Natural Organic Matter (NOM) in water arises from decomposed plant and animal matter and is ubiquitous in drinking water sources. The variation in NOM concentrations and characteristics, influenced by events like floods and droughts, plays a crucial role in water treatment efficiency and water quality received by the public. For example, increased NOM concentrations necessitate higher levels of coagulants and disinfectants, leads to the formation of disinfection by-products (DBPs), and plays a key role in biofilm development. When considering impacts of NOM, it is not only the presence or concentration but the makeup or proportion of varying sub-groups which can impact water quality. Formation of DBPs, corrosion and scaling, pollutant transport, aesthetic deterioration, and biofilm growth are dependent on the relative composition of NOM within the distribution system. Although the role of NOM concentration and characteristics is well studied during treatment, the impacts of residual NOM in water distribution systems have received less attention. In particular, it is clear, due to the varying roles of NOM sub-groups, that greater consideration of NOM characteristics in distribution systems is needed. This paper reviews the broad implications of NOM characteristics for water distribution systems and explores challenges and opportunities in NOM characterization within distribution systems. Furthermore, the influence of NOM characteristics in premise plumbing is examined. The review highlights the necessity for precise NOM characterization and real-time monitoring, aiming to strengthen water distribution system resilience.

The management of water quality in distribution systems is a pervasive challenge. A high degree of uncertainty in water demand, reaction rates, and conditions of the pipe networks results in significant discrepancies between expected and observed water quality. In an effort to enhance the prediction of chlorine residual within water distribution systems (WDS), this study utilized full-scale WDS data to investigate the capabilities of a hydraulic model EPANET-Water Network Tool for Resilience (WNTR) coupled with process-based chlorine residual and data-driven models. Calculation and analysis of observed chlorine decay rates over 19 weeks of recorded data from a full-scale WDS (n = 19 512) demonstrated significant non-linearities and complex relationships with operational parameters and water quality. Linear regression was applied as a baseline method to model the relationship between water quality parameters and chlorine residual, but its limitations in capturing complex, non-linear interactions prompted a transition towards more sophisticated neural network architectures. Furthermore, EPANET-WNTR coupled with a first-order chlorine residual model showed poor performance in predicting chlorine residuals at a downstream node over the full range of flow conditions with high-frequency. Utilizing a windowing technique to account for sequences representing significant travel times in the dataset, the shift to neural networks, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks demonstrated a significantly enhanced ability to incorporate temporal information and predict chlorine residual. The models achieved mean absolute errors as low as 0.022 mg L⁻¹ and R² as high as 0.952 using a 4-layer LSTM. This research illustrates the effectiveness of adopting data-driven approaches that can capture the relationships and dynamics of water quality parameters based on previous data, marking a significant advancement in water quality management within WDS.