Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1–15 ng/L for SMX, 0.5–5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and −1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.
Understanding the factors governing irreversible and reversible sorption is essential to predict the influence of sediment on contaminant fate and transport in surface waters. This study used sediment of an urban water body to demonstrate irreversible accumulation and reversible absorption of perfluoroalkyl acids (perfluorooctane sulfonate (PFOS), perfluorononanoic (PFNA) and perfluorodecanoic acid (PFDA)) during two sorption–desorption (S–D) cycles. Absorption and desorption isotherms were consistent with sorption to irreversibly absorbing glassy sediment organic carbon (SOC) and liquid–liquid like partitioning to rubbery SOC. The observed absorption isotherms and the desorption isotherms were adequately fitted linear models. Irreversible absorption showed signs of saturation and the reversible partitioning diminished during the 2nd S–D cycle causing the composite sorption capacity to decrease. Results suggest that under the conditions tested, surface water sediment can act as sink for the studied PFAAs. Rubbery SOC can potentially release or absorb contaminants, thus acting as a concentration buffer. S–D cycling can cause irreversible absorption concentration to increase even when exposure occurs at lower concentrations (i.e. 5μg/L). logKOC values of sediment that underwent S–D cycling overlaps with the range of field date, suggesting that the sediment of surface water may have experienced some degree of S–D cycling.
Predicting the transport and fate of antimicrobial resistance (AMR) in aquatic environments is crucial for managing this pressing environmental issue. We proposed a hybrid modeling framework that couples process-based and data-driven models to predict the spatiotemporal distribution of antibiotics and their related antibiotic resistance genes (ARGs) in Singapore's coastal waters (SCW). In this study, Lincomycin and its related ARGs were selected for analysis. Firstly, this study provides valuable insights into the complex dynamics of ARGs in coastal waters through the application of a meticulously constructed Random Forest (RF) model. This model helps identify key environmental drivers of ARGs, specifically Lincomycin, pH, zinc, DO and temperature, thereby illuminating the factors influencing ARG levels. Subsequently, we applied a process-based model using the Delft 3D suite to simulate the fate and transport of these key environmental drivers. Finally, the outputs from the process-based model were integrated with the RF model to predict ARGs. The modelling framework was calibrated and validated against monthly data collected from 12 sampling points around SCW during 2022-2023. The results revealed that the simulation performance provided 'reasonable prediction' results, with all modeled targets showing an R² above 0.7 and an NSE greater than 0.8. The research presented in this study not only enhances our understanding of the intricate interplay between environmental variables and ARG levels but also has significant implications for environmental and public health management. By emphasizing the importance of specific environmental factors, these models offer a proactive approach to addressing the urgent challenge of antibiotic resistance in coastal ecosystems. This ultimately contributes to the preservation of both the environment and public health.
PPCPs and pesticides have been documented throughout the world over the years, yet relatively little is known about the factors affecting their spatial distribution and temporal change in order to know their potential risk to the ecosystem or human health in the future. In our study, 5 PPCPs and 9 pesticides were selected to study their occurrence, impact variables and potential risk in a drinking water reservoir in Yangtze Estuary and related drinking water treatment plants (DWTPs) in China. The detection results showed the presence of PPCPs and pesticides reflected in a large part of croplands and urban and built-up land in the adjacent basin. The discrepancy of concentration among the different PPCPs and pesticides was mainly decided by their application amount or daily usage. Then, the major factors regulating the occurrence of these contaminants in the surface water were found as the living expenditure attributed to food and medicine based on a correlation analysis. Also, the PPCPs were found to negatively correlate to the effectiveness of sewage management. The detection of the PPCPs and pesticides in DWTPs indicated that, except for atrazine and simazine, the removal percentages were increased significantly in advanced DWTPs. Moreover, risk assessment estimated by a Risk Quotient and Hazard Quotient showed that while caffeine, bisphenol A, estrone and simazine were at a high-risk level in the reservoir water, all of the contaminants detected posed no risk to human health through drinking water. It's possible that atrazine could pose a high risk to the ecosystem while simazine could pose a risk to human health in the future considering the increasing expenditure attributed to food.
Bacteria play a crucial role in driving ecological processes in aquatic ecosystems. Studies have shown that bacteria–cyanobacteria interactions contributed significantly to phytoplankton dynamics. However, information on the contribution of bacterial communities to blooms remains scarce. Here, we tracked changes in the bacterial community during the development of a cyanobacterial bloom in an equatorial estuarine reservoir. Two forms of blooms were observed simultaneously corresponding to the lotic and lentic characteristics of the sampling sites where significant spatial variabilities in physicochemical water quality, cyanobacterial biomass, secondary metabolites, and cyanobacterial/bacterial compositions were detected. Microcystis dominated the upstream sites during peak periods and were succeeded by Synechococcus when the bloom subsided. For the main body of the reservoir, a mixed bloom featuring coccoid and filamentous cyanobacteria (Microcystis, Synechococcus, Planktothricoides, Nodosilinea, Raphidiopsis, and Prochlorothrix) was observed. Concentrations of the picocyanobacteria Synechococcus remained high throughout the study, and their positive correlations with cylindrospermopsin and anatoxin-a suggested that they could produce cyanotoxins, which pose more damaging impacts than previously supposed. Succession of different cyanobacteria (Synechococcus and Microcystis) following changes in nutrient composition and ionic strength was demonstrated. The microbiomes associated with blooms were unique to the dominant cyanobacteria. Generic and specialized bloom biomarkers for the Microcystis and downstream mixed blooms were also identified. Microscillaceae, Chthoniobacteraceae, and Roseomonas were the major heterotrophic bacteria associated with Microcystis bloom, whereas Phycisphaeraceae and Methylacidiphilaceae were the most prominent groups for the Synechococcus bloom. Collectively, bacterial community can be greatly deviated by the geological condition, monsoon season, cyanobacterial density, and dominant cyanobacteria.
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