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  • 标题:Bias, complexity, and uncertainty in ecosystem risk assessment: pharmaceuticals, a new challenge in scale and perspective
  • 本地全文:下载
  • 作者:Lawrence K Duffy ; Kriya L Dunlap ; Anna R Godduhn
  • 期刊名称:Environmental Research Letters
  • 印刷版ISSN:1748-9326
  • 电子版ISSN:1748-9326
  • 出版年度:2014
  • 卷号:9
  • 期号:9
  • 页码:091004
  • DOI:10.1088/1748-9326/9/9/091004
  • 语种:English
  • 出版社:IOP Publishing Ltd
  • 摘要:Because thousands of pharmaceutical and industrial compounds are in use today and distributed into ecosystems via waste water, effective analysis of environmental risk needs to change as our understanding of the complexity of ecosystem services grows. Klaminder et al (2014 Environ. Res. Lett. 9 084003 ) now provide some important observations on a methodological bias in standardized ecotoxicological tests. First, the authors show that the formalized use of control species in risk assessment impacts how data is judged and valued. Reducing quantitative uncertainty may improve the rigor of toxicological assays, but may also increase the risk of missing likely ecosystem-scale impacts, essentially 'throwing the baby out with the bathwater'. Second, we should recognize the importance of integrating nature, complexity, and dynamics across temporal and spatial scales in relation to the unintended consequences of pharmaceuticals and their partial degradation products in the environment. Since ecosystems and our broader life support system are composed of various stability states with dynamic cycles, feedback can destabilize a system as we know it. Complex systems have emergent properties with high degrees of uncertainty and ecosystem risk assessments must report not only toxicological risks but also 'benefits'. In addition, risk assessment must expand its scale from the molecular and cellular to that of the ecosystem with real world conditions. The authors' findings that exposure to Oxazepam led to increased survival and aggressiveness should inform changes in standardized testing methodology as well as improvements in regulatory policy.
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