• Journal of Soil and Groundwater Environment
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• v.13 no.5
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• pp.81-87
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• 2008

In accompany with the transfer of US army bases, recent surveys reported serious contamination of soils by the release of petroleum from storage facilities and heavy metals accumulated in rifle-ranges. These problems have made an increased concerns of cleanup technology for contaminated soils. In this study, a full-scale soil washing process improved by multistage continuous desorption and agitational desorption techniques was examined for petroleum-contaminated soils obtained from three different remedial sites that contained 29.3, 16.6, and 7.8% of silt and clay, respectively. The initial concentrations of total petroleum hydrocarbon (TPH) were 5,183, 2,560, and 4,860 mg/kg for each soil. Pure water was applied to operate washing process, in which water used for washing process was recycled 100% for over 6 months. The results of full-scale washing tests showed that the TPH concentrations for soils (> 3.0 mm) were 50${\sim}$356 mg/kg (85.2${\sim}$98.2% removal rates), regardless of the contents of silt and clay from in A, B and C soil, when the soils were washed at 3.0 kg/$cm^2$ of injection pressure with the method of wet particle separation. Based on the initial TPH concentration, the TPH removal rates for each site were 85.2, 98.2 and 89.9%. For soils in the range of 3.0${\sim}$0.075 mm, the application of first-stage desorption technique as a physical method resulted 834, 1,110, and 1,460 mg/kg of TPH concentrations for each soil, also additional multi-stage continuous desorption reduced the TPH concentration to 330, 385, and 245 mg/kg that were equivalent to 92.4, 90.6, and 90.1% removal rates, respectively. The result of multi-stage continuous desorption for fine soil (0.075${\sim}$0.053 mm) were 791, 885, and 1,560 mg/kg, and additional agitation desorption showed 428, 440, and, 358 mg/kg of TPH concentrations. Compared with initial concentration, the removal rates were 92.0, 93.9 and 92.9%, respectively. These results implied we could apply strategic process of soil washing for varies types of contaminated soils to meet the regulatory limit of TPH.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Journal of Soil and Groundwater Environment
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• v.9 no.4
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• pp.24-31
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• 2004

A cold mix asphalt (CMA) treatment process was proposed as a tool to recycle soils contaminated with petroleum hydrocarbons. Experimental studies were conducted to characterize performances of the CMA process in treating soils contaminated with diesel or diesel compounds. From the screening experiments, it was found that performances of five types of asphalt emulsions that contained a cationic or an anionic or a nonionic surfactant were not substantially different. In consideration of higher affinity for soils and higher sorption coefficients obtained, an emulsion containing Lauryl Dimethyl Benzyl Ammonium Chloride (LDBAC) was selected as a promising asphalt emulsion for treating diesel-contaminated soils. When the asphalt emulsion LDBAC was applied to treat three compounds that originated from diesel, the removal efficiencies obtained in the order of decreasing efficiencies were as follows: docosane > pentadecane > undecane. Leaching experiments on the specimen formulated by the emulsion LDBAC found that the selected treatment method could treat soils with diesel concentrations as high as 10,000 mg/kg. Leaching of the diesel from the specimen was controlled by diffusion for the first four days and then leaching rate diminished substantially. The latter behavior was characterized as depletion, which represents that the contaminant released amounts to more than $50\%$ of the total amount of the contaminant that can be leached. The amounts of three diesel compounds leached from the specimen in the order of decreasing amount were undecane, pentadecane, and docosane. The curing of the soil contaminated with pentadecane was relatively slow.

