Optimizing disinfection by-product monitoring points in a distribution system using cluster analysis

Optimizing sampling location for water quality degradation monitoring in distribution systems: Assessing global representativeness and potential health risk

Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The lack of a standardized methodology for monitoring point selection forces operators to rely on general recommendations, historical data and professional experience, which can mask water quality problems and increase the risk to consumers. This study proposes a new methodology to optimize the selection of monitoring points in distribution networks. The method considers the spatiotemporal degradation of water quality, the definition of representative zones and two selection criteria: global representativeness and potential health risk. Representative zones were determined for each node of the network based on hydraulic paths and their water quality spatial variability. Part of the distribution network in Quebec City, Canada was used as the case study, in which four water quality parameters were investigated: free chlorine residual (FRC), heterotrophic plate counts (HPC), trihalomethanes (THMs) and haloacetic acids (HAAs). Seasonal variabilities (summer and winter) were also analyzed. The results obtained for the two criteria and for both seasons were compared, and methodological and practical recommendations were established for dynamic monitoring programs that respond to the needs of operators.

Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review

Drinking water may undergo spatiotemporal changes in quality as it leaves the treatment plant and enters the distribution system. This variability means that not all consumers receive water of the same quality. Monitoring water quality in distribution networks makes it possible to verify the compliance of current regulations and reduce consumption risks associated with water quality degradation. An inaccurate interpretation of the spatiotemporal variability of water quality affects the selection of monitoring locations and the sampling frequency, which may conceal problems with the water quality and increase consumers' risk. This paper presents a chronological and critical review of the literature on the evolution, benefits and limitations of methodologies for the optimization of water quality degradation monitoring in water distribution systems supplied by surface sources. This review compares the different methodologies and examines the types of approaches, optimization objectives, variables, and types of spatial and temporal analysis, as well as the main advantages and limitations. A cost-benefit analysis was conducted to assess applicability in different-sized municipalities (small, medium and large). Future research recommendations for optimal water quality monitoring in distribution networks are also provided.

Comparison of topological, empirical and optimization-based approaches for locating quality detection points in water distribution networks

The positioning of quality detection points as well as the frequency of sampling is a crucial aspect for the implementation of Water Safety Plans (WSPs), which have been proposed worldwide to ensure water quality and to minimize the risk from contamination in water distribution networks (WDNs). In this regard, some international legislations and best practices about quality of drinking water suggest very fine sampling frequencies, but they do not specify where the detection points should be located in a WDN. In this paper, three different approaches, based on empiricism, optimization and topology, respectively, were applied to locate detection quality points in a WDN. The comparison highlighted that empirical approach commonly adopted by water utility practitioners is unsatisfactory. The optimization-based approach, although performing significantly better, is difficult to apply, since it requires a calibrated hydraulic model. The topological approach, based on the use of the betweenness centrality and not requiring any hydraulic information and simulation, proves to be effective, and it can be easily adopted by water utilities to identify the location for quality detection points, due to its simplicity compared with the optimization-based approach.

Spatial distribution of heavy metal contamination and uncertainty-based human health risk in the aquatic environment using multivariate statistical method

Heavy metal contamination in the aquatic environment is one of the most serious health issues worldwide. In this study, an evaluation framework is developed to identify the sources and health risk of heavy metals (i.e., As, Hg, Cr, Cu, Zn, Pb, and Cd) contamination in the North Canal of Fengtai District, China, which is based on multiple approaches, including multivariate statistical method, health risk assessment, and uncertainty analysis. Spatial distribution of these heavy metals could exhibit their impact on the aquatic environment. Pearson's correlation analysis shows that a majority of the correlations between different heavy metals are not significant due to the differences in sources of heavy metals. Principal component analysis indicates that there are four principal components to explain 91.381% of the total variance. Moreover, health risk reveals that hazard quotient values are in low levels, ranging from 0.48 to 0.74, relative higher quotient levels could be observed in the northern section. The carcinogenic risk of Cd has exceeded the acceptable level in S1, S3, and S7. Sensitivity analysis ensures the reliability of health risk assessments. Furthermore, some specific recommendations are given to help decision-makers develop more comprehensive strategies for improving water environment quality.