Estimation of the Upper Confidence Limit on the Mean of Datasets with Count-Based Concentration Values

ABSTRACT

Mathematical approaches are not well established for calculating the upper confidence limit (UCL) of the mean of a set of concentration values that have been measured using a count-based analytical approach such as is commonly used for asbestos in air. This is because the uncertainty around the sample mean is determined not only by the authentic between-sample variation (sampling error), but also by random Poisson variation that occurs in the measurement of sample concentrations (measurement error). This report describes a computer-based application, referred to as CB-UCL, that supports the estimation of UCL values for asbestos and other count-based samples sets, with special attention to datasets with relatively small numbers of samples and relatively low counts (including datasets with all-zero count samples). Evaluation of the performance of the application with a range of test datasets indicates the application is useful for deriving UCL estimates for datasets of this type.

Key Words:

• UCL
• asbestos
• CB-UCL

Acknowledgments

This project was sponsored by the U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response. SRC, Inc. is a contractor for USEPA. The authors declare there are no specific potential competing financial interests as a consequence of employment. The opinions expressed in this report are not necessarily those of the U.S. Environmental Protection Agency.

Notes

Editor's note: Superfund sites are uncontrolled hazardous waste sites in the United States that are designated for remediation under the authorities of the Federal Comprehensive Environmental Response, Compensation, and Liability Act, as amended (also called the Superfund Act).