• Journal of Soil and Groundwater Environment
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• v.20 no.7
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• pp.13-22
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• 2015

In this study, we have developed the performance evaluation model for the optimal soil remediation technology selection. Performance evaluation model is composed in the evaluation of two steps. In the first stage, the candidate technologies are derived according to the conditions of drilling, type and concentration of pollutants, and the saturated/unsaturated of target site. In the second stage, each individual candidate technology is evaluated by performance evaluation model. The performance evaluation model has 5 groups of evaluation items and 12 evaluation items which have their own evaluation index and their own weights through the AHP approach surveying 40 experts. From the case study of actual design cases, the applicability of the performance evaluation model was confirmed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.1-12
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• 2020

As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.211-224
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• 2023

In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km² area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.681-692
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• 2017

In recent years, large-scale construction projects such as road pavement construction and new city construction have been carried out nationwide with by the expansion of social overhead facilities and base on the economic development planning, resulting in a rapid increase in artificial slope damage. The existing vegetation-based re-installation method of the slope surface greening method reveals various problems such as lack of bonding force, drying, and lack of organic matter. In this study, research was carried out using vegetation-based material and environmentally friendly soil additives, were are used in combination with natural humus, Bark compost, coco peat, and vermiculite. Uniaxial compressive strength was measured according to the mixing ratio of soil additives and the strength was analyzed. Experiments were carried out on the characteristics of the soil material to gauge the slope protection properties by using the soil compaction test method wherein the soil and the soil additive materials are mixed in relation to the soil height, the number of compaction, the compaction method (layer) and the curing condition. As a result of the experiment, excellent strength performance was demonstrated in soil additives using gypsum cement, and it satisfied vegetation growth standards by using performance enhancer and pH regulator. It was confirmed that the strength increases with the mixing of soil and soil additive, and the stability of slope protection can be improved.

• Journal of Animal Environmental Science
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• v.8 no.3
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• pp.135-144
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• 2002

A computer simulation was carried out to investigate the optimum capacity of liquid manure tank spreader which is used as a tractor attachment. Soil physical properties, such as soil moisture content, bulk density, soil hardness and soil types were measured in the 10 major rice production area for computer simulation. Mathematical model which include soil physical properties and vehicle factor was used for computer simulation. Most of the soil type of the investigated area was sandy clay loam. Soil moisture content ranged between 30 and 40% mostly. Soil bulk density was in the range of 1,500 to 1,700 kg/$m^3$. Soil hardness ranged between 1 to 18 $cm^2$. Soil hardness incorporate the effects of many soil physical properties such as soil moisture content, soil type and soil bulk density, and so the range of soil hardness is greater than any other physical properties. The capacity of liquid manure tank spreader was above 3,000 kg$_{f}$ for the most of the investigated areas, and mostly in the range of 4,000 to 6,000 $kg_f$ depending upon the slip. But for the soft soil area such as Andong and Asan, the tractor itself has mobility problem and shows no pulling force for some places. For this area, the capacity of liquid manure tank spreader ranged between 1,000 and 2,000 $kg_f$ mostly, so the capacity of liquid manure tank spreader should be designed as a small capacity trailer compared to the other area.mpared to the other area.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.438-443
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• 2002

