Antibiotic Toxicity Isolated and as Binary Mixture to Freshwater Algae Raphidocelis subcapitata: Growth Inhibition, Prediction Model, and Environmental Risk Assessment

• Published in: Toxics (Basel) Vol. 10; no. 12; p. 739
• Main Authors: Chang, Fang, Yi, Malan, Li, Huiting, Wang, Jiangnan, Zhao, Xuefeng, Hu, Xiaoyue, Qi, Qianju
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.11.2022; MDPI
• Subjects: Algae; Antibiotics; Aqueous environments; Binary mixtures; CA and IA models; Environmental assessment; Environmental quality; Environmental risk; environmental risk assessment; Erythromycin; Evaluation; Experiments; mixture toxicity; Organisms; Pollutants; Prediction models; Raphidocelis subcapitata; Research projects; Risk assessment; Side effects; Sulfamethazine; Sulfamethoxazole; Toxicity; Toxicology; mixture toxicity; algae; CA and IA models; antibiotics; environmental risk assessment
• ISSN: 2305-6304; 2305-6304
• DOI: 10.3390/toxics10120739

Antibiotics in aqueous environments can have extremely adverse effects on non-targeted organisms. However, many research projects have only focused on the toxicological evaluation of individual antibiotics in various environments. In the present work, individual and binary mixture toxicity experiments have been conducted with the model organism Raphidocelis subcapitata (R. subcapitata), and a mixture concentration-response curve was established and contrasted with the estimated effects on the basis of both the concentration addition (CA) and the independent action (IA) models. In addition, different risk assessment methods were used and compared to evaluate the environmental risk of binary mixtures. The toxic ranking of the selected antibiotics to R. subcapitata was erythromycin (ERY) > sulfamethoxazole (SMX) > sulfamethazine (SMZ). In general, the conclusion of this study is that the adverse effects of binary mixtures are higher than the individual antibiotics. The CA model and RQSTU are more suitable for toxicity prediction and risk assessment of binary mixtures. This study reveals the potential ecological risks that antibiotics and their mixtures may pose to water ecosystems, thus providing scientific information for environmental quality regulation.