메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술대회자료
저자정보
Kim, Won-Seok (Arsenic Geoenvironment Laboratory [NRL], Deportment of Environmental Science and Engineering, Gwungju Institute of Science and Technology [GIST]) Kim, Soon-Oh (Department of Earth Environment Science, Gyeongsang National University) Kim, Kyoung-Woong (Arsenic Geoenvironment Laboratory [NRL], Deportment of Environmental Science and Engineering, Gwungju Institute of Science and Technology [GIST])
저널정보
한국지하수토양환경학회 한국지하수토양환경학회 학술발표회 한국지하수토양환경학회 2004년도 임시총회 및 추계학술발표회
발행연도
2004.1
수록면
72 - 75 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
The potential of electrokinetic (EK) technology has been successfully demonstrated for the remediation of heavy metal contaminated fine-grained soils through laboratory scale and field application studies. Arsenic contamination in soil is a serious problem affecting both site use and groundwater quality. The EK technology was evaluated for the removal of arsenic from two soil samples: kaolinite clay artificially contaminated with arsenic and arsenic-bearing tailing soil taken from the Myungbong (MB) mining area. The effect of cathodic electrolyte on the process was investigated using three different types of electrolyte: deionized water (DIW), potassium phosphate (KH$_2$PO$_4$) and sodium hydroxide (NaOH). The result of experiments on the kaolinite clay shows that the potassium phosphate was most effective in extracting arsenic, probably resulting from anion exchange of arsenic species by phosphate. On the contrary, the sodium hydroxide seemed to be most efficient in removing arsenic from the tailing soil, and it is explained by the fact that sodium hydroxide increased the soil pH and accelerated ionic migration of arsenic species through increase in desorption and dissolution of arsenic species into pore water.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0