본 연구는 수도작에서 토양과 식물체의 분광반사특성과 영상처리를 이용한 기계시각 잡초검출 센서를 개발하기 위한 기초연구로서 분류하고자 하는 대상체들의 분광반사율을 조사하여 주요한 파장을 선정하고 선정된 파장을 이용한 판별분석을 통해 각 대상체에 대한 분류 정확도를 중심으로 잡초검출 가능성을 조사하기 위하여 수행하였으며, 실험으로부터 얻은 결론은 다음과 같다. 1. 토양과 식물체를 구분하는데 효과적인 파장은 마른 토양의 경우 680 nm, 배수 토양에 있어서는 810 nm로 선정하였고, 토양을 배제한 후 벼와 잡초를 구분하기에 효과적인 파장은 580, 680 nm로 선정하였다. 2. 토양과 식물체를 구분하기 위한 판별분석 결과 2가지 토양상태 모두 식물체와 완전히 구분 가능한 것으로 나타났다. 벼와 잡초를 구분하기 위한 실험에서, 벼는 98%의 분류정확도로 구분이 가능하였고, 잡초는 83%의 분류정확도로 구분이 되는 것으로 나타났다. 따라서 차후 분광학적 원리를 이용한 센서를 제작할 때 본 연구에서 선택한 주요 파장과 판별함수를 이용하여 장치를 구성하고 알고리즘을 제작한다면 벼, 잡초, 토양을 효과적으로 구분이 가능할 것으로 판단되었다. 3. 컬러 CCD 카메라를 사용하는 경우에 있어 식물체와 토양을 구분하기 위해 3 종의 파장 중 630 nm 파장만의 이용을 고려하여 그 분류성능을 분석한 결과, 식물체와 토양은 소수의 관측치를 제외하고 완전히 구분이 가능했고, 벼와 잡초를 구분한 결과에서는 비교적 높은 분류능력을 가진 것으로 나타나 차후 컬러 CCD 카메라를 이용하여 장치를 구성하는데 좋은 기초가 될 것으로 판단된다. 배양체의 접종작업은 모든 배양실이 인력에 의존하였으며, 배양체를 배지와 분리하여 불필요한 부분을 제거하고 배양작물에 따라 생육정도를 2~3등급으로 구분하여 배양용기의 배지 위에 치상하는 과정으로 수행되었으며, 작업능률은 호접란의 경우 배양병에 25본을 접종하는데 시간당 6병, 심비디움은 원형 플라스크에 25본을 접종하는데 시간당 10병 정도였다. 바. 식물체의 대량증식에 사용되는 플라스크, 배양병, PE용기 등 배양용기의 세척작업은 농원의 1개배양실에서 간이식 세척기, 이 외의 9개배양실은 모두 물에 담겨 두었다가 세제와 브러쉬 등을 사용하여 인력으로 세척하고 있어 생력화 기술개발이 요구되었다.도가 빠를수록 건조속도가 빨라졌으며, 건조에너지도 1,334kcal/kg.water로 비슷하게 소요되었다. 마. 시험구와 대비구의 건감률은 시험구에서 1.08~1.36w.b./h로 나타나 대비구보다 약 9.9~18.3%가 높게 나타났고, 건조에너지는 10.2~14.6%가 절감되었다. 발아율은 열풍온도가 낮을수록 높게 나타났고 시험구가 대비구보다 발아율이 낮게 나타났으며, 동할률 증가량도 원적외선.열풍 복합건조방법이 높게 나타나 이것은 곡물 표면에 원적외선 방사에의한 복사열이 전달되어 열장해를 받았기 때문으로 판단되며, 금후 더 연구하여 적정 열풍온도 및 방사체 크기를 구명해야 할 것이다.으로 보여진다 따라서 옻나무 유래 F는 포유동물의 생식기능에 중요하게 작용하는 것으로 사료된다.된다.정량 분석한 결과이다. 시편의 조성은 33.6 at% U, 66.4 at% O의 결과를 얻었다. 산화물 핵연료의 표면 관찰 및 정량 분석 시험시 시편 표면을

• The Korean Journal of Pesticide Science
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• v.9 no.4
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• pp.303-310
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• 2005

This study was performed to examine analytical method of a quinolone compound, oxolinic acid in paddy soil by HPLC coupled with UV detector. Two types of soil texture in different regions were used for this experiment. Oxolinic acid was extracted by a 4 M-KOH : MeOH(1 : 3, v/v) mixtures and acidified followed by liquid-liquid partitioning in dichloromethane. Dichlormethane layer was dehydrated, evaporated and analyzed by HPLC (262 nm). Retention time was 10.2 min. The standard calibration curve of oxolinic acid showed linearity ($r^2>0.999^{**}$, y=378.99x+135.08) in the range of $1{\sim}40$ ng. The mean recoveries, evaluated from fortified soil samples at two concentration levels of 0.2 mg/kg and 1.0 mg/kg, were $90.9{\pm}4.52%$(C.V. 4.97%) and $95.0{\pm}0.23%$(C.V. 0.24%) for soil 1 and $92.2{\pm}1.15%$(C.V. 1.25%) and $93.1{\pm}0.31%$ (C.V. 0.33%) for soil 2, respectively The detection limits of two types of soils were same as 0.05 ppm. Overall, the present analytical method of oxolinic acid by HPLC coupled with UV detector seems to be used reasonably.