Effect of Uncertainty on Source Contributions from the Positive Matrix Factorization Model for a Source Apportionment Study

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.

Characteristics of fine particulate matter and its sources in an industrialized coastal city, Ningbo, Yangtze River Delta, China

“…Positive Matrix Factorization (PMF) model, an effective receptor modeling tool, has been worldwide applied for source apportionment in the field of environmental research and administration (Brown et al, 2007;Shi et al, 2016). The description of PMF method has been introduced in details in the supplementary materials.…”

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

“…However, CMB-GC does not incorporate the uncertainty of the source profiles and the receptor data. Uncertainty estimates are key parameters in SA, and practice has shown that uncertainty substantially impacts SA results (Belis et al, 2015a(Belis et al, , 2015bShi et al, 2016b). In the field of environmental science, uncertainty can represent the physical reality, and various types of information can be communicated to the model (Paatero and Tapper, 1994;Cheng and Sandu, 2009).…”