超越数据验证-评估溢油调查数据质量的高级策略

L. Cook, Laurie D. Benton, Melanie Edwards
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引用次数: 0

摘要

为了应对溢油事故,现场抽样调查变得越来越复杂,许多利益相关方多年来收集的分析数据具有不同的调查目的。对于深水地平线(DWH)漏油事件,分析化学数据和毒性研究数据需要按照美国环境保护署(EPA)对超级基金项目方法的数据验证进行验证。根据EPA指南验证数据的过程是一个手动且耗时的过程,重点关注单个数据包内单个样品的化学结果,以评估数据是否符合质量控制标准。事后看来,验证所有化学数据的负担似乎过重,而且有些参数是不必要的,这不仅成本高昂,而且减慢了数据传播的进程。根据数据的使用(例如,评估人类和生态风险、定性石油跟踪或法医指纹),数据验证可能不需要在每种情况下或每种数据类型中都需要。从墨西哥湾研究倡议信息和数据合作数据门户网站获得的与DWH溢油相关的公开水柱、沉积物和石油化学分析数据进行了评估,以了解数据验证过程的影响、努力、准确性和效益。探讨的问题包括:根据数据验证审查,哪些数据发生了更改?这些变化将如何影响相关的数据评估结果?数据验证是否引入了额外的错误?数据验证过程遗漏了哪些数据质量问题?什么样的统计和数据分析方法可以更有效地识别潜在的数据质量问题?根据我们对DWH漏油事件相关的化学数据的评估,我们将提出新的策略来评估与漏油调查相关的数据质量。
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Beyond Data Validation – Advanced Strategies for Assessing Data Quality for Oil Spill Investigations
Field sampling investigations in response to oil spill incidents are growing increasingly more complex with analytical data collected by a variety of interested parties over many years and with different investigative purposes. For the Deepwater Horizon (DWH) Oil Spill, the analytical chemistry data and toxicity study data were required to be validated in accordance with U.S. Environmental Protection Agency's (EPA's) data validation for Superfund program methods. The process of validating data according to EPA guidelines is a manual and time-consuming process focused on chemistry results for individual samples within a single data package to assess if data meet quality control criteria. In hindsight, the burden of validating all of the chemistry data appears to be excessive, and for some parameters unnecessary, which was costly and slowed the process of disseminating data. Depending on the data use (e.g., assessing human and ecological risk, qualitative oil tracking, or forensic fingerprinting), data validation may not be needed in every circumstance or for every data type. Publicly available water column, sediment, and oil chemistry analytical data associated with the DWH Oil Spill, obtained from the Gulf of Mexico Research Initiative Information and Data Cooperative data portal were evaluated to understand the impact, effort, accuracy, and benefit of the data validation process. Questions explored include: What data changed based on data validation reviews?How would these changes affect the associated data evaluation findings?Did data validation introduce additional errors?What data quality issues did the data validation process miss?What statistical and data analytical approaches would more efficiently identify potential data quality issues? Based on our evaluation of the chemical data associated with the DWH Oil Spill, new strategies to assess the quality of data associated with oil spill investigations will be presented.
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