Predicting distribution coefficients for antibiotics in a river water-sediment using quantitative models based on...

Area of Science:

• Environmental Chemistry
• Ecotoxicology
• Water Quality Assessment

Background:

• Antibiotics are prevalent in environmental matrices, raising concerns about their distribution in aquatic ecosystems.
• Understanding the factors influencing antibiotic partitioning between water and sediment is crucial for risk assessment.
• Spatiotemporal variations in antibiotic concentrations highlight the dynamic nature of aquatic pollution.

Purpose of the Study:

• To propose and evaluate a solid/liquid distribution coefficient (Kd(pre)) for predicting antibiotic behavior in water-sediment systems.
• To develop and validate 12 quantitative models for predicting the Kd of 12 different antibiotics.
• To investigate the influence of sediment physicochemical properties on antibiotic distribution.

Main Methods:

• Field data collection from aquatic environments in northwestern Guangzhou.
• Calculation and comparison of a proposed Kd(pre) with a bulk coefficient Kd.
• Development of 12 quantitative predictive models for antibiotic distribution coefficients.
• Analysis of sediment properties including pH, organic matter, and specific surface area.

Main Results:

• The study confirmed the representativeness of the investigated area for antibiotic pollution status.
• Median antibiotic concentrations were <100 ng·L⁻¹ in water and 220 ng·g⁻¹ (d.w.) in sediments.
• Specific site (site 13) showed high concentrations of sulfonamides (SAs) and fluoroquinolones (FQs), linked to population density and low water flow.
• The developed models demonstrated high robustness in spatiotemporally predicting Kd for 12 antibiotics.
• Sediment properties like pH, organic matter, and specific surface area significantly influenced the adsorption of SAs, FQs, tetracyclines (TCs), and macrolides (MLs).

Conclusions:

• Physicochemical properties of sediments are critical determinants of antibiotic distribution in aquatic ecosystems.
• The developed models offer valuable tools for predicting antibiotic fate, transport, and potential exposure risks.
• Findings contribute to a better understanding of emerging pollutant behavior and risk assessment in aquatic environments.