The contamination of rivers with potentially pathogenic bacteria poses health risks for users. This is particularly true when the bacteria are carriers of antibiotic resistance genes (ARG). Pathogenic bacteria and ARG are primarily discharged into rivers from wastewater treatment plants (WWTP). There, ARG are subject to the processes of transport, retention, and degradation. Simultaneously, they can also propagate through the growth of the carrier bacteria and by horizontal gene transfer. According to the current state of knowledge, the horizontal transfer of ARG is mediated predominantly by plasmid transfer. While the transport of bacteria in rivers has been intensively investigated, the relationship between the location of the wastewater discharges and their impact on microbial (and ARG) loading in the receiving waters downstream remains largely unexplored. Process-based mathematical models have been designed in the past to specifically describe the plasmid-mediated transfer of ARG. However, these models are used with numerous pragmatic simplifications whose effects on the computational outcomes have not been systematically examined. The present work uses virtual experiments (VE) to answer crucial questions regarding the spread of ARG in rivers. On the one hand, VE are used to compare alternative configurations of WWTP (in terms of size and location) within a catchment with respect to the resulting microbial contamination in the river network. On the other hand, VE are used to quantify the biases that arise in the estimation of plasmid transfer rates from laboratory experiments when the mathematical models used for this purpose have structural deficiencies. The rates of plasmid transfer determined from laboratory experiments provide an initial basis for assessing the potential importance of the horizontal transfer of ARG occurring in the water column along rivers. The knowledge gained makes an important contribution to describing the spread of ARG in rivers in the future through mathematical models and to identifying possible mitigation measures.

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