A novel sensor partitioning placement model is presented to evenly distribute sensors to water distribution systems (WDS) for monitoring leakages and contamination. First, random walk community detection (RWCD) is used to divide WDS into different partitions. Then, an extended period leakage detection (EPLD) model is presented. The total leakage detection and the average time of leakage detection are used as objective functions for pressure sensor placement. Next, the extended period water quality detection (EPWQD) model is presented. The total intrusion detection, the average percentage of clean water, and the average time of water quality detection are used as objective functions for water quality sensor placement. Evolutionary algorithm (EA) modules are applied to optimize the locations of pressure and water quality sensors. Seven networks are employed to verify the practicability of the model. The results show that leakage and intrusion detection rate is up to 85% during 24 h, and the average percentage of clean water is up to 0.9 in these cases. Finally, the model compares the leakage zone identification (LZI) and the water quality sensor placement strategy (WQSPS) models. The total detection number, the total average time of detection, and the total average percentage of clean water have been improved. Therefore, this model is a high-potential way of sensor placement.
The presence of unpleasant taste and odour in drinking water is an ongoing aesthetic concern for water providers worldwide. The need for a sensitive and robust method capable of analysis in both natural and treated waters is essential for early detection of taste and odour events. The purpose of this study was to develop and optimise a fast stir bar sorptive extraction (SBSE) method for the analysis of geosmin and 2-methylisoborneol (MIB) in both natural water and drinking water. Limits of detection with the optimised fast method (45 min extraction time at 60 degrees C using 24 microL stir bars) were 1.1 ng/L for geosmin and 4.2 ng/L for MIB. Relative standard deviations at the detection limits were under 17% for both compounds. Use of multiple stir bars can be used to decrease the detection limits further. The use of 25% NaCl and 5% methanol sample modifiers decreased the experimental recoveries. Likewise, addition of 1 mg/L and 1.5 mg/L NaOCI decreased the recoveries and this effect was not reversed by addition of 10% thiosulphate. The optimised method was used to measure geosmin concentrations in treated and untreated drinking water. MIB concentrations were below the detection limits in these waters. 相似文献
A real-time PCR assay combined with a pre-enrichment step for the specific and rapid detection of Salmonella in water samples is described. Following amplification of the invA gene target, High Resolution Melt (HRM) curve analysis was used to discriminate between products formed and to positively identify invA amplification. The real-time PCR assay was evaluated for specificity and sensitivity. The assay displayed 100% specificity for Salmonella and combined with a 16-18 h non-selective pre-enrichment step, the assay proved to be highly sensitive with a detection limit of 1.0 CFU/ml for surface water samples. The detection assay also demonstrated a high intra-run and inter-run repeatability with very little variation in invA amplicon melting temperature. When applied to water samples received routinely by the laboratory, the assay showed the presence of Salmonella in particularly surface water and treated effluent samples. Using the HRM based assay, the time required for Salmonella detection was drastically shortened to less than 24 h compared to several days when using standard culturing methods. This assay provides a useful tool for routine water quality monitoring as well as for quick screening during disease outbreaks. 相似文献
Monitoring of microbiological contaminants in water supplies requires fast and sensitive methods for the specific detection of indicator organisms or pathogens. We developed a protocol for the simultaneous detection of E. coli and coliform bacteria based on the Fluorescence in situ Hybridization (FISH) technology. This protocol consists of two approaches. The first allows the direct detection of single E. coli and coliform bacterial cells on the filter membranes. The second approach includes incubation of the filter membranes on a nutrient agar plate and subsequent detection of the grown micro-colonies. Both approaches were validated using drinking water samples spiked with pure cultures and naturally contaminated water samples. The effects of heat, chlorine and UV disinfection were also investigated. The micro-colony approach yielded very good results for all samples and conditions tested, and thus can be thoroughly recommended for usage as an alternative method to detect E. coli and coliform bacteria in water samples. However, during this study, some limitations became visible for the single cell approach. The method cannot be applied for water samples which have been disinfected by UV irradiation. In addition, our results indicated that green fluorescent dyes are not suitable to be used with chlorine disinfected samples. 相似文献
The work presented herein addresses the automatic detection of water losses in water distribution networks (WDN), through the dynamic analysis of the time series related to water consumption within the network and the use of a wavelet change-point detection classifier for identifying anomalies in the consumption patterns. The wavelet change-point method utilizes the continuous wavelet transform (CWT) of time-series (signals) to analyze how the frequency content of a signal changes over time. In the case of water distribution networks the time-series relates to streaming water consumption data from automatic meter reading (AMR) devices, at either the individual consumers’ level or at an aggregated district meter area (DMA) level. The wavelet change-point detection method analyzes the provided time-series to acquire inherent knowledge on water consumption under normal conditions at household or area-wide levels, to then make inferences about water consumption under abnormal conditions. The method is demonstrated on several abnormal WDN operating conditions and anomaly detection cases. 相似文献
The presence of enteric pathogens in water resources represents a serious risk for public health. Therefore, their precise detection, and especially detection of E. coli, which is obviously regarded as the main indicator of faecal contamination of water, is an essential step in ensuring bacterial safety of water. Numerous PCR protocols for detection of E. coli have been published to date. They are usually based on amplification of regions derived from lacZ (beta-D-galactosidase) and uidA (beta-D-glucuronidase) gene sequences. However, these methods are not universal enough for precise detection of all E. coli strains found in water samples. We developed a novel triplex PCR method for detection of E. coli in which cyd gene coding for cytochrome bd complex was co-amplified along with lacZ and uidA genes. Our triplex PCR approach significantly increases the specificity and reliability of E. coli detection in water samples. This approach allowed us to distinguish Shigella flexneri from E. coli. In addition, we were able to detect even non-coliform Klebsiella and Raoutella spp., some of which can also cause infections to humans. 相似文献
Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.
