The anoxic zone and sludge line as critical reservoirs: Absolute quantification reveals divergent fates of antibiotics, antibiotic resistance genes, and pathogens in a wastewater treatment plant.

Wastewater treatment plants (WWTPs) are pivotal in the dissemination of antibiotics, antibiotic resistance genes (ARGs), and human pathogenic bacteria (HPB); however, their fate across treatment units and the associated microbial metabolic responses remain poorly quantified, and current risk assessments based on relative abundance mask the true occurrence of contaminants due to biomass fluctuations. Here, we applied absolute quantification across the wastewater-sludge continuum of a large-scale WWTP (serving 9 million inhabitants) to unravel the divergent fates of 46 antibiotics, ARGs, and HPB, and to elucidate metabolic responses associated with biogeochemical cycling and xenobiotic biodegradation. The results revealed distinct phase-partitioning behaviors of antibiotics within the WWTP. Sulfonamides and macrolides predominated in the wastewater, whereas quinolones strongly partitioned into the sludge, enriching across gravity thickening, anaerobic digestion (AD), dewatering, and drying processes. The anoxic unit was a key hotspot for sulfonamides, quinolones, and associated ARG accumulation during wastewater treatment. Sludge AD effectively removed sulfonamides (44.42%-100%) and reduced the absolute abundances of sul1 and sul2 by 1.47 and 1.60 log copies/g, respectively. Microbial absolute quantification analysis indicated that, although most potential HPB were significantly reduced during biological wastewater treatment and AD, Legionella-like amoebal pathogen HT99 was distinctly enriched. Functional prediction indicated that xenobiotic biodegradation and metabolism was most abundant in sludge AD, with enriched cytochrome P450 and monooxygenase encoding genes likely facilitated antibiotic co-metabolic pathways. Collectively, this study demonstrates that the anoxic zone and sludge processing are critical reservoirs requiring targeted optimization, and underscores the importance of absolute quantification for accurate antimicrobial resistance risk management.