of Korea Water Resources Association
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• v.38 no.11
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• pp.927-935
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• 2005

Soil erosion by rainfall is important factor for basin management because it reduces reservoir capacity and breaks out the contamination of water caused by turbid water. Recently, soil erosion study with GIS is in progress but does not consider soil erosion source area. This study calculated soil erosion amount using GIS-based soil erosion model in Imha basin and examined soil erosion source area using SPOT 5 High-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area by applying field survey method in common areas such as dry field and orchard area those are difficult to confirm soil erosion source area using satellite image.