The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011–2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQI_RF model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQI_RF and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies.

In the present study, the effect of pipe materials on water quality as well as the microbial community was researched with static devices as well as dynamic ones. Five kinds of pipe materials (SP: steel plastic composite pipe, SS: stainless steel pipe, DI: ductile iron pipe, CI: cast iron pipe, GS: galvanized steel pipe) were chosen, and the soaking experiment was carried out with bench-scale devices. To further investigate the performance of pipe materials over a long term, a pilot-scale simulated drinking water distribution system was constructed, and the water quality parameters were monitored for six months. The pipe materials were ranked as SP, DI, and CI by the order of increasing turbidity, COD_Mn, and NH₃-N. Furthermore, the biofilm samples were analyzed via pyrosequencing and COG functional categories. The DI biofilm possessed the highest bacterial diversity with a Shannon index of 3.56, followed by SP (3.14) and CI (0.77). The presence of nitrate-reducing bacteria (NRB), iron-oxidizing bacteria (IOB), iron-reducing bacteria (IRB), and sulfate-reducing bacteria (SRB)was identified, and NRB composed the largest share in all pipe materials (13.0%–17.2%), with other redox bacteria making up a minor proportion (0.02%–1.52%). NRB and IRB inhibited the corrosion process while IOB and SRB enhanced it. Most dominant genera present in samples were derived firstly from soil or active sludge, indicating a turbidity problem due to soil contamination in the distribution network.

Understanding the spatiotemporal dynamics of water quality parameters and microbial communities in drinking water distribution systems (DWDS) and their interrelationships is critical for ensuring the safety of tap water supply. This study investigated the diurnal, monthly, and annual variation patterns of water quality and the stage-specific succession behaviors of microbial communities in a DWDS located in southeastern China. Results indicated that hydraulic shear stress during peak usage periods drove biofilm detachment and particle resuspension. This process led to significant diurnal fluctuations in total cell counts (TCC) and metal ions, with coefficients of variation ranging from 0.44 to 1.89. Monthly analyses revealed the synergistic risks of disinfection by-products (e.g., 24.5 μg/L of trichloromethane) under conditions of low chlorine residual (<0.2 mg/L) and high organic loading. Annual trends suggested seasonal coupling: winter pH reductions correlated with organic acid accumulation, while summer microbial blooms associated with chlorine decay and temperature increase. Nonlinear interactions indicated weakened metal–organic complexation but enhanced turbidity–sulfate adsorption, suggesting altered contaminant mobility in pipe scales. Microbial analysis demonstrated persistent dominance of oligotrophic Phreatobacter and prevalence of Pseudomonas in biofilms, highlighting hydrodynamic conditions, nutrient availability, and disinfection pressure as key drivers of community succession. These findings reveal DWDS complexity and inform targeted operational and microbial risk control strategies.

The widespread use of antibiotics has led to the persistence of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in drinking water systems, posing potential public health risks at the point of use. In this study, a residential secondary water supply system (SWSS) in eastern China was investigated over one year to characterize microbial communities, ARB and ARG occurrence, and their associations with water quality in bulk water and biofilms. Culture-based methods, flow cytometry, quantitative PCR, and high-throughput 16S rRNA and ITS sequencing were applied. Although conventional treatment removed 94.8% of total bacteria, significant microbial regrowth occurred during secondary distribution, with the highest heterotrophic plate counts observed in rooftop storage tanks (up to 4718 CFU/mL). ARG concentrations increased along the distribution line, and the class 1 integron intI1 was enriched in downstream locations, indicating enhanced horizontal gene transfer potential. Sulfonamide resistance genes dominated the resistome, accounting for more than 60% of total ARG abundance in water samples. Seasonally, ARG levels were higher in autumn and winter, coinciding with elevated disinfectant residuals and lower temperatures. Chlorine was negatively associated with total bacterial abundance, while positive correlations were observed with the relative abundance of several ARGs when normalized to bacterial biomass, suggesting selective pressure under oxidative stress. Turbidity and bacterial abundance were positively correlated with ARB, particularly sulfonamide-resistant bacteria. Biofilms exhibited more stable microbial communities and provided microhabitats that facilitated microbial persistence. Notably, fungal abundance showed strong positive correlations with multiple ARGs, implying that microbial interactions may indirectly contribute to ARG persistence in SWSSs. These findings highlight the role of secondary distribution conditions, disinfectant pressure, and microbial interactions in shaping resistance risks in residential water supply systems, and provide insights for improving microbial risk management at the point of consumption.