• Journal of Korea Soil Environment Society
• /
• v.1 no.1
• /
• pp.55-65
• /
• 1996

Soil quality of most of Soil Network area was estimated healthy by employing Soil Pollution Indices (Soil Pollution Score and Soil Pollution Class). However, 1.5∼3.7% of the total Soil Network area was determined Soil Pollution Class (SPC) 4 which may need cleanup process due to slight or heavy pollution with arsenics and heavy metals. Numbers of the SPC 4 sites were 9, 47, 19, 17, and 17 in 1987, 1989, 1991, 1993, and 1994, respectively During 1987 and 1994, all of SPC 4 sites were identified agricultural land except one in 1994. Soil Pollution Scores (SPSs) was determined high around smelters, metalliferous mines, and industrial sites among the 16 major soil pollution sources of the Soil Network. Also, most area of SPC 4 sites were densely populated in these area of the Soil Network. SPSs of Inchon and Taegu were high among the other major cities and provinces in Korea. Numbers of SPC 4 were high in the province of Kangwon, Kyongbuk, Kyongnam amongst. Cumulative numbers of SPC 4 multiplied by a weighting value 0.3 during 1987 and 1994 of the Soil Network were regressed to develop a model equation for prospecting the soil quality. The model equation was Y= 1.16+0.23x, where as Y is the number of Class 4 and x is the year. Resulting the area of SPC 4 were 4.8%, 6.0%, 6.6% of the Soil Network in the year of 2001, 2006, 2011, respectively Based on this results, the area of SPC 4 would increase 5, 7, and 10 times comparing the area polluted with heavy metals in 1987.

• Journal of Korea Soil Environment Society
• /
• v.4 no.2
• /
• pp.45-53
• /
• 1999

The soil samples were measured at 90 sites of Soil's Network In 1997~1998 which was established for the investigation of soil contamination in Seoul. This study was more focused to measure and analyze for BTEX(Benzene, Toluene, Ethylbenzene and Xylene) concentration in the Soil Network. Also, the samples were analyzed by Purge & Trap method. As a result, the BTEX were detected at all sampling sites in Seoul. The Min. Max and Mean BTEX concentration were respectively 0.047mg/kg, 2.618mg/kg and 0.437mg/kg in 1998. The concentration of the BTEX detected at all sampling sites was lower than that of the intervention standards(at industrial areas) of Soil Preservation Act.

• Journal of Korea Soil Environment Society
• /
• v.1 no.1
• /
• pp.47-54
• /
• 1996

Soil pollution indices (SPI) were designed for estimating quality of soil polluted with arsenic and heavy metals. Applying the quality reference value of soil based on its multifunctional purpose was a key step. For considereing multifunctions of soil, soil was classified into 4 groups-agricultural land, residential area, recreational area, factorial site. Then, each concentration of arsenic and each of five heavy metals (Cd, Cu, Hg, Pb, Zn) in soils grouped was transformed to a mathematical value based on the soil quality reference value which may stand for ecological impact. Soil pollution score (SPS) was the addition of the 6 values transformed, and the range of the SPS was divided into 4 Soil Pollution Classes (SPC). The SPC 1, 2, 3, and 4 were SPS <100, SPS 100-200, SPS >200-300, and SPS >300, repectively. SPS and SPC were evaluated with the results of the data from employing the Soil Network of 1994. Based on the soil quality reference values, SPS and SPC of the Soil Network's data were transformed and classified, respectively. Then, SPS and SPC were compared with arsenic and the 5 heavy metal contents of their reference values resulted from the Soil Network's. From this method, soil quality of most of the Soil Network area was estimated to be healthy. However, ca. 3.0~4.0% of the Soil Network area was determined in a range of slightly and heavily polluted. As the mean value of SPS of the Soil Network's was 66.2 which indicates most of soil evaluated was healthy. When the SPSs of the data were divided into 4 groups of SPC, Class 1 (Good quality of soil), Class 2 (Need to be checked area 1), Class 3 (Need to be checked area 2) and Class 4 (Polluted area) were 87.0, 9.4, 2.4, 1.2%, respectively. Using SPI were comparable to those of heavy metal contents in soils, and would be comprehenve to determine the status of soil qulity. Methodology of the developing SPI would be applicable to the other soil pollutants such as organic and inorganics than arsenic and 5 heavy metals used here.

• Journal of Soil and Groundwater Environment
• /
• v.12 no.6
• /
• pp.92-99
• /
• 2007

As of 2007, there are 2,499 groundwater quality monitoring stations in total in Korea. Among them,478 are operated by the MOCT (Ministry of Construction and Transportation) for the National Groundwater Network Program, 781 wells by the ME (Ministry of Environment) for monitoring of the area where imminent contamination is expected, and 1240 wells by the local governments for monitoring of other areas. Even though, water quality data obtained from those wells are being provided to the public since 1999, the information for the wells has not been appropriately informed. In this study, we assessed the wells that are being used for the national groundwater quality monitoring from the points of operation, location, and well configuration to provide suggestions for the improvement of the national groundwater quality monitoring.

