Two decades of research on antibiotic resistance genes in global wastewater treatment systems: Critical knowledge gaps and pathways toward integrated risk management

Abstract

Wastewater treatment plants serve as both barriers against and gateways for the environmental dissemination of antibiotic resistance genes (ARGs), yet the scientific community's collective research priorities over the past two decades have shaped—and been shaped by—critical knowledge gaps that now constrain effective risk management. Through systematic analysis of 2,894 publications (2004-2024) from the Web of Science Core Collection, this study identifies three fundamental imbalances that undermine progress toward resistance control. First, a source-research misalignment: livestock wastewater, responsible for >50% of global antibiotic consumption, accounts for merely 5.55% of research attention, while municipal wastewater—with lower selective pressure—dominates at 68.98%, leaving the most significant resistance reservoir critically understudied. Second, a mechanism-application disconnect: fundamental research on horizontal gene transfer predominantly utilizes hospital-derived clinical isolates, yet hospital wastewater research constitutes only 25.47% of publications, limiting the translational value of mechanistic insights. Third, a risk-investment mismatch: although "super resistance genes" (mcr-1, blaNDM) with demonstrated clinical impact have emerged as post-2020 research hotspots, targeted control technologies remain in early development, with 78% of treatment studies focusing on conventional processes that are inadequate for high-risk gene elimination. China leads global output (1,184 articles, 40.9%) but exhibits lower average citations (24.1), suggesting a scale-depth trade-off. Temporal analysis reveals three paradigm shifts—from mechanism cognition (2004-2007), through technical standardization (2008-2013), to risk management (2014-2024)—with current emphasis on "pollution," "surveillance," and "generation" signaling integration of resistome research with intelligent early-warning technologies. Addressing these imbalances requires: (1) rebalancing research investment toward livestock-associated resistance through targeted funding mechanisms; (2) developing translational frameworks that connect mechanistic insights (e.g., MGE-mediated transfer) to source-specific intervention design; and (3) prioritizing technology development for high-risk gene elimination (tet, mcr-1, blaNDM) over incremental optimization of conventional processes. This analysis transforms bibliometric patterns into actionable scientific priorities, providing a roadmap for transitioning from reactive monitoring to proactive resistance control.