Burns Emily E, Thomas-Oates Jane, Kolpin Dana W, Furlong Edward T, Boxall Alistair B A
Chemistry Department, University of York, York, UK.
US Geological Survey, Iowa City, Iowa, USA.
Environ Toxicol Chem. 2017 Oct;36(10):2823-2832. doi: 10.1002/etc.3842. Epub 2017 Jun 2.
Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk-based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs. Environ Toxicol Chem 2017;36:2823-2832. © 2017 SETAC.
优先级排序方法通常用于识别那些对自然环境构成最大风险的药物,并将实验室检测或环境监测集中于最受关注的药物。基于风险的优先级排序方法,即采用模型来推导暴露浓度,是常用的方法,但这些模型的可靠性尚不清楚。本研究评估了常用于药物优先级排序的暴露模型的准确性。对英国约克市福斯河和乌斯河中的95种药物进行了针对性监测。根据当地处方、水文数据、报告的代谢情况和污水处理厂(WWTP)的去除率估算了预测环境浓度(PEC)范围,并与实测环境浓度(MEC)进行了比较。对于福斯河,使用最高代谢率和最低污水处理厂去除率获得的PEC与MEC相似。相比之下,在乌斯河未观察到这种趋势,可能是因为我们的模型未考虑到的药物输入。根据MEC或PEC对药物进行风险排序。除了2个例外(右美沙芬和苯海拉明),在福斯河中基于MEC和PEC的风险排序产生了相似的结果。总体而言,这些发现表明,当有关于感兴趣系统的可靠且本地的数据,并且这些数据能反映大多数源输入时,PEC很可能适用于对环境中的药物进行优先级排序。《环境毒理学与化学》2017年;36:2823 - 2832。© 2017 SETAC。