Contamination event diagnosis in drinking water networks: A review

The Ganga is largest river in India, serves as a lifeline for agriculture, drinking water, and religious rites. However, it became highly polluted due to influx of industrial wastes and untreated sewages leading to decline of aquatic biodiversity. This study investigated the microbial diversity and plastic-xenobiotic degrading enzymes of six sediment metagenomes of river Ganga at Prayagraj (RDG, TSG, SDG) and Devprayag (KRG, BNG, BRG). The water quality parameters, higher values of BOD (1.8-3.7 ppm), COD (23-29.2 ppm) and organic carbon (0.18–0.51%) were recorded at Prayagraj. Comparative analysis of microbial community structure between Prayagraj and Devprayag revealed significant differences with Bacteroidetes and Firmicutes emerging as the predominant bacterial phyla across six sediment samples. Notably, their prevalence was highest in the BRG samples. Furthermore, 25 OTUs at genus level was consistent across all six samples. Alpha diversity exhibited minimal variation among samples, while beta diversity indicated an inverse relationship between species richness and diversity. Co-occurrence network analysis established that, genera from same and different groups of phyla are showing positive relations with each other. Thirteen plastic degrading enzymes including Laccase, Alkane-1 monooxygenase and Alkane monooxygenase were identified from six sediment metagenomes of river Ganga which can degrade non-biodegradable plastic viz. Polyethylene, Polystyrene and Low-density Polyethelene. Further, 18 xenobiotic degradation enzymes were identified for degradation of Bisphenol, Xylene, Toluene, Polycyclic aromatic hydrocarbon, Styrene, Atrazene and Dioxin etc. This is first report of identification of non-biodegradable plastic degrading enzymes from sediment metagenomes of river Ganga, India. The findings of this study would help in pollution abatement and sustainable management of riverine ecosystem. Investigating the microbiome, gene or enzyme composition, and functional pathways within the sediment of the polluted river Ganga provides insights into the microbial reactions to pollution and their roles within the ecosystem. This is the first report on whole-genome metagenomic analysis of river Ganga sediment at Koteswar (KRG), Bagwan (BNG), Bageswar (BRG), Rasulabad Ghat (RDG), Triveni Sangam (TSG), and Sahidabad (SDG) delves into bacterial diversity and functional characterization. Further, this study identified microbial diversity, plastic and xenobiotic degrading enzymes from sediments of river Ganga, providing insights into the river's ecological health. 

Metagenomic landscape of sediments of river Ganga reveals microbial diversity, potential plastic and xenobiotic degradation enzymes.

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications. 

Gated graph neural networks for identifying contamination sources in water distribution systems.

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.

/pdf/potable-water-identification-with-machine-learning-an-1j4mwmmp.pdf

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

The primary objective of this work is to introduce a novel approach that modifies the method for analyzing and assessing the risk of water supply network failure. The approach aligns with recommendations from the World Health Organization and the European Union regarding the reliability and safety of water supply to consumers. The presented method for assessing the risk in the water distribution subsystem was based on the vulnerability identifying method (VIM) and involves the determination of the vulnerability index (VI). The VIM vulnerability factors considered encompass the failure rate, chemical stability of water, and issues related to water corrosion properties in water distribution subsystems. The obtained risk assessment includes parameters such as the probability of hazard occurrence, the consequences of these hazards, and vulnerability to them. This concept was evaluated using real operational data from the water distribution subsystem. The estimated risk level, under the given operating conditions, indicates its acceptability. 

The Failure Risk Analysis of the Water Supply Network

When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, it is obviously crucial to research the water contamination source identification problem, for which scholars have made considerable efforts and achieved many advances. This paper provides a comprehensive review of this problem. Firstly, some basic theoretical knowledge of the problem is introduced, including the water distribution network, sensor system, and simulation model. Then, this paper puts forward a new classification method to classify water contamination source identification methods into three categories according to the algorithms or methods used: solutions with traditional methods, heuristic methods, and machine learning methods. This paper focuses on the new approaches proposed in the past 5 years and summarizes their main work and technical challenges. Lastly, this paper suggests the future development directions of this problem.

/pdf/review-of-urban-drinking-water-contamination-source-2ugs7dqs.pdf

Review of Urban Drinking Water Contamination Source Identification Methods

Long-term manganese exposure-mediated benthic diatom assemblage in a subtropical stream: Distribution, substrate preferences and Mn-tolerance

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Review of Urban Drinking Water Contamination Source Identification Methods

Long-term manganese exposure-mediated benthic diatom assemblage in a subtropical stream: Distribution, substrate preferences and Mn-tolerance