Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

“…The water quality model performance was evaluated using statistical metrics such as RMSE, ARD (%), and NSE (Figure S2). The results indicated a good fit with the historical data set, which has been reported in our previous studies, , with NSE values ranging from 0.31 to 0.91 and ARD (%) values below 25, demonstrating that the HWQ model can realistically capture the fluctuations of hydrodynamics water quality within this water body.…”

“…From a spatial distribution point of view, the SMX and TMP levels in the northern part of the study area were higher than those in the rest of the region, especially in the three tributaries in the upper region (Figure a,b). Due to the proximity of the upper region to higher-density residential and commercial areas in the downtown area, these three tributaries are more impacted by human activities. , In addition, lower dilution factors with smaller water inflow volumes in the tributaries could induce higher concentrations of antibiotics . Since SMX and TMP are frequently used in association with SXT for medication purposes, the combination ratio generated a unique index for source tracking .…”

“…82 In addition, a large data set could provide diverse modeling choices, such as applying machine learning algorithms to explore the relationship between antibiotic resistance and the surrounding environment. 19 Beyond the constraints imposed by data set size, establishing sustainable monitoring programs is crucial for providing a valid database essential for water quality model development and achieving expected results. In recent decades, a wealth of field studies on AMR in aquatic environments has emerged.…”

“…18 Hence, it is vital to develop novel methods to investigate the transport and fate of AMR in the aquatic environment to overcome the burdens of expensive sampling campaigns and laboratory analysis. 19 To overcome this limitation, the numerical model can act as a powerful technique to investigate the transport and fate of AMR in aquatic environments 19 and provide a holistic distribution of AMR over time and space. 16 The process-based hydrodynamic water quality (HWQ) model that considers the coupled physical−chemical−biological processes (e.g., advection-diffusion, adsorption/desorption, growth/mortality, and grazing processes) of modeled substances is a reliable method to predict the transport and fate of chemical and microbiological pollutants in aquatic environments 20 and has been widely applied in freshwater and marine ecosystems.…”

“…To overcome this limitation, the numerical model can act as a powerful technique to investigate the transport and fate of AMR in aquatic environments and provide a holistic distribution of AMR over time and space . The process-based hydrodynamic water quality (HWQ) model that considers the coupled physical–chemical–biological processes (e.g., advection-diffusion, adsorption/desorption, growth/mortality, and grazing processes) of modeled substances is a reliable method to predict the transport and fate of chemical and microbiological pollutants in aquatic environments and has been widely applied in freshwater and marine ecosystems . Modeling the fate and transport of AMR (e.g., ARB) in catchments is still at an early stage, and so far, only a few studies have been conducted to develop such a model. , Hellweger et al introduced a mechanistic model to study tetracycline resistance in aquatic environments, incorporating variables such as antibiotics, bacteria, and organic matter and successfully applied it to the Poudre river .…”