Fecha de publicación: -- Fuente: PubMed "plant biotechnology" Water Res. 2026 Oct 15;309(Pt A):127149. doi: 10.1016/j.watres.2026.127149. Online ahead of print.ABSTRACTAntibiotic pollution in the rivers can lead to environmental antibiotic resistance (AR) selection, yet selection risks from complex antibiotic mixtures in the rivers remain poorly understood. In this study, we coupled the GloWPa-Antibiotics C1 model, a process-based model simulating emissions and concentrations of 33 antibiotics, with a systematic three-level risk assessment framework to quantify AR selection risk across China's rivers. In 2020, 2303 tonnes of antibiotics were estimated to enter rivers, predominantly from livestock (91.7%), mainly via direct discharge of faecal waste from pigs, broilers, beef cattle and laying hens. At the bacterial community level, fluoroquinolones and sulfonamides exhibited the highest potential AR selection risk; for WHO priority pathogens specifically, fluoroquinolones posed a high potential selection risk. Mixtures of antibiotics amplified this potential selection risk compared with individual compounds. By advancing from deterministic screening to a novel probabilistic strain-based assessment, we estimated that a median of 26.7% of China's riverine Escherichia coli load resides in rivers where fluoroquinolone mixture concentrations exceed their strain-specific minimal selective concentration, mainly concentrated in the most populous and agriculturally intensive basins, Haihe, Huaihe and Yangtze River Basins, where high pathogen density intersects with elevated antibiotic concentrations. Across China's rivers, Escherichia coli in 3.2% of river cells was persistently at risk, while a further 49.1% were conditionally at risk depending on the assessment factors chosen for quantifying AR selection risk. This study is the first to integrate process-based modelling with a systematic risk assessment framework for AR selection, offering critical insights for targeted monitoring and management.PMID:42901127 | DOI:10.1016/j.watres.2026.127149