Pollution Source Localization in Wastewater Networks

Abstract

In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

“…We propose an enhanced data fusion algorithm for the detection, localization, and quantification of pollutants in WWNs. This article extends the system depicted in Chachuła et al [ 24 ], where we proposed the preliminary and very limited version of the data fusion algorithm. The algorithm presented in Chachuła et al [ 24 ] represents the sewage network as a directed tree.…”

“…This article extends the system depicted in Chachuła et al [ 24 ], where we proposed the preliminary and very limited version of the data fusion algorithm. The algorithm presented in Chachuła et al [ 24 ] represents the sewage network as a directed tree. In this structure, there is one and only one path from any point of the sewage network to sink.…”

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

“…In addition, it will be further evaluated with larger sewage networks, more substances, and different sewage hydraulic conditions in further publications. A benchmark of different localization methods–such those described in [ 24 , 27 , 28 , 29 , 30 ]—for an estimation of the pollution source could be considered as an important step forward in this area, as well as adapting effective existing methods for detection of polluting sources in similar environments, such as in [ 37 , 38 ].…”

“…Chachula et al [ 29 ] proposed a data fusion algorithm for solving the pollution source localization problem in wastewater networks. The approach does not utilize machine learning algorithms.…”

Anomaly Detection of Water Level Using Deep Autoencoder

“…Chachula et al [ 36 ] implemented and evaluated a data fusion system that transforms the time-series of sensor measurements into a collection of source-localized discharge events. Based on experiment results, the authors have shown that the proposed framework is an efficient solution for pollution source localization because it can narrow down the number of sources of pollution nodes and therefore achieved faster sensor observations processing (i.e., 100 observations per second).…”