외상성 간장 손상의 임상적 고찰

서상익(Sang Ik Suh),문상은(Sang Eun Moon) 대한외과학회 1990 Annals of Surgical Treatment and Research Vol.38 No.2

심층학습을 이용한 국내 및 해외 국가의 외래어류 위해성 평가

서상익(Sang-Ik Suh),조경철(Gyeong Cheol Jo),남건우(Gun-Woo Nam),김다원(Dawon Kim),안상준(Sang Jun Ahn),기서진(Seo Jin Ki) 대한환경공학회 2022 대한환경공학회 학술발표논문집 Vol.2022 No.11

농업분야의 노출 위해성 평가 모델 개발을 위한 입력 자료 및 시스템 요구사항 분석

서상익 ( Sang-ik Suh ),조경철 ( Gyeongcheol Jo ),기서진 ( Seo Jin Ki ) 한국환경농학회 2021 한국환경농학회 학술대회집 Vol.2021 No.-

광역 스케일의 노출 위해성 평가 모델은 세부적인 물리기반 모델에 비해 (적은 수의 입력 자료를 활용하여) 넓은 지역의 위해성을 보다 효율적을 평가할 수 있고, 특히 위해성이 상대적으로 높은 지역을 빠른 시간에 판별할 수 있는 장점이 있다. 본 연구에서는 농업활동 및 농업용수 유래 유해물질 노출에 따른 (오프라인 기반의) 위해성 평가 모델 개발하고 (최종적으로) 온라인 위해성 평가 시스템을 구축하기 위해 수행되었으며, 이를 위한 예비 연구로 위해성 평가 모델에 필요한 입력 자료 검토하고 오프라인 시스템 요구 사항을 우선적으로 분석하였다. 유럽과 미국 등의 선진국에서 위해성 평가 모델을 검토한 결과 위해성 평가 방식이 상이함을 알 수 있었으며, 이에 따라 요구되는 입력 자료 수준이 상이함을 알 수 있었다. 다만, 위해성 평가에 공통적으로 요구되는 대상 농약에 대한 화학적 특성은 동일한 것으로 판단되어 국내 노출 위해성 평가 모델 개발에 활용할 수 있을 것으로 판단된다. 노출 위해성 평가 모델에 적용하고자 하는 총 190개의 후보 대상 농약 중 일차적으로 53개 오염물질의 특성 DB가 구축되었으며, 향후 추가적인 문헌검토 작업을 통해 나머지 오염물질의 특성 DB가 구축될 경우 국내 노출 위해성 모델에 활용될 수 있을 것으로 판단된다. 오프라인 기반의 노출 위해성 평가 모델을 지리정보시스템(Geographic Information System, GIS)에 탑재할 방안을 검토한 결과, 다양한 오픈 소스 기반의 GIS 소프트웨어 중 최근 환경 및 생태 등의 분야에 활용도가 높은 QGIS 프로그램이 적합한 것으로 판단된다. 또한, 노출 위해성 평가 모델은 Python 콘솔. Python 프로세싱 도구, Python 플러그인 등의 방법을 사용하여 QGIS 소프트웨어 내 탑재가 가능할 것으로 평가되었다.

Biological Treatment of Recalcitrant Industrial Wastewater Using Microbial Augmentation and Bioflocculant-Producing Microorganisms

서상익 ( Sang-ik Suh ),박정호 ( Jung-ho Park ),서현효 ( Hyun-hyo Suh ) 한국환경기술학회 2024 한국환경기술학회지 Vol.25 No.1

