한국환경보건학회지 (Journal of Environmental Health Sciences)

  • 제45권2호
  • /
  • Pages.192-202
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  • 2019
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  • 1738-4087(pISSN)
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  • 2233-8616(eISSN)
한국환경보건학회 (Korean Society of Environmental Health)
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DOI QR코드

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수생태 독성자료의 정규성 분포 특성 확인을 통해 통계분석 시 분포 특성 적용에 대한 타당성 확인 연구

The Validation Study of Normality Distribution of Aquatic Toxicity Data for Statistical Analysis

  • 옥승엽 (한양대학교 해양융합공학과) ;
  • 문효방 (한양대학교 해양융합공학과) ;
  • 나진성 (한국생산기술연구원 환경규제기술센터)
  • OK, Seung-yeop (Department of Marine Sciences and Convergent Technology, Hanyang University) ;
  • Moon, Hyo-Bang (Department of Marine Sciences and Convergent Technology, Hanyang University) ;
  • Ra, Jin-Sung (Regulatory Chemical Analysis & Risk assessment Center, Korea Institute of Industrial Technology (KITECH))
  • 투고 : 2019.04.01
  • 심사 : 2019.04.20
  • 발행 : 2019.04.30
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초록

Objectives: According to the central limit theorem, the samples in population might be considered to follow normal distribution if a large number of samples are available. Once we assume that toxicity dataset follow normal distribution, we can treat and process data statistically to calculate genus or species mean value with standard deviation. However, little is known and only limited studies are conducted to investigate whether toxicity dataset follows normal distribution or not. Therefore, the purpose of study is to evaluate the generally accepted normality hypothesis of aquatic toxicity dataset Methods: We selected the 8 chemicals, which consist of 4 organic and 4 inorganic chemical compounds considering data availability for the development of species sensitivity distribution. Toxicity data were collected at the US EPA ECOTOX Knowledgebase by simple search with target chemicals. Toxicity data were re-arranged to a proper format based on the endpoint and test duration, where we conducted normality test according to the Shapiro-Wilk test. Also we investigated the degree of normality by simple log transformation of toxicity data Results: Despite of the central limit theorem, only one large dataset (n>25) follow normal distribution out of 25 large dataset. By log transforming, more 7 large dataset show normality. As a result of normality test on small dataset (n<25), log transformation of toxicity value generally increases normality. Both organic and inorganic chemicals show normality growth for 26 species and 30 species, respectively. Those 56 species shows normality growth by log transformation in the taxonomic groups such as amphibian (1), crustacean (21), fish (22), insect (5), rotifer (2), and worm (5). In contrast, mollusca shows normality decrease at 1 species out of 23 that originally show normality. Conclusions: The normality of large toxicity dataset was not always satisfactory to the central limit theorem. Normality of those data could be improved through log transformation. Therefore, care should be taken when using toxicity data to induce, for example, mean value for risk assessment.

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