• Journal of Korea Water Resources Association
• /
• v.37 no.1
• /
• pp.31-41
• /
• 2004

A soil moisture measuring method of hillslope for Korean watershed is developed to configure spatial-temporal distribution of soil moisture. Intensive surveying of topography had been performed to make a digital elevation model(DEM). Flow distribution algorithms were applied and a measurement system was established to maximize representative features of spatial variation. Soil moisture contribution mechanisms of rainfall-runoff process have been derived. Measurements were performed at the right side hillslope of Buprunsa located at the Sulmachun watershed. A Multiplex system has been operated and spatial-temporal soil moisture data have been acquired. Relatively high correlation relationship between flow distribution algorithm and measurement data can be found on the condition of high humidity.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2006.04a
• /
• pp.100-104
• /
• 2006

58개 기상관측소에서 최근 22년간 측정된 천부 지중온도 자료와 국가지하수관측망의 169개 암반 및 95개 충적층 관측소에서 측정된 지하수 온도 자료를 이용하여 다음과 같은 연구를 수행하였다. 첫째, 우리나라 대기, 지면 및 지하수의 연평균 온도분포도를 제시하였으며, 다중회귀분석을 통하여 대기 및 지면온도를 추정할 수 있는 회귀식을 산정하였다. 둘째, 지면온도에 영향을 미치는 기상 요소로서 일사량, 지구복사, 강수량 및 적설량 자료를 분석하였다. 마지막으로 열전도 모델을 이용하여 심도별 열확산계수를 산정하고 통계 자료를 제시하였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.324-324
• /
• 2011

토양수분량과 증발산량은 물 순환과 강우-유출모형의 검증과 개발, 수자원 계획 및 개발, 작물의 소비수량 산정, 수자원의 손실량 산정 등에 다목적으로 이용되는 귀중한 수문자료로서 유역을 대표할 수 있는 적정한 위치에서 정기적으로 측정되어야 자료의 이용성이 크다. 선진 외국에서는 일찌기 자국의 유역특성에 맞는 토양수분량 및 증발산량 관측망을 구축하여 정기적으로 자료를 생산하여 활용하고 있으나, 국내의 경우는 관련기관의 특정 목적에 따라 관측을 수행하고 있을 뿐 유역을 대표할 수 있는 토양수분량 및 증발산량 자료를 생산하고 있지는 않다. 토양수분 및 증발산량은 기상, 유역, 토지피복, 토양, 임상 특성 인자에 따라 그 크기와 특성이 상이하다. 이와 같은 자료의 특성 때문에 토양수분량 및 증발산량 관측망은 반드시 유역의 대표성이 담보될 수 있도록 설계되어야 한다. 이에 따라 본 연구에서는 토양수분 및 증발산량의 조절인자에 대한 대표성을 반영 할 있는 면적 개념의 관측망을 개념화하여 이를 한강 등 5대 권역에 적용하였다. 토양수분 및 증발산량 관측망 설계 시 필요한 설계인자를 산정하기 위해서 "국가수자원관리종합정보시스템(WAMIS)"의 토지이용 자료를 활용하였으며, 그 결과 산림 66.8%, 논 25.98%, 밭 7.14%로 분석되었다. 산림지를 보다 세분화하였을 때 침엽수림 48.55%, 활엽수림 25.36%, 혼효림 27.09%로 분석되었으며, 이를 중권역수로 구분하면 침엽수림 69개, 활엽수림 21개, 혼효림 13개, 논 8개가 된다. 위의 분석 결과를 토대로 토양수분량 증발산량 관측망을 구축한 결과, 한강 권역은 8개소, 낙동강 권역 8개소, 금강 권역 5개소, 섬진강 권역 2개소, 영산강 권역 2개소의 총 25개소로 구축되었다.

• Journal of Korea Soil Environment Society
• /
• v.4 no.2
• /
• pp.185-192
• /
• 1999

To assess the soil quality of Chonan City, soil analyses were conducted according to the 14 different sampling sites. The soil pH of the agricultural area near the expressway was lower than that of the other farming area, which indicated that this acidification was probably attributed to the acid rain caused by the traffic exhaust gas such as SOx and NOx. Acidification was more severe in the dry farming area than in the rice paddy area. All concentration of 6 different heavy metals (As, Cu, Cd, $Cr^{6+}$, Hg, Pb) and organic contaminants (cyanide, organic-p, PCBs, phenols) were found to be lower than the standard of soil pollution. The concentration of BTEX also lower than the standard of soil pollution. An assessment using the SPI (Soil Pollution Index). which was developed to estimate an overall soil quality, was performed. Each SPC (Soil Pollution Score) were evaluated with the results of the data from this study. The soil quality of most area of Chonan City was determined to Class 1 , which indicated that the soil was healthy.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.248-248
• /
• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• Journal of Soil and Groundwater Environment
• /
• v.18 no.2
• /
• pp.19-26
• /
• 2013

This study developed the classification of National Soil Quality Monitoring Network (NSQM) and its site selection criteria to meet the recently established purposes of the NSQM. The NSQM were suggested by this study to classify into the six-purposes site groups from the current classification of land uses. The six purposes site groups were 1) intensive observation sites, 2) contaminant loading sites, 3) human activity sites, 4) background sites, 5) river soil sites, and 6) sites near the groundwater quality monitoring wells. Furthermore, this study developed the site selection criteria of NSQM utilizing the accumulated NSQM data, road traffic data, chemical emission data, census, soil information, and the literature related to soil quality variation due to contaminant loads. For selecting suitable sites for NSQM, this study used road traffic, chemical emission, the distance from the contaminant sources, and population information as specific criteria. The suggested site classification and criteria were appled for the current 100 NSQM sites for evaluation. Forty sites were met to the criteria suggested by this study, but sixty sites were not met to the criteria. However, some of the sixty sites also included the obscure sites that their addresses were not apparent to find them.