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Evaluation for Impacts of Nitrogen Source to Groundwater Quality in Livestock Farming Area
Gyeong-Mi Lee,Sunhwa Park,Ki-In Kim,Sang-Ho Jeon,Dahee Song,Deok-hyun Kim,Tae-Seung Kim,Seong-Taek Yun,Hyen Mi Chung,Hyun-Koo Kim 한국토양비료학회 2017 한국토양비료학회지 Vol.50 No.5
We investigated 52 livestock farming complexes in Gyeong-Gi and Incheon provinces based on low, medium, and high livestock density and groundwater quality. The objective of this study was to evaluate a relationship between nitrate N concentration in groundwater and animal factors, such as livestock density and animal species. 2,200 groundwater samples for 3 years from 2012 to 2014 at Gyeong-Gi and Incheon provinces were collected and analyzed for pH, EC, DO, ORP, temperature, major anions and cations, such as NO₃-N, HCO₃<SUP>-</SUP>, PO₄<SUP>-</SUP>, SO₄<SUP>2-</SUP>, Cl<SUP>-</SUP>, NH₄-N, K<SUP>+</SUP>, Na<SUP>+</SUP>, Ca<SUP>2+</SUP>, Mg<SUP>2+</SUP>, T-N, and TOC. Average concentration of total N for generated load density was 23,973 g day<SUP>-1</SUP> km<SUP>-2</SUP> for cattle, 51,551 g day<SUP>-1</SUP> km<SUP>-2</SUP> for pig, and 52,100 g day<SUP>-1</SUP> km<SUP>-2</SUP> for poultry. For animal feeding species, average ratio for generated load over discharge load was 16.1% for cattle, 7.8% for pig, and 7.1% for poultry. Therefore, cattle feeding region is highly vulnerable for water pollution compared to pig and poultry feeding areas. The concentrations of chloride, nitrate, and total N in the groundwater samples were higher at high animal farming regions than other regions. The average concentration of nitrate, and chloride in groundwater samples was 5.0 mg L<SUP>-1</SUP>, 16.6 mg L<SUP>-1</SUP> for low livestock density, 6.9 mg L<SUP>-1</SUP>, 17.7 mg L<SUP>-1</SUP> for medium livestock density and 7.6 mg L<SUP>-1</SUP>, 22.7 mg L<SUP>-1</SUP> for high livestock density and total nitrogen (T-N) was 7.7 mg L<SUP>-1</SUP> for low livestock density, 9.4 mg L<SUP>-1</SUP> for medium livestock density, 10.7 mg L<SUP>-1</SUP> for high livestock density. In conclusion, based on this research, for managing groundwater quality near livestock farming regions, Ca-(Cl+NO₃) group from the Piper diagram is more efficient than using 19 factors for water quality standard.
Evaluation for Impacts of Nitrogen Source to Groundwater Quality in Livestock Farming Area
Lee, Gyeong-Mi,Park, Sunhwa,Kim, Ki-In,Jeon, Sang-Ho,Song, Dahee,Kim, Deok-hyun,Kim, Tae-Seung,Yun, Seong-Taek,Chung, Hyen Mi,Kim, Hyun-Koo 한국토양비료학회 2017 한국토양비료학회지 Vol.50 No.5
We investigated 52 livestock farming complexes in Gyeong-Gi and Incheon provinces based on low, medium, and high livestock density and groundwater quality. The objective of this study was to evaluate a relationship between nitrate N concentration in groundwater and animal factors, such as livestock density and animal species. 2,200 groundwater samples for 3 years from 2012 to 2014 at Gyeong-Gi and Incheon provinces were collected and analyzed for pH, EC, DO, ORP, temperature, major anions and cations, such as $NO_3-N$, ${HCO_3}^-$, ${PO_4}^-$, ${SO_4}^{2-}$, $Cl^-$, $NH_4-N$, $K^+$, $Na^+$, $Ca^{2+}$, $Mg^{2+}$, T-N, and TOC. Average concentration of total N for generated load density was $23,973g\;day^{-1}\;km^{-2}$ for cattle, $51,551g\;day^{-1}\;km^{-2}$ for pig, and $52,100g\;day^{-1}\;km^{-2}$ for poultry. For animal feeding species, average ratio for generated load over discharge load was 16.1% for cattle, 7.8% for pig, and 7.1% for poultry. Therefore, cattle feeding region is highly vulnerable for water pollution compared to pig and poultry feeding areas. The concentrations of chloride, nitrate, and total N in the groundwater samples were higher at high animal farming regions than other regions. The average concentration of nitrate, and chloride in groundwater samples was $5.0mg\;L^{-1}$, $16.6mg\;L^{-1}$ for low livestock density, $6.9mg\;L^{-1}$, $17.7mg\;L^{-1}$ for medium livestock density and $7.6mg\;L^{-1}$, $22.7mg\;L^{-1}$ for high livestock density and total nitrogen (T-N) was $7.7mg\;L^{-1}$ for low livestock density, $9.4mg\;L^{-1}$ for medium livestock density, $10.7mg\;L^{-1}$ for high livestock density. In conclusion, based on this research, for managing groundwater quality near livestock farming regions, $Ca-(Cl+NO_3)$ group from the Piper diagram is more efficient than using 19 factors for water quality standard.
