title: Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model.
abstract: Samples of PM₁₀ and PM₂.₅ were synchronously collected from a megacity in China (Chengdu) during the 2011 sampling campaign and then analyzed by an improved three-way factor analysis method based on ME2 (multilinear engine 2), to investigate the contributions and size distributions of the source categories for size segregated particulate matter (PM). Firstly, the synthetic test was performed to evaluate the accuracy of the improved three-way model. The same five source categories with slightly different source profiles were caught. The low AAE (average absolute error) values between the estimated and the synthetic source contributions (<15%) and the approachable estimated PM₂.₅/PM₁₀ ratios with the simulated ratios might indicate that the results of the improved three-way factor analysis might be satisfactory. Then, for the ambient PM samples, the mean levels were 206.65 ± 69.90 μg/m(3) (PM₁₀) and 130.47 ± 43.67 μg/m(3) (PM₂.₅). The average ratio of PM₂.₅/PM₁₀ was 0.63. PM₁₀ and PM₂.₅ in Chengdu were influenced by the same source categories and their percentage contributions were in the same order: crustal dust & coal combustion presented the highest percentage contributions, accounting for 58.20% (PM₁₀) and 53.73% (PM2.5); followed by vehicle exhaust & secondary organic carbon (18.45% for PM₁₀ and 21.63% for PM₂.₅), secondary sulfate and nitrate (17.06% for PM₁₀ and 20.91% for PM₂.₅) and cement dust (6.30% for PM₁₀ and 3.73% for PM₂.₅). The source profiles and contributions presented slightly different distributions for PM₁₀ and PM₂.₅, which could better reflect the actual situation. The findings based on the improved three-way factor analysis method may provide clear and deep insights into the sources of synchronously size-resolved PM.
authors.affiliation: 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. Electronic address: nksgl@hotmail.com.
authors.forename: Guo-Liang
authors.initials: GL
authors.lastname: Shi
authors.affiliation: 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. Electronic address: tianyingze@hotmail.com.
authors.forename: Ying-Ze
authors.initials: YZ
authors.lastname: Tian
authors.affiliation: College of Software, Nankai University, No. 94 Weijin Road, Tianjin 300071, China.
authors.forename: Si
authors.initials: S
authors.lastname: Ye
authors.affiliation: 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