Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
Water Res. 2011 Jan;45(1):75-92. doi: 10.1016/j.watres.2010.08.019. Epub 2010 Aug 17.
In this paper, we evaluated the ecotoxicological potential of the 100 pharmaceuticals expected to occur in highest quantities in the wastewater of a general hospital and a psychiatric center in Switzerland. We related the toxicity data to predicted concentrations in different wastewater streams to assess the overall risk potential for different scenarios, including conventional biological pretreatment in the hospital and urine source separation. The concentrations in wastewater were estimated with pharmaceutical usage information provided by the hospitals and literature data on human excretion into feces and urine. Environmental concentrations in the effluents of the exposure scenarios were predicted by estimating dilution in sewers and with literature data on elimination during wastewater treatment. Effect assessment was performed using quantitative structure-activity relationships because experimental ecotoxicity data were only available for less than 20% of the 100 pharmaceuticals with expected highest loads. As many pharmaceuticals are acids or bases, a correction for the speciation was implemented in the toxicity prediction model. The lists of Top-100 pharmaceuticals were distinctly different between the two hospital types with only 37 pharmaceuticals overlapping in both datasets. 31 Pharmaceuticals in the general hospital and 42 pharmaceuticals in the psychiatric center had a risk quotient above 0.01 and thus contributed to the mixture risk quotient. However, together they constituted only 14% (hospital) and 30% (psychiatry) of the load of pharmaceuticals. Hence, medical consumption data alone are insufficient predictors of environmental risk. The risk quotients were dominated by amiodarone, ritonavir, clotrimazole, and diclofenac. Only diclofenac is well researched in ecotoxicology, while amiodarone, ritonavir, and clotrimazole have no or very limited experimental fate or toxicity data available. The presented computational analysis thus helps setting priorities for further testing. Separate treatment of hospital wastewater would reduce the pharmaceutical load of wastewater treatment plants, and the risk from the newly identified priority pharmaceuticals. However, because high-risk pharmaceuticals are excreted mainly with feces, urine source separation is not a viable option for reducing the risk potential from hospital wastewater, while a sorption step could be beneficial.
在本文中,我们评估了预计在瑞士一家综合医院和一家精神病中心废水中含量最高的 100 种药物的生态毒理学潜力。我们将毒性数据与不同废水流中的预测浓度相关联,以评估不同情景的总体风险潜力,包括医院常规的生物预处理和尿液源分离。通过医院提供的药物使用信息和人类粪便和尿液排泄的文献数据,估算了废水中的药物浓度。通过估算污水管中的稀释作用,并利用文献中关于废水处理过程中消除的资料,预测了暴露情景下污水中的环境浓度。由于只有不到 20%的预计负荷最高的 100 种药物具有实验性生态毒性数据,因此使用定量结构-活性关系进行了效果评估。由于许多药物是酸或碱,因此在毒性预测模型中实施了对形态的校正。这两种医院类型的前 100 种药物清单明显不同,只有 37 种药物在两个数据集重叠。在综合医院中有 31 种药物和在精神病中心中有 42 种药物的风险商数超过 0.01,因此对混合物风险商数有贡献。然而,它们总共只占药物负荷的 14%(医院)和 30%(精神病学)。因此,仅医疗消费数据不足以预测环境风险。风险商数主要由胺碘酮、利托那韦、克霉唑和双氯芬酸决定。只有双氯芬酸在生态毒理学方面研究得很好,而胺碘酮、利托那韦和克霉唑没有或只有非常有限的实验性命运或毒性数据。因此,本文提出的计算分析有助于确定进一步测试的优先级。对医院废水进行单独处理可以降低废水处理厂的药物负荷,以及新确定的优先药物的风险。然而,由于高风险药物主要随粪便排泄,因此尿液源分离不是降低医院废水风险潜力的可行选择,而吸附步骤可能是有益的。