Guo Tao, Tang Jian-Feng, Du Miao-Rou, Wan Jie-Ting, Wang Xiang-Fen, Geng Chun-Nü
School of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Ningbo 315830, China.
Huan Jing Ke Xue. 2024 Dec 8;45(12):7060-7072. doi: 10.13227/j.hjkx.202312276.
In recent years, due to large-scale production and widespread use, drugs have posed a potential threat to biological and human health and have received increasing attention. The occurrence and distribution of drugs in urban rivers are influenced by various factors, such as urbanization level, population density, regional geographical and climatic characteristics, and drug consumption habits. In October 2022, the Yongjiang River Basin was divided into four sub-basins： upstream, midstream, tributaries, and downstream. A total of 20 surface water and sediment samples were collected to explore the occurrence, spatial distribution, and ecological risks of drugs in water and sediment. Among the 40 target drugs, 22 drugs were detected in water samples, accounting for 55% of the target drugs, with a maximum concentration of 207.25 ng·L, and 12 drugs were detected in sediment, accounting for 30% of the target drug, with a maximum concentration of 153.63 μg·kg. The dominant species in water samples were metabolic drugs. In sediment, the dominant species in the upstream and midstream were quinolone antibiotics, while the dominant species in the tributaries and downstream were cardiovascular drugs. Nine drugs were detected in both water and sediment, with an apparent distribution coefficient value of 5.0-385 029 L·kg. According to the average lg value, the order from highest to lowest was： sertraline hydrochloride > ofloxacin > clarithromycin > D,L-venlafaxine > roxithromycin > sulfamethoxazole > fluoxetine > metoprolol > carbamazepine. Principal component analysis was used to analyze the source of drugs in water and four principal components explained 76.24% of the variation, corresponding to a source of medical wastewater, a mixed source of domestic wastewater and aquaculture wastewater, an untreated source of rural domestic wastewater, and an agricultural non-point source. Principal component analysis coupled multiple linear regression analysis well predicted the total concentration of drugs in 20 water samples （ = 0.868）. Three drugs were present in the water sample that posed ecological risks, with risk quotients in the order of 1,7-dimethylxanthine > dehydrated erythromycin > carbamazepine, whereas only carbamazepine in sediment posed ecological risks. The cumulative risk quotient of multiple drugs in the water at 20 sampling sites was 1.22-16.27, all of which belonged to high risk, while the cumulative risk quotient of multiple drugs in sediment was 0.009~0.064, with no risk at site S11 and low risk at all other sites. The research results can be used to analyze the distribution mechanism of drugs between water and sediment, providing a technical basis for the prevention and control of drug pollution and environmental risks in urban rivers.
近年来，由于大规模生产和广泛使用，药物对生物和人类健康构成了潜在威胁，并受到越来越多的关注。城市河流中药物的存在和分布受多种因素影响，如城市化水平、人口密度、区域地理和气候特征以及药物消费习惯。2022年10月，邕江流域被划分为四个子流域：上游、中游、支流和下游。共采集了20个地表水和沉积物样本，以探究水中和沉积物中药物的存在情况、空间分布及生态风险。在40种目标药物中，水样中检测出22种药物，占目标药物的55%，最高浓度为207.25 ng·L，沉积物中检测出12种药物，占目标药物的30%，最高浓度为153.63 μg·kg。水样中的优势物种为代谢类药物。在沉积物中，上游和中游的优势物种为喹诺酮类抗生素，而支流和下游的优势物种为心血管类药物。水中和沉积物中均检测出9种药物，表观分配系数值为5.0 - 385029 L·kg。根据平均lg值，从高到低的顺序为：盐酸舍曲林＞氧氟沙星＞克拉霉素＞D,L - 文拉法辛＞罗红霉素＞磺胺甲恶唑＞氟西汀＞美托洛尔＞卡马西平。采用主成分分析来分析水中药物的来源，四个主成分解释了76.24%的变异，分别对应医疗废水源、生活污水和养殖废水混合源、农村生活污水未经处理源以及农业面源。主成分分析结合多元线性回归分析对20个水样中药物的总浓度预测效果良好（ = 0.868）。水样中有三种药物存在生态风险，风险商数顺序为1,7 - 二甲基黄嘌呤＞脱水红霉素＞卡马西平，而沉积物中只有卡马西平存在生态风险。20个采样点水中多种药物的累积风险商数为1.22 - 16.27，均属于高风险，而沉积物中多种药物的累积风险商数为0.009~0.064，S11站点无风险，其他站点均为低风险。研究结果可用于分析药物在水和沉积物之间的分配机制，为城市河流药物污染和环境风险的防控提供技术依据。
