State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China.
Shanxi Research Academy of Environmental Science, No. 11, Xinghua Street, Taiyuan, 030027, China.
Chemosphere. 2022 Jan;287(Pt 1):131997. doi: 10.1016/j.chemosphere.2021.131997. Epub 2021 Aug 25.
A comprehensive understanding of the sources and distribution of antibiotic resistance risk is essential for controlling antibiotic pollution and resistance. Based on surface water samples collected from the Fenhe River basin in the flood season, using the positive matrix factorization (PMF) model, the risk quotient (RQ) method and the multiple attribute decision making (MADM) method, the resistance risk and source-specific resistance risk of antibiotics were analyzed in this study. The results showed that sulfonamides (SAs) were the dominant antibiotics with a mean concentration of 118.30 ng/L, whereas tetracyclines (TCs) and macrolides (MLs) had the highest detection frequencies (100%). The significant resistance risk rate of antibiotics in the entire river basin was 48%, but no high risk occurred. The significant resistance risk rate of quinolones (QNs) was the highest (100%), followed by that of MLs and TCs. Owing to human activities, the most serious resistance risk occurred in the midstream of the river basin. The resistance risk was the lowest upstream. The antibiotics were mainly contributed by six sources. Pharmaceutical wastewater was the main source, accounting for 30%, followed by livestock discharge (22%). The resistance risk from the six sources showed clear differences, but none of the sources caused a high risk of antibiotic resistance. Pharmaceutical wastewater poses the greatest risk of antibiotic resistance in the Fenhe River basin and is widely distributed. The second greatest source was livestock discharge, which was mainly concentrated in the upstream and midstream areas. The critical sources upstream, midstream, and downstream were all pharmaceutical wastewater, whereas the sequences of other sources were different because different areas were affected by different human activities. The proposed method might provide an important reference for the identification the key source of antibiotics and management of antibiotic pollution, as well as help for the management of antibiotics in Fenhe and Shanxi Province.
全面了解抗生素耐药性的来源和分布对于控制抗生素污染和耐药性至关重要。本研究基于丰河汛期地表水样本，采用正定矩阵因子（PMF）模型、风险商（RQ）法和多属性决策（MADM）法，分析了抗生素的耐药性风险和源特异性耐药性风险。结果表明，磺胺类（SAs）是主要的抗生素，平均浓度为 118.30ng/L，而四环素（TCs）和大环内酯类（MLs）的检测频率最高（100%）。整个流域抗生素的显著耐药风险率为 48%，但没有高风险。喹诺酮类（QNs）的显著耐药风险率最高（100%），其次是 MLs 和 TCs。由于人类活动，流域中游的耐药风险最为严重，上游的耐药风险最低。抗生素主要来自六个来源。制药废水是主要来源，占 30%，其次是牲畜排放（22%）。六个来源的耐药风险存在明显差异，但均未造成抗生素耐药性的高风险。制药废水是丰河流域抗生素耐药性的最大威胁，分布广泛。第二大来源是牲畜排放，主要集中在上游和中游地区。上游、中游和下游的关键源均为制药废水，而其他源的顺序不同，因为不同地区受到不同人类活动的影响。该方法可为识别抗生素的关键源和管理抗生素污染提供重要参考，也有助于丰河和山西省的抗生素管理。
