Evaluation of Uncertainties in PM{sub}2.5 Chemical Speciation Data for use with the Positive Matrix Factorization Model

摘要

Ambient air monitoring data are often used as input for models that are used for decision making about pollution sources, remedial strategies, etc. To interpret and use these data, modelers need to understand the inherent uncertainty in the monitoring data. RTI International has recently started adding uncertainty values to AQS data that it posts as part of its support for the PM{sub}2.5 Speciation Trends Network (STN) analytical services contract with EPA. Uncertainties inherent in the STN data set must be understood in order to apply models such as the Positive Matrix Factorization model, PMF2, and other models used for source attribution and similar purposes. This paper describes how modelers can understand and use the uncertainty information that is now available the STN dataset. This includes a description of how uncertainties are estimated by RTI International in situations where insufficient information is available to calculate them rigorously.