Quantitative Prediction of Sediment–Water Partition Coefficients for Tetracycline Antibiotics in a Typical Karst Wetland

The soil–water partition coefficient (Kd) of antibiotics is a critical indicator for assessing their migration potential in the environment. Currently, research on antibiotic Kd values in specific geological settings such as karst wetlands remains relatively limited. This study uniquely integrates partial least squares (PLS) regression with redundancy analysis (RDA), a hybrid approach that effectively handles complex environmental datasets prone to multicollinearity. The results identified Fe3+, NO3−, and PO43− in water, as well as clay content, organic matter, bulk density, and pH in sediments, as key factors influencing Kd through redundancy analysis. Using PLS, predictive models were developed for the logKd of four antibiotics: tetracycline (TC), doxycycline (DOX), chlortetracycline (CTC), and demeclocycline (DMC). The models demonstrated strong predictability with Q2cum values of 0.96, 0.93, 0.99, and 0.83, respectively, indicating excellent model convergence. These findings provide important insights into how soil and water physicochemical properties influence the distribution of antibiotics, support the prediction of antibiotic transport and fate, and contribute to the exposure and risk assessment of these emerging contaminants in aquatic ecosystems.