Establishing Chemometric and Statistical Foundations of Receptor Models

受体建模是一个跨学科的研究领域，发现化学 污染源的指纹 受体模型使用化学计量学方法， 化学，统计学，图像分析，以及许多其他领域。 这些模型 常用主成分分析法和因子分析法作为其主要成分。 受体建模需要良好的科学知识来合理地模拟观察到的数据。 例如，了解乙炔是机动车排放物的示踪剂， 成功的受体建模。 本研究开发的方法将用于提供污染源的指纹识别。 该研究也将为该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，是对联邦战略领域的重要贡献。 环境保护 受体模型将作为确定污染源的无偏见的科学证据。接受者模型应该能够在立法和法院诉讼中不受损害。

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.