Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers

Serving as a vital linkage between surface water and groundwater, the hyporheic zone (HZ) plays a fundamental role in improving water quality and maintaining ecological security. In arid or semi-arid areas, effluent discharge from wastewater treatment facilities could occupy a predominant proportion of the total base flow of receiving rivers. Nonetheless the relationship between microbial activity, abundance and environmental factors in the HZ of effluent-receiving rivers appear to be rarely addressed. In this study, a spatiotemporal field study was performed in two representative effluent-dominated receiving rivers in Xi'an, China. Land use data, physical and chemical water quality parameters of surface and subsurface water were used as predictive variables, while the microbial respiratory electron transport system activity (ETSA), the Chao1 and Shannon index of total microbial community, as well as the Chao1 and Shannon index of denitrifying bacteria community were used as response variables, while ETSA was used as response variables indicating ecological processes and Shannon and Chao1 were utilized as parameters indicating microbial diversity. Two machine learning models were utilized to provide evidence-based information on how environmental factors interact and drive microbial activity and abundance in the HZ at variable depths. The models with Chao1 and Shannon as response variables exhibited excellent predictive performances (R2: 0.754-0.81 and 0.783-0.839). Dissolved organic nitrogen (DON) was the most important factor affecting the microbial functions, and an obvious threshold value of ∼2 mg/L was observed. Credible predictions of models with Chao1 and Shannon index of denitrifying bacteria community as response variables were detected (R2: 0.484-0.624 and 0.567-0.638), with soluble reactive phosphorus (SRP) being the key influencing factor. Fe (Ⅱ) was favorable in predicting denitrifying bacteria community. The ESTA model highlighted the importance of total nitrogen in the ecological health monitoring in HZ. These findings provide novel insights in predicting microbial activity and abundance in highly-impacted areas such as the HZ of effluent-dominated receiving rivers.

Effects of wastewater treatment plant effluent on microbial risks of pathogens and their antibiotic resistance in the receiving river

The increase in effluent discharge from wastewater treatment plants (WWTPs) into urban rivers has raised concerns about the potential effects on pathogen risks. This study utilized metagenomic sequencing combined with flow cytometry to analyze pathogen concentrations and antibiotic resistance in a typical effluent-receiving river. Quantitative microbial risk assessment (QMRA) was employed to assess the microbial risks of pathogens. The results indicated obvious spatial-temporal differences (i.e., summer vs. winter and effluent vs. river) in microbial composition. Microcystis emerged as a crucial species contributing to these variations. Pathogen concentrations were found to be higher in the river than in the effluent, with the winter exhibiting higher concentrations compared to the summer. The effluent discharge slightly increased the pathogen concentrations in the river in summer but dramatically reduced them in winter. The combined effects of cyanobacterial bloom and high temperature were considered key factors suppressing pathogen concentrations in summer. Moreover, the prevalence of antibiotic resistance of pathogens in the river was inferior to that in the effluent, with higher levels in winter than in summer. Three high-concentration pathogens (Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were selected for QMRA. The results showed that the risks of pathogens exceeded the recommended threshold value. Escherichia coli posed the highest risks. And the fishing scenario posed significantly higher risks than the walking scenario. Importantly, the effluent discharge helped reduce the microbial risks in the receiving river in winter. The study contributes to the management and decision-making regarding microbial risks in the effluent-receiving river.

Chemical fingerprinting of organic micropollutants in different industrial treated wastewater effluents and their effluent-receiving river

Industry wastewater is considered one of the worst polluters of our precious water ecologies. However, the types of pollutants present in wastewater from industrial wastewater treatment plants (IWTPs) are still unclear. In this study, a simple and effective chemical fingerprinting method for checking the source-sink relationships among different industrial wastewaters and their effluent-receiving river was established. 107, 228, 155, and 337 chemicals were screened out in wastewater from electronics, steel, textile, and printing and dyeing plants, respectively. Chemical fingerprinting of the detected chemicals was performed, and results showed that aromatic compounds were the most prevalent among the pollutant categories (i.e., 56, 189, and 168 in electronics, iron and steel, and printing and dyeing plants, respectively). The traceability analysis of the chemicals selected in the effluent determined the characteristic pollutants of different industrial enterprises. Sixty-eight compounds were identified as the characteristic pollutants in the different process stages of wastewater of the four IWTPs. Of the 84 effluent-receiving river water signature pollutants, 47.6% (n = 40) were also detected in the effluent from the four IWTPs. Effective screening of organic pollutants in industrial wastewater and determining their sources will help accelerate the improvement of industrial wastewater treatment technology.