In-pipe water quality monitoring in water supply systems under steady and unsteady state flow conditions: A quantitative assessment

Aisopou, Angeliki; Stoianov, Ivan; Graham, Nigel

doi:10.1016/j.watres.2011.10.058

Cited by 85 publications

(43 citation statements)

References 6 publications

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“…Although online multivariate probes are useful for tracking water quality in distribution systems, unsteady hydraulic conditions can affect results and sensor accuracy can be unreliable (Aisopou et al, 2012). In particular, Aisopou et al (2012) found that resuspension of sediments and mixing conditions in pipes was problematic for detecting disinfectant residual, turbidity, and color.…”

Section: Online Sensor Technologymentioning

confidence: 99%

“…In particular, Aisopou et al (2012) found that resuspension of sediments and mixing conditions in pipes was problematic for detecting disinfectant residual, turbidity, and color. Sources of variation in water quality data include instrument noise, change in instrument response over time, actual water quality variation (seasonal, residence time), and distribution system operational changes (source water blending, valve opening, pipe flushing) (Murray et al, 2012).…”

Section: Online Sensor Technologymentioning

confidence: 99%

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Detection

Jain¹

2014

Drinking Water Security for Engineers, Planners, and Managers

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Section: Online Sensor Technologymentioning

confidence: 99%

Section: Online Sensor Technologymentioning

confidence: 99%

Detection

Jain¹

2014

Drinking Water Security for Engineers, Planners, and Managers

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“…precise monitoring of drinking water quality) with contamination detection algorithm can be achieved easily. To categorize the quality of the drinking water, the traditional methods of water quality prediction in [3], [4], [5], [6], [7] implies the collection of water samples at different locations manually and at different sampling time. As there will be a long time gap between the sampling time and the detection time of the contamination, the real time notification about the contamination to the public/consumer cannot be achieved which may lead to adverse health effects.…”

Section: Introductionmentioning

confidence: 99%

Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains

Priya¹,

Shenbagalakshmi²,

Revathi³

2017

IJET

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Drinking Water Distribution Systems facilitate to carry portable water from water resources such as reservoirs, river, and water tanks to industrial, commercial and residential consumers through complex buried pipe networks. Determining the consequences of a water contamination event is an important concern in the field of water systems security and in drinking water distribution systems. The proposed work is based on the development of low cost fuzzy based water quality monitoring system using wireless sensor networks which is capable of measuring physiochemical parameters of water quality such as pH, temperature, conductivity, oxidation reduction potential and turbidity. Based on selected parameters a sensing unit is developed along with several microsystems for analog signal conditioning, data aggregation, sensor data analysis and logging, and remote representation of data to the consumers. Finally, algorithms for fusing the real time data and decision making using fuzzy logic at local level are developed to assess the water contamination risk. Based on the water contamination level in the distribution pipeline the drinking water quality is classified as acceptable/reject/desirable. When the contamination is detected, the sensing unit with ZigBee sends signals to close the solenoid valve inside the pipeline to prevent the flow of contaminated water supply and it intimates the consumers about drinking water quality through mobile app. Experimental results indicate that this low cost real time water quality monitoring system acts as an ideal early warning system with best detection accuracy. The derived solution can also be applied to different IoT (Internet of Things) scenario such as smart cities, the city transport system etc.

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“…Chemometric methods, such as hierarchical cluster analysis, discriminant analysis and factor analysis help to find statistically important factors in water quality data variability and thus improve conclusions in hydro-environmental impact studies. Therefore, the results of application of chemometric techniques along with analytical results, geographical data, and trends in the construction of water systems may all play a meaningful role in the assessment of variations in the quality of drinking water (e.g., Astel et al, 2007;Aisopou et al, 2012). These chemometric methods were used to analyze data sets of water quality parameters measured in order to determine temporal and spatial variations in water (drinking water, surface water, marine water, coastal water, rainwater, river water, groundwater, etc.)…”

Section: Introductionmentioning

confidence: 99%

Assessment of Groundwater Quality by Chemometrics

Papaioannou

Rigas²,

Kella³

et al. 2016

Water Environment Research

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Chemometric methods were used to analyze large data sets of groundwater quality from 18 wells supplying the central drinking water system of Larissa city (Greece) during the period 2001 to 2007 (8.064 observations) to determine temporal and spatial variations in groundwater quality and to identify pollution sources. Cluster analysis grouped each year into three temporal periods (January-April (first), May-August (second) and September-December (third). Furthermore, spatial cluster analysis was conducted for each period and for all samples, and grouped the 28 monitoring Units HJI (HJI=represent the observations of the monitoring site H, the J-year and the period I) into three groups (A, B and C). Discriminant Analysis used only 16 from the 24 parameters to correctly assign 97.3% of the cases. In addition, Factor Analysis identified 7, 9 and 8 latent factors for groups A, B and C, respectively.

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In-pipe water quality monitoring in water supply systems under steady and unsteady state flow conditions: A quantitative assessment

Cited by 85 publications

References 6 publications

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Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains

Assessment of Groundwater Quality by Chemometrics

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