For effective treatment of recalcitrant industrial wastewater, microbial augmentation J30, which is composed of Pseudomonas sp. GT21, Bacillus sp. KN27, Acinetobacter sp. KN11, and Neisseria sp. KN13, decomposing strains that show high activities on various organic substances and aromatic compounds, were prepared. As a method to control the solid suspension of industrial wastewater, the culture solutions of A and B, which produce bioflocculants, were applied to wastewater treatment. A microbial augmentation for treating laundry wastewater containing TCE and PCE were prepared by mixing 4 types of strains used to treat paper mill wastewater, 6 types of strains prepared by formulating culture solutions of Flavobacterium sp. GN18 and Acalgenes sp. GN23, which are richloroethylene and pentachloroethylene decomposing strains. It was named microbial augmentation TP32. These culture solutions of strain groups were added to treat paper mill wastewater containing phenolic chlorine compounds and laundry wastewater containing chlorine compounds efficiently. The removal efficiencies of the treatment groups, for TOC in paper mill wastewater were 81.9 % and 73.2 %, respectively. The TOC removal efficiency in paper mill wastewater was 86.5 % in the mixed strains treatment group, where decomposing and flocculating strains were added simultaneously. Changes in T-N and T-P showed a removal efficiency of more than 70 % in all treatments using decomposing, flocculating, and mixed strains. Still, the mixed strain treatment showed the highest removal efficiency. The phenolic chlorine compound, 3-chlorophenol removal efficiency was 85.1 % in the mixed strain treatment group. The removal efficiency of TCE from laundry wastewater showed a removal rate of 85.3 % in 42 hours of incubation when only decomposing strains were added and a removal rate of more than 90 % in 32 hours of incubation when mixed strains were added. The PCE removal rate has been decreased over time of incubation in the decomposing strain treatment and mixed strain treatment, respectively. For effective treatment of recalcitrant industrial wastewater, microbial augmentation J30, which is composed of Pseudomonas sp. GT21, Bacillus sp. KN27, Acinetobacter sp. KN11, and Neisseria sp. KN13, decomposing strains that show high activities on various organic substances and aromatic compounds, were prepared. As a method to control the solid suspension of industrial wastewater, the culture solutions of A and B, which produce bioflocculants, were applied to wastewater treatment. A microbial augmentation for treating laundry wastewater containing TCE and PCE were prepared by mixing 4 types of strains used to treat paper mill wastewater, 6 types of strains prepared by formulating culture solutions of Flavobacterium sp. GN18 and Acalgenes sp. GN23, which are richloroethylene and pentachloroethylene decomposing strains. It was named microbial augmentation TP32. These culture solutions of strain groups were added to treat paper mill wastewater containing phenolic chlorine compounds and laundry wastewater containing chlorine compounds efficiently. The removal efficiencies of the treatment groups, for TOC in paper mill wastewater were 81.9 % and 73.2 %, respectively. The TOC removal efficiency in paper mill wastewater was 86.5 % in the mixed strains treatment group, where decomposing and flocculating strains were added simultaneously. Changes in T-N and T-P showed a removal efficiency of more than 70 % in all treatments using decomposing, flocculating, and mixed strains. Still, the mixed strain treatment showed the highest removal efficiency. The phenolic chlorine compound, 3-chlorophenol removal efficiency was 85.1 % in the mixed strain treatment group. The removal efficiency of TCE from laundry wastewater showed a removal rate of 85.3 % in 42 hours of incubation when only decomposing strains were added and a removal rate of more than 90 % in 32 hours of incubation when mixed strains were added. The PCE removal rate has been decreased over time of incubation in the decomposing strain treatment and mixed strain treatment, respectively.

남강 상류 유역 시설하우스 유출수의 하천 오염기여도 평가

기서진 ( Seo Jin Ki ),서상익 ( Sang-ik Suh ),이춘식 ( Chun-sik Lee ) 한국환경기술학회 2021 한국환경기술학회지 Vol.22 No.4

This study was done to address the effect of greenhouse cultivation effluents on river waters in the Upper Nam River Basin. We measured flow rate and six water quality parameters at the final outlet of the greenhouse cultivation during eight rainfall events randomly occurred between October 2019 and April 2021 which were provided as input to a popular watershed modeling tool SWAT. The years 2012-2014, 2015-2016, and 2017-2018 were set as the warm-up, calibration and validation periods, respectively. Our findings showed that a good model performance was achieved for discharge prediction reaching minimum R² values of 0.65, which was followed by sediments, NO₃, ORGP, and BOD. Annual pollutant loads estimated from the monitoring data were slightly lower than the land-based (unit) pollutant loads. Accordingly, the pollutant load contributions to receiving waters were underestimated by 6.35 % (for BOD), 1.86 % (for T-N), and 3.09 % (for T-P). In addition, pollutant movement, described as the delivery ratio of pollutant loads, varied largely across 18 river segments for BOD and but to only a small extent for T-N and T-P. This calls for more sophisticated monitoring and modeling efforts to develop customized water management strategies along the river segments.

합성곱 신경망을 사용한 하천 수질예측 정확도 평가

박현건 ( Hyun-geoun Park ),서상익 ( Sang-ik Suh ),김수희 ( Su-hee Kim ),기서진 ( Seo Jin Ki ) 한국환경기술학회 2021 한국환경기술학회지 Vol.22 No.4

The present study assessed the applicability of convolutional neural network (CNN), which showed superior performance for classification, segmentation, and natural language processing, to river water quality prediction. Monthly data was compiled from upstream and downstream water quality monitoring stations in the Hwang River over the period of January 2007 through December 2020, from which training and test sets were constructed in the ratio of 70:30. The performance of CNN consisting of single and multiple layers were evaluated separately using univariate data with single dependent variable (i.e., either chemical oxygen demand (COD) or chlorophyll-a (Chl-a) as well as multivariate data with dependent and 9 independent variables. The results showed that the prediction accuracy of the proposed CNN algorithm tested with univariate data was noticeably higher for COD than for Chl-a (in terms of target variable) as well as for multiple layers than for single layer (with respect to model architecture). In addition, the CNN algorithm evaluated with multivariate data achieved had better prediction performance than that of univariate data although its performance varied widely among data sets, and to a less extent, among stations and target variables. No measurable difference was also found in prediction performance of the CNN algorithm (for two target dependent variables) according to the number of (important) independent variables. All these results demonstrate that while the proposed CNN algorithm can be adopted to predict (monthly) water quality variables, its careful architecture design is yet required to achieve substantial performance improvement.

결측치를 포함한 데이터 셋에서의 장단기 메모리 알고리즘의 성능 평가

박현건(Hyun-Geoun Park),서상익(Sang-Ik Suh),조경철(Gyeong Cheol Jo),장진욱(Jinuk Jang),기서진(Seo Jin Ki) 대한환경공학회 2022 대한환경공학회지 Vol.44 No.12