Understanding point-of-use tap water quality: From instrument measurement to intelligent analysis using sample filtration

Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?

River water quality has been significantly impacted by climate change and extreme weather events worldwide. Despite increasing studies on deep learning techniques for river water quality management, understanding which riverine water quality parameters can be well predicted by meteorologically-driven deep learning still requires further investigation. Here we explored the prediction performance of a traditional Recurrent Neural Network, a Long Short-Term Memory network (LSTM), and a Gated Recurrent Unit (GRU) using meteorological conditions as inputs in the Dahei River basin. We found that deep learning models (i.e., LSTM and GRU) demonstrated remarkable effectiveness in predicting multiple water quality parameters at daily scale, including water temperature, dissolved oxygen, electrical conductivity, chemical oxygen demand, ammonia nitrogen, total phosphorous, and total nitrogen, but not turbidity. The GRU model performed best with an average determination coefficient of 0.94. Compared to the daily-average prediction, the GRU model exhibited limited error increment of 10-40 % for most water quality parameters when predicting daily extreme values (i.e., the maximum and minimum). Moreover, deep learning showed superior performance in collective prediction for multiple water quality parameters than individual ones, enabling a more comprehensive understanding of the river water quality dynamics from meteorological data. This study holds the promise of applying meteorologically-driven deep learning techniques for water quality prediction to a broader range of watersheds, particularly in chemically ungauged areas.

Drinking Water Microbiota, Entero-Mammary Pathways, and Breast Cancer: Focus on Nontuberculous Mycobacteria

The prospect of drinking water serving as a conduit for gut bacteria, artificially selected by disinfection strategies and a lack of monitoring at the point of use, is concerning. Certain opportunistic pathogens, notably some nontuberculous mycobacteria (NTM), often exceed coliform bacteria levels in drinking water, posing safety risks. NTM and other microbiota resist chlorination and thrive in plumbing systems. When inhaled, opportunistic NTM can infect the lungs of immunocompromised or chronically ill patients and the elderly, primarily postmenopausal women. When ingested with drinking water, NTM often survive stomach acidity, reach the intestines, and migrate to other organs using immune cells as vehicles, potentially colonizing tumor tissue, including in breast cancer. The link between the microbiome and cancer is not new, yet the recognition of intratumoral microbiomes is a recent development. Breast cancer risk rises with age, and NTM infections have emerged as a concern among breast cancer patients. In addition to studies hinting at a potential association between chronic NTM infections and lung cancer, NTM have also been detected in breast tumors at levels higher than normal adjacent tissue. Evaluating the risks of continued ingestion of contaminated drinking water is paramount, especially given the ability of various bacteria to migrate from the gut to breast tissue via entero-mammary pathways. This underscores a pressing need to revise water safety monitoring guidelines and delve into hormonal factors, including addressing the disproportionate impact of NTM infections and breast cancer on women and examining the potential health risks posed by the cryptic and unchecked microbiota from drinking water.

Less pressure contributes to gravity-driven membrane ultrafiltration with greater performance: Enhanced driving efficiency and reduced disinfection by-products formation potential

Gravity-driven membrane (GDM) systems have been well developed previously; however, impacts of driving (i.e., transmembrane) pressure on their performance received little attention, which may influence GDM performance. In this study, we evaluated 4 GDM systems via altering the transmembrane pressure from 50 mbar to 150 mbar with 2 groups, treating surface water in Beijing, China. Results showed that less driving pressure was more favorable. Specifically, compared to groups (150 mbar), groups under a pressure of 50 mbar were found to have greater normalized permeability and lower total resistance. During the whole operation period, the quality of effluents was gradually improved. For example, the removal efficiency of UV254 was significantly improved; particularly, under low driving pressure, the removal efficiency of UV254 in PES GDM system increased by 11.91%, as compared to the corresponding system under high driving pressure. This observation was consistent with the reduction on disinfection by-products (DBPs) formation potential; groups under 50 mbar achieved better DBPs potential control, indicating the advantages of lower driving pressure. Biofilms were analyzed and responsible for these differences, and distinct distributions of bacteria communities of two GDM systems under 50 and 150 mbar may be responsible for various humic-like substances removal efficiency. Overall, GDM systems under less pressure should be considered and expected to provide suggestions on the design of GDM systems in real applications.

A workflow for the detection of antibiotic residues, measurement of water chemistry and preservation of hospital sink drain samples for metagenomic sequencing

BACKGROUND

Hospital sinks are environmental reservoirs that harbour healthcare-associated (HCA) pathogens. Selective pressures in sink environments, such as antibiotic residues, nutrient waste and hardness ions, may promote antibiotic resistance gene (ARG) exchange between bacteria. However, cheap and accurate sampling methods to characterize these factors are lacking.

AIMS

To validate a workflow to detect antibiotic residues and evaluate water chemistry using dipsticks. Secondarily, to validate boric acid to preserve the taxonomic and ARG ('resistome') composition of sink trap samples for metagenomic sequencing.

METHODS

Antibiotic residue dipsticks were validated against serial dilutions of ampicillin, doxycycline, sulfamethoxazole and ciprofloxacin, and water chemistry dipsticks against serial dilutions of chemical calibration standards. Sink trap aspirates were used for a 'real-world' pilot evaluation of dipsticks. To assess boric acid as a preservative of microbial diversity, the impact of incubation with and without boric acid at ∼22 °C on metagenomic sequencing outputs was evaluated at Day 2 and Day 5 compared with baseline (Day 0).

FINDINGS

The limits of detection for each antibiotic were: 3 μg/L (ampicillin), 10 μg/L (doxycycline), 20 μg/L (sulfamethoxazole) and 8 μg/L (ciprofloxacin). The best performing water chemistry dipstick correctly characterized 34/40 (85%) standards in a concentration-dependent manner. One trap sample tested positive for the presence of tetracyclines and sulphonamides. Taxonomic and resistome composition were largely maintained after storage with boric acid at ∼22 °C for up to five days.

CONCLUSIONS

Dipsticks can be used to detect antibiotic residues and characterize water chemistry in sink trap samples. Boric acid was an effective preservative of trap sample composition, representing a low-cost alternative to cold-chain transport.

Biofilm on the pipeline wall is an important transmission route of resistome in drinking water distribution system

Due to the intensive use of antibiotics, the drinking water distribution system (DWDS) has become one of the hotspots of antibiotic resistance. However, little is known about the role of biofilm in the aspect of spreading resistance in DWDS. In present study, four lab-scale biological annular reactors (BAR) were constructed to investigate the transmission of ARGs exposed to a certain amount of antibiotic (sulfamethoxazole) synergistic disinfectants. It was emphasized that pipe wall biofilm was an important way for ARGs to propagate in the pipeline, and the results were quantified by constructing an operational taxonomic unit (OTU) network map. The network analysis results showed the biofilm contribution to waterborne bacteria was finally estimated to be 51.45% and 34.27% in polyethylen (PE) pipe and ductile iron (DI) pipe, respectively. The proportion of vertical gene transfer (VGT) in biofilm was higher than that in water, and the occurrence of this situation had little relationship with the selection of pipe type. Overall, this study revealed how biofilm promoted the transmission of resistome in bulk water, which can provide insights into assessing biofilm-associated risks and optimizing pipe material selection for biofilm control in DWDS.