Source apportionment and predictable driving factors contribute to antibiotics profiles in Changshou Lake of the Three Gorges Reservoir area, China

Lakes, crucial antibiotic reservoirs, lack thorough exploration of quantitative relationships between antibiotics and influencing factors. Here, we conducted a comprehensive year-long investigation in Changshou Lake within the Three Gorges Reservoir area, China. The concentrations of 21 antibiotics spanned 35.6-200 ng/L, 50.3-348 ng/L and 0.57-57.9 ng/g in surface water, overlying water and sediment, respectively. Compared with abundant water period, surface water and overlying water displayed significantly high antibiotic concentrations in flat and low water periods, while sediment remained unchanged. Moreover, tetracyclines, fluoroquinolones and erythromycin posed notable risks to algae. Six primary sources were identified using positive matrix factorization model, with aquaculture contributing 21.2%, 22.7% and 25.4% in surface water, overlying water and sediment, respectively. The crucial predictors were screened through machine learning, redundancy analysis and Mantel test. Our findings emphasized the pivotal roles of water quality parameters, including water temperature (WT), pH, dissolved oxygen, electrical conductivity, inorganic anions (NO3⁻, Cl⁻ and F⁻) and metal cations (Ca, Mg, Fe, K and Cr), with WT influencing greatest. Total nitrogen (TN), cation exchange capacity, K, Al and Cd significantly impacted sediment antibiotics, with TN having the most pronounced effect. This study can promise valuable insights for environmental planning and policies addressing antibiotic pollution. Antibiotics present a significant global concern owing to their pervasive presence and associated aquatic toxicity. Inspired by lakes as the reservoirs for antibiotics, we presented a comprehensive investigation of antibiotic occurrences in Changshou Lake within the Three Gorges Reservoir area, China, including spatio-temporal variations, influencing factors and their contributions, source apportionment and ecological risks. We have successfully identified six major sources of antibiotics in aquatic ecosystems, and screened key factors contributing to antibiotics in water and sediment through machine learning integration. Our findings can promise valuable insights for environmental planning and policies addressing antibiotic pollution.