• Title/Summary/Keyword: Contamination Warning System (CWS)

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An Experimental Study on the Determination of Minimum Response Concentration of Inorganic Pollutants in Tap Water (수돗물에서 무기 오염물질 최소 반응 농도 결정의 실험적 고찰)

  • Yoon, Sukmin;Kim, Seong-Su;Chea, Seon-Ha;Park, No-Suk
    • Journal of Korean Society of Environmental Engineers
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    • v.39 no.4
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    • pp.208-213
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    • 2017
  • In this study, four inorganic pollutants (cadmium, chromium, manganese, lead), that could cause contamination events in drinking water distribution system, were selected and batch tests were carried out to determine the "minimum response concentration (MRC)", a part of Korean Contamination Warning system establishment. As the results, the minimum response concentration of cadmium was found to be 0.05 to 0.08 mg/L (0.005 mg/L : water quality standard) and that of chrome was 0.03 mg/L (0.05 mg/L). And the minimum reaction concentration was 0.005 mg/L for manganese (0.05 mg / L for water quality) and 0.02~0.08 mg/L for lead (0.01 mg/L).

A Study on the Statistical Predictability of Drinking Water Qualities for Contamination Warning System (수질오염 감시체계 구축을 위한 수질 데이터의 통계적 예측 가능성 검토)

  • Park, No-Suk;Lee, Young-Joo;Chae, Seonha;Yoon, Sukmin
    • Journal of Korean Society of Water and Wastewater
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    • v.29 no.4
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    • pp.469-479
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    • 2015
  • This study have been conducted to analyze the feasibility of establishing Contamination Warning System(CWS) that is capable of monitoring early natural or intentional water quality accidents, and providing active and quick responses for domestic C_water supply system. In order to evaluate the water quality data set, pH, turbidity and free residual chlorine concentration data were collected and each statistical value(mean, variation, range) was calculated, then the seasonal variability of those were analyzed using the independent t-test. From the results of analyzing the distribution of outliers in the measurement data using a high-pass filter, it could be confirmed that a lot of lower outliers appeared due to data missing. In addition, linear filter model based on autoregressive model(AR(1) and AR(2)) was applied for the state estimation of each water quality data set. From the results of analyzing the variability of the autocorrelation coefficient structure according to the change of window size(6hours~48hours), at least the window size longer than 12hours should be necessary for estimating the state of water quality data satisfactorily.