거대 점오염원이 주변 대기질에 미치는 영향에 관한 연구
김유근,이화운,전병일,장은숙,홍정혜,문윤섭,원경미,송정희 부산대학교 환경문제연구소 1996 環境硏究報 Vol.14 No.1
In order to show the effect of a vast point pollutant source on air quality of Pusan Thermoeletric Power Plant and its surrounding area, air quality around Pusan Thermoeletric Power Plant was simulated by ISCLT-2 which was supplied by EPA. For this purpose the emission amount of SO_2, NO_2 and TSP was calculated and atmospheric stability was classified for a recent decade(1985~1994) in Pusan. A result of the emission amount showed that much amount of NO_2, NO_2 and TSP are emitted from industrial area. It was clear that NO_2 is much emitted from line source and industrial area. And as a result of classification of atmospheric stability, neutral, stable and unstable state were 58%, 24.1% and 17.9%, respectivly. The result of ai quality simulation by ISCLT-2 showed that Pusan Thermoeletric Power Plant is affecting on the increse of 2.0ppb, 3.0ppb and 5.0㎍/㎥, SO_2, NO_2, and TSP respectively at its surrounding area, site A-3 which was located westward 2.2㎞ distance from Plant
송윤경(Yun-Gyeong Song),정경민(Kyung-Min Jung),이현(Hyun Lee) 한국컴퓨터정보학회 2021 韓國컴퓨터情報學會論文誌 Vol.26 No.12
본 연구에서는 "레플리카"와 같은 텍스트 입력 기반의 부정적 감정 완화가 가능한 국내 인공지능 챗봇인 BERGPT-chatbot을 제안하고자 한다. BERGPT-chatbot은 KR-BERT와 KoGPT2-chatbot을 파이프라인으로 만들어 감정 완화 챗봇을 모델링하였다. KR-BERT를 통해 정제되지 않은 일상 데이터셋에 감정을 부여하고, 추가 데이터셋을 KoGPT2-chatbot을 통해 학습하는 방식이다. BERGPT-chatbot의 개발 배경은 다음과 같다. 현재 전 세계적으로 우울증 환자가 증가하고 있으며, 이는 COVID-19로 인해 장기적 실내 생활이나 대인 관계 제한으로 더욱 심각한 문제로 대두되었다. 그로 인해 부정적 감정 완화나 정신 건강 케어에 목적을 둔 국외의 인공지능 챗봇이 팬데믹 사태로 사용량이 증가하였다. 국내에서도 국외의 챗봇과 비슷한 심리 진단 챗봇이 서비스 되고 있으나, 국내의 챗봇은 텍스트 입력 기반 답변이 아닌 버튼형 답변 중심으로 국외 챗봇과 비교하였을 때 심리 진단 수준에 그쳐 아쉬운 실정이다. 따라서, BERGPT-chatbot을 통해 감정 완화에 도움을 주는 챗봇을 제안하였으며, BERGPT-chatbot과 KoGPT2-chatbot을 언어 모델의 내부 평가 지표인 ‘퍼플렉서티’를 통해 비교 분석하여 BERGPT-chatbot의 우수함을 보여주고자 한다. In this paper, we propose a BERGPT-chatbot, a domestic AI chatbot that can alleviate negative emotions based on text input such as ‘Replika’. We made BERGPT-chatbot into a chatbot capable of mitigating negative emotions by pipelined two models, KR-BERT and KoGPT2-chatbot. We applied a creative method of giving emotions to unrefined everyday datasets through KR-BERT, and learning additional datasets through KoGPT2-chatbot. The development background of BERGPT-chatbot is as follows. Currently, the number of people with depression is increasing all over the world. This phenomenon is emerging as a more serious problem due to COVID-19, which causes people to increase long-term indoor living or limit interpersonal relationships. Overseas artificial intelligence chatbots aimed at relieving negative emotions or taking care of mental health care, have increased in use due to the pandemic. In Korea, Psychological diagnosis chatbots similar to those of overseas cases are being operated. However, as the domestic chatbot is a system that outputs a button-based answer rather than a text input-based answer, when compared to overseas chatbots, domestic chatbots remain at a low level of diagnosing human psychology. Therefore, we proposed a chatbot that helps mitigating negative emotions through BERGPT-chatbot. Finally, we compared BERGPT-chatbot and KoGPT2-chatbot through ‘Perplexity’, an internal evaluation metric for evaluating language models, and showed the superity of BERGPT-chatbot.