Reliable online water quality monitoring as basis for fault tolerant control

Edthofer, F.; Broeke, J. van den; Ettl, Johannes; Lettl, W.; Weingartner, A.

doi:10.1109/systol.2010.5675985

Cited by 10 publications

(9 citation statements)

References 3 publications

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“…Regional case studies demonstrate that urbanization could increase levels of coliform bacteria [8,9], heavy metals [10,11], and other water indicators of streams, lakes, and rivers, including dissolved oxygen, chemical oxygen demand, pH, total hardness, and other chemicals [12,13]. In recently years, many studies were carried out on pollution source identification [14][15][16], early warning of water pollution [17,18], pollutant event detection [19,20], and water treatment and disinfection [21,22] to explain the influencing factors of drinking water quality. However, there are seldom studies on the influence of urbanization processes on rural drinking water quality.…”

Section: Introductionmentioning

confidence: 99%

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Zhang¹,

Gao²,

Wei

et al. 2017

Sustainability

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Abstract:Urbanization is an inevitable trend in historical development, but eco-environmental problems, including drinking water safety, have gradually become more and more outstanding during the process of rural urbanization. Ten districts in rural areas of Beijing, China were selected to study the effects of urbanization on drinking water quality. The relation between the urbanization index and drinking water quality indicators were explored. The influence of the urbanization process on drinking water quality showed that housing construction, population urbanization, energy consumption, and industrialization during urban development were closely related to drinking water quality. The paired t-test showed the total electricity consumption, living electricity consumption, tertiary industry, and the GDP growth rate had boundary (p = 0.06) or significantly positive (p < 0.05) relations with the qualified rate of rural drinking water. The grey correlation analysis showed that the growth rates of the value-added of housing construction areas were the most important factor affecting comprehensive water quality of Beijing rural areas, followed by the growth rates of the value-added by secondary industry and total electricity consumption, and then the growth rates of the value-added by the tertiary industry and GDP. Urbanization had a significant impact on individual water quality indicators. The results of this study provided some supports for drinking water security in the face of urbanization.

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Section: Introductionmentioning

confidence: 99%

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Zhang¹,

Gao²,

Wei

et al. 2017

Sustainability

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“…Extensive work has also been done for the detection of contamination events. Other activities focused on the detection by sensor nodes [16][17][18][19][20][21], while others [22,23] on the incorporation of the customer complaints as an additional monitoring tool. Edthofer et al [17] have suggested that the station management module is combined with a self adapting data validation module that marks suspicious data, corrects minor problems, and provides feedback when data is too unreliable.…”

Section: Related Workmentioning

confidence: 99%

“…Other activities focused on the detection by sensor nodes [16][17][18][19][20][21], while others [22,23] on the incorporation of the customer complaints as an additional monitoring tool. Edthofer et al [17] have suggested that the station management module is combined with a self adapting data validation module that marks suspicious data, corrects minor problems, and provides feedback when data is too unreliable. Zhao et al [20] proposed the use of conventional water quality parameters (e.g., free chlorine, total chlorine, chloride, pH, turbidity, conductivity, ammonia-nitrogen, nitrate-nitrogen, total organic carbon (TOC), and oxidation-reduction potential (ORP) as surrogate parameters providing indications of contaminants in the water.…”

Section: Related Workmentioning

confidence: 99%

QoWater

Rapousis

Katsarakis

Papadopouli

2015

Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks

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This paper presents QoWater, a novel user-centric crowdsourcing system that enables mobile users to evaluate the quality of drinking, recreational, and irrigation water. The QoWater follows a client-to-server architecture. Via QoWater clients, users upload their feedback about the water quality, together with their position and a timestamp in the spatio-temporal database of QoWater. At the same time, a wireless sensor network monitors the water distribution network (WDN) and uploads the measurements to the QoWater database. We have developed a proof-of-concept implementation of QoWater and performed a preliminary analysis. The paper also highlights the research directions and challenges for developing a robust and effective mechanism.

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“…The model was capable of detecting contaminants after one minute of addition at a concentration of 0.008 mg/L. Utilities already use various EDS combined with a routinely and targeted grab sampling program (Panguluri et al, 2009;Edthofer, 2010). An example of a commercial local EDS is the GuardianBlue™ system.…”

Section: Introductionmentioning

confidence: 99%