Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies |
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| Authors: | R. Tauler M. Viana X. Querol A. Alastuey R.M. Flight P.D. Wentzell P.K. Hopke |
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| Affiliation: | 1. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China;2. Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843-3136, USA;3. Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 77803, USA;4. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;1. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China;2. Taian Environmental Protection Monitoring Station, Taian 271000, China;1. Key Laboratory of Coal Clean Conversion & Chemical Engineering Process, College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi 830046, China;2. State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry Chinese Academy of Sciences, Guangzhou 510640, China;1. Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China;2. Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China;3. University of Chinese Academy of Sciences, Beijing 100086, China;4. Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA |
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| Abstract: | In this work the performance and theoretical background behind two of the most commonly used receptor modelling methods in aerosol science, principal components analysis (PCA) and positive matrix factorization (PMF), as well as multivariate curve resolution by alternating least squares (MCR-ALS) and weighted alternating least squares (MCR-WALS), are examined. The performance of the four methods was initially evaluated under standard operational conditions, and modifications regarding data pre-treatment were then included. The methods were applied using raw and scaled data, with and without uncertainty estimations. Strong similarities were found among the sources identified by PMF and MCR-WALS (weighted models), whereas discrepancies were obtained with MCR-ALS (unweighted model). Weighting of input data by means of uncertainty estimates was found to be essential to obtain robust and accurate factor identification. The use of scaled (as opposed to raw) data highlighted the contribution of trace elements to the compositional profiles, which was key to the correct interpretation of the nature of the sources. Our results validate the performance of MCR-WALS for aerosol pollution studies. |
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