Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects.

中文翻译：

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使用国家数据库估算颗粒物污染源贡献的贝叶斯多元受体模型

时间序列研究表明，空气污染会对健康产生负面影响。这些研究通常侧重于细颗粒物空气污染的总质量或造成污染的单个化学成分，而不是特定来源的空气污染贡献。从监管角度来看，特定来源的贡献估计是有用的，因为它允许监管机构将有限的资源集中用于减少空气污染的主要来源的排放，并且在估计特定来源的健康影响时也是需要的。然而，研究人员往往缺乏对源级排放的直接观察。我们提出了一个贝叶斯多元受体模型，以从环境空气污染测量中推断有关源贡献的信息。提议的模型结合了来自国家数据库的信息，这些数据库包含有关源排放构成和已知空气污染源排放量的数据。提议的模型用于对美国的两个不同位置（马萨诸塞州波士顿和亚利桑那州凤凰城）执行源分配分析。我们的结果反映了之前的源分配分析，这些分析没有利用来自国家数据库的信息，并提供了与健康影响估计相关的不确定性的额外信息。马萨诸塞州和亚利桑那州凤凰城）。我们的结果反映了之前的源分配分析，这些分析没有利用来自国家数据库的信息，并提供了与健康影响估计相关的不确定性的额外信息。马萨诸塞州和亚利桑那州凤凰城）。我们的结果反映了之前的源分配分析，这些分析没有利用来自国家数据库的信息，并提供了与健康影响估计相关的不确定性的额外信息。