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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received December 2, 2011
Accepted July 5, 2012

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Determination of key sensor locations for non-point pollutant sources management in sewer network

OnYu Kang¹ SeungChul Lee¹ Kailas Wasewar^{1 2} MinJeong Kim¹ Hongbin Liu¹ TaeSeok Oh¹ Emad Janghorban¹ ChangKyoo Yoo^1†

¹Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea ²Department of Chemical Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur - 440010 Maharashtra, India

Korean Journal of Chemical Engineering, January 2013, 30(1), 20-26(7), 10.1007/s11814-012-0108-y

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Abstract

As the importance of watershed management has emerged for water systems, non-point pollutant sources have been blamed as the main problem of water pollution. To control non-point pollutant sources, it is necessary to monitor sewers connected to the watershed and to analyze their effects on the sewer network. As the cost to monitor a sewer network depends on the number of sensors installed, the monitoring stations should be decided with proper guide of_x000D_ the installation location rule. In present paper, a new method to select the proper sensor location is proposed by combining monitoring information with data mining techniques. To estimate the amount of pollutants by wash-off and to find the sensor locations in a sewer network, three scenarios are considered based on rainfall intensity, influent concentrations and flow rate. The optimal locations of the sensor were selected based on the proposed method to facilitate the management of non-point pollutant source in sewer network. The presented approach can be extended to a complex sewer network system to design a minimum number of sensors and optimum locations for the sensors.

Keywords

Sewer Network Non-point Pollutant Sources Sensor Location Data Mining ANOVA

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