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Development of a Comprehensive Analytical Method for Semi-Volatile Organic Compounds in Sediments by Using an Automated Identification and Quantification System with a GC-MS Database

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Abstract

A comprehensive analytical method for nearly 1000 semi-volatile organic compounds (SVOC) in sediments has been developed using an automated identification and quantification system with a GC-MS database. The results of recovery tests using model compounds, which comprise of 119 non-polar to polar compounds, showed that the method can quantitatively analyze most SVOC, except for very polar substances. Analytical results of a standard reference material were close to certified concentrations. The detection limits of the method were 4 μg/kg when measuring by TIM and 0.4 μg/kg by SIM. The method was applied to actual sediments in rivers in Ho Chi Minh City, Vietnam. A large number of substances, including persistent organic pollutants, which seem to be discharged from domestic sources, were found at relatively high concentrations. From these results, it is confirmed that the developed method is a useful way to obtain a holistic picture of pollution by SVOC, and is a good tool for rapid screening of chemical pollution in sediments.

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Authors and Affiliations

  1. Faculty of Environmental Engineering, The University of Kitakyushu, 1-1 Hibikino, 808-0135, Wakamatsu, Kitakyushu, Japan

    Kiwao Kadokami, Shuangye Pan & Duong Thi Hanh

  2. Department of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, 116024, Dalian, China

    Xuehua Li

  3. Nippon Steel Kankyou Engineering Co., Ltd., 46-80 Nakabaru-sakinohama, 804-0002, Tobata, Kitakyushu, Japan

    Terumi Miyazaki

Authors
  1. Kiwao Kadokami
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  2. Shuangye Pan
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  3. Duong Thi Hanh
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  4. Xuehua Li
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  5. Terumi Miyazaki
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Correspondence to Kiwao Kadokami.

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Kadokami, K., Pan, S., Hanh, D.T. et al. Development of a Comprehensive Analytical Method for Semi-Volatile Organic Compounds in Sediments by Using an Automated Identification and Quantification System with a GC-MS Database. ANAL. SCI. 28, 1183–1189 (2012). https://doi.org/10.2116/analsci.28.1183

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  • DOI: https://doi.org/10.2116/analsci.28.1183

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