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Processes 2019, 7(3), 169; https://doi.org/10.3390/pr7030169

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

1
Lushan Binjiang Experimental School, Changsha 410013, China
2
School of Mathematics and Statistics, Central South University, Changsha 410083, China
3
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
4
School of Mathematics and Statistics, and Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Commerce, Changsha 410205, China
*
Author to whom correspondence should be addressed.
Received: 15 February 2019 / Revised: 17 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
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Abstract

The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB. View Full-Text
Keywords: CMB receptor model; effective variance weighted least squares algorithm; enhanced sampling Monte Carlo simulation CMB receptor model; effective variance weighted least squares algorithm; enhanced sampling Monte Carlo simulation
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Hou, W.; Yang, Y.; Wang, Z.; Hou, M.; Wu, Q.; Xie, X. A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation. Processes 2019, 7, 169.

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