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ARE EXPOSURE PREDICTIONS, USED FOR THE PRIORITIZATION OF PHARMACEUTICALS IN THE ENVIRONMENT, FIT FOR PURPOSE?

机译:适用于环境中药品优先级的曝光预测是否适合目的?

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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. (C) 2017 SETAC
机译:优先排序方法通常用于识别对自然环境构成最大风险的药物,并将实验室测试或环境监测重点放在最受关注的药物上。通常使用基于风险的优先级排序方法,该模型采用模型来得出暴露浓度,但是这些模型的可靠性尚不清楚。本研究评估了通常用于药物优先排序的暴露模型的准确性。在约克市(英国)的Rivers Foss和Ouse河中对95种药品进行了有针对性的监测。预测的环境浓度(PEC)范围是根据本地处方,水文数据,报告的代谢和废水处理厂(WWTP)去除率估算的,并与测得的环境浓度(MEC)进行比较。对于River Foss,使用最高代谢和最低WWTP去除率获得的PEC与MEC相似。相反,在Ouse河中没有观察到这种趋势,这可能是由于我们的建模无法解释的药物投入。根据MEC或PEC按风险对药品进行排名。除2个例外(右美沙芬和苯海拉明)外,基于MEC和PEC的风险等级在福斯河产生了相似的结果。总体而言,这些发现表明,当可获得有关目标系统的可靠且本地的数据并反映大多数来源输入时,PEC可能非常适合环境中的药物优先排序。 (C)2017年SETAC

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