Establishing Chemometric and Statistical Foundations of Receptor Models

建立受体模型的化学计量学和统计基础

• 批准号: 9523878
• 负责人: Clifford Spiegelman
• 金额: $ 14万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-05-01 至 2000-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9523878&HistoricalAwards=false
• 关键词: Establishing; Chemometric; Statistical; Foundations; Receptor

Receptor modeling is a cross-disciplinary research area that finds chemical fingerprints of pollution sources. Receptor models use methods from chemometrics, chemistry, statistics, and image analysis, as well as many other fields. These models commonly use principal component analysis, and factor methods as their main ingredients. Receptor modeling requires good scientific knowledge to reasonably model observed data. For example, knowing that acetylene is a tracer for motor vehicle emissions is essential to successful receptor modeling. The methods that this research develops will be used to provide fingerprint identification of pollution sources. The research will also provide a firm mathematical basis for the methods. This research provides a firm chemometric and statistical basis for receptor modeling and is an important contribution to the Federal Strategic Area of the Environment. Receptor models will serve as unbiased scientific evidence for identifying pollution sources. Receptor models should be able to stand untarnished in legislative and court proceedings.

受体建模是一个跨学科的研究领域，它寻找污染源的化学指纹。受体模型使用化学计量学、化学、统计学、图像分析以及许多其他领域的方法。这些模型通常使用主成分分析和因子法作为其主要成分。受体建模需要良好的科学知识才能合理地对观测数据进行建模。例如，了解乙炔是机动车尾气的示踪剂对于成功的受体建模至关重要。这项研究开发的方法将用于提供污染源的指纹识别。这项研究也将为这些方法提供坚实的数学基础。这项研究为受体建模提供了坚实的化学计量学和统计学基础，是对联邦环境战略领域的重要贡献。受体模型将作为识别污染源的公正的科学证据。受体模型应该能够在立法和法庭程序中不受玷污。