Abstract
Uncertainty estimation plays an important role in source apportionment models such as the positive matrix factorization (PMF) model. In this study, synthetic datasets were generated and analyzed using PMF with specified uncertainties at different levels to investigate the impact of uncertainty inputs on the results of PMF model, as well as the benefits and risks of emphasizing on certain species. The results showed that: (1) uncertainties for the PMF model should be estimated based on characteristics of the dataset being analyzed; (2) emphasizing on correct tracers will improved model performance; and (3) emphasizing on unsuitable tracers may lead to disruptive consequences that might not be captured by the Q metric. Tests were also performed on collected ambient PM_(2.5) samples and similar conclusions were drawn: emphasizing on correct tracers was shown to improve the separation of important source categories from mixed sources. When emphasizing on incorrect tracers, a counterfeit factor of Fe industrial source was extracted, which are inconsistent with field observations. Results from this study provide insights on how uncertainties should be estimated for the PMF model.
Highlights
The results showed that: (1) uncertainties for the Positive Matrix Factorization (PMF) model should be estimated based on characteristics of the dataset being analyzed; (2) emphasizing on correct tracers will improved model performance; and (3) emphasizing on unsuitable tracers may lead to disruptive consequences that might not be captured by the Q metric
Results from this study provide insights on how uncertainties should be estimated for the PMF model
The Positive Matrix Factorization (PMF) model is a receptor-based model recommended by the United States Environmental Protection Agency (US EPA) for the source apportionment of Particulate Matter (PM) in urban areas (Hopke et al, 2003; Zheng et al, 2007; Watson et al, 2008)
Summary
The Positive Matrix Factorization (PMF) model is a receptor-based model recommended by the United States Environmental Protection Agency (US EPA) for the source apportionment of Particulate Matter (PM) in urban areas (Hopke et al, 2003; Zheng et al, 2007; Watson et al, 2008). PMF has been frequently applied throughout the world to quantify the contributions of individual sources to PM (Lee et al, 1999; Tian et al, 2013a; Huang et al, 2014; Alam et al, 2015), and its results have significant implications in policies related PM control and scientific investigations on PM and their impacts on public health (Zheng et al, 2005; Chen et al, 2012; Shen et al, 2012; Holzkamper et al, 2015; Lin et al, 2015; Panicker et al, 2015). Users of the PMF model are expected to provide carefully specified uncertainties to allow for proper estimation of the confidence levels associated with corresponding input variables. A proper understanding of how uncertainties affect model results is critical, for the appropriate use of PMF model, and for subsequent policies implementations (Holzkamper et al, 2015; Panicker et al, 2015)
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