...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >A Bayesian Approach to Incorporating Spatiotemporal Variation and Uncertainty Limits into Modeling of Predicted Environmental Concentrations from Chemical Monitoring Campaigns
【24h】

A Bayesian Approach to Incorporating Spatiotemporal Variation and Uncertainty Limits into Modeling of Predicted Environmental Concentrations from Chemical Monitoring Campaigns

机译：一种贝叶斯方法，将不确定的变化和不确定性限制在化学监测活动中预测环境浓度的建模中

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Environmental monitoring studies provide key information to assess ecosystem health. Results of chemical monitoring campaigns can be used to identify the exposure scenarios of regulatory concern. In environmental risk assessment (ERA), measured concentrations of chemicals can be used to model predicted environmental concentrations (PECs). As the PEC is, by definition, a predicted variable, it is highly dependent on the underlying modeling approach from which it is derived. We demonstrate the use of Bayesian distributional regression models to derive PECs by incorporating spatiotemporal conditional variances, and limits of quantification (LOQ) and detection (LOD) as de facto data censoring. Model accuracies increase when incorporating spatiotemporal conditional variances, and the inclusion of LOQ and LOD results in potentially more robust PEC distributions. The methodology is flexible, credibly quantifies uncertainty, and can be adjusted to different scientific and regulatory needs. Posterior sampling allows to express PECs as distributions, which makes this modeling procedure directly compatible with other Bayesian ERA approaches. We recommend the use of Bayesian modeling approaches with chemical monitoring data to make realistic and robust PEC estimations and encourage the scientific debate about the benefits and challenges of Bayesian methodologies in the context of ERA.
机译：环境监测研究提供评估生态系统健康的关键信息。化学监测活动的结果可用于识别监管问题的曝光情景。在环境风险评估（时代）中，测量的化学品浓度可用于模拟预测的环境浓度（PEC）。由于PEC是根据定义预测变量，它高度依赖于它导出的底层建模方法。我们展示了使用贝叶斯分布回归模型来通过掺入时空条件方差来衍生PEC，以及定量限制（LOQ）和检测（LOD）作为事实上的数据审查。在包含时空条件方差时，模型准确性增加，并将LOQ和LOD包含在可能更强大的PEC分布中。该方法是灵活的，可靠地量化不确定性，可以调整到不同的科学和监管需求。后部采样允许将PECS表达为分布，这使得该建模程序与其他贝叶斯时代方法直接兼容。我们建议使用贝叶斯建模方法与化学监测数据，以制定现实和强大的PEC估计，并鼓励科学辩论在时代的背景下贝叶斯方法的益处和挑战。

著录项

  • 来源
    《Environmental Science & Technology》 |2021年第3期|1699-1709|共11页
  • 作者

    Raoul Wolf; Knut Erik Tollefsen;

  • 作者单位

    Norwegian Institute for Water Research (NIVA) 0349 Oslo Norway;

    Norwegian Institute for Water Research (NIVA) 0349 Oslo Norway Norwegian University of Life Science (NMBU) 1430 As Norway Centre for Environmental Radioactivity (CERAD CoE) 1432 As Norway;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bayesian methodologies; chemical monitoring; environmental risk assessment; LOD; LOQ;

    机译：贝叶斯方法;化学监测;环境风险评估;lod;LOQ.;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号