A quantitative risk ranking model to evaluate emerging organic contaminants in biosolid amended land and potential transport to drinking water
A quantitative risk ranking model to evaluate emerging organic contaminants in biosolid amended land and potential transport to drinking water
复制标题
DOI：
10.1080/10807039.2015.1121376
复制
发表时间：
2016-01-01
影响因子：
4.3
通讯作者：
Cummins, Enda
中科院分区：
文献类型：
--
作者：
Clarke, Rachel;Healy, Mark G.;Cummins, Enda
在将处理后的城市污水污泥（生物固体）应用于爱尔兰农业用地后，针对人类接触新兴污染物（EC）的情况开发了定量风险排名模型。该模型包含土壤、地表径流、地下水以及人类随后摄入的饮用水中的预测环境浓度 (PEC)。使用蒙特卡罗模拟方法估算了 16 种有机污染物的人体暴露和随后的风险。壬基酚在土壤（PECsoil）、径流（PECrunoff）和地下水（PECgroundwater）三个环境分区中的浓度最高，其平均值分别为5.69mg/kg、1.15x 10(-2) mu g/l和2.22x 10(-1) mu g/l。使用 LC50（化学品摄入毒性比，(RR)）作为毒性终点并结合 PECrunoff 和 PECgroundwater 来估计人类健康风险。 NP 与 PEC 径流和 PEC 地下水组合的 LC50 排名最高（平均 RR 值分别为 1.10x 10(-4) 和 2.40x 10(-3)）。该模型强调三氯卡班和三氯生是需要进一步研究的 EC。敏感性分析显示，土壤吸附系数和土壤有机碳是影响模型方差的最重要参数（相关系数分别为-0.89和-0.30），凸显了污染物和土壤性质在影响风险评估中的重要性。该模型可以帮助优先考虑环境分区中需要警惕的新出现的污染物。
A quantitative risk ranking model was developed for human exposure to emerging contaminants (EC) following treated municipal sewage sludge (biosolids) application to Irish agricultural land. The model encompasses the predicted environmental concentration (PEC) in soil, surface runoff, groundwater, and subsequent drinking water ingestion by humans. Human exposure and subsequent risk was estimated for 16 organic contaminants using a Monte Carlo simulation approach. Nonylphenols ranked the highest across three environmental compartments: concentration in soil (PECsoil), runoff (PECrunoff), and groundwater (PECgroundwater), which had mean values of 5.69mg/kg, 1.15x 10(-2) mu g/l, and 2.22x 10(-1) mu g/l, respectively. Human health risk was estimated using the LC50 (chemical intake toxicity ratio, (RR)) as a toxicity endpoint combined with PECrunoff and PECgroundwater. NP ranked highest for LC50 combined with PECrunoff and PECgroundwater (mean RR values 1.10x 10(-4) and 2.40x 10(-3), respectively). The model highlighted triclocarban and triclosan as ECs requiring further investigation. A sensitivity analysis revealed that soil sorption coefficient and soil organic carbon were the most important parameters that affected model variance (correlation coefficient -0.89 and -0.30, respectively), highlighting the significance of contaminant and soil properties in influencing risk assessments. This model can help to prioritize emerging contaminants of concern requiring vigilance in environmental compartments.