Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results
University of Helsinki · VA Office of Research and Development · +1 more institution
Abstract
The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin…
Citation impact
- FWCI
- 24.39
- Percentile
- 100%
- References
- 33
Authors
4Topics & keywords
- Environmental science
- Measurement uncertainty
- Statistics
- Data quality
- Las vegas
- Data mining
- Computer science
- Mathematics
- Clean water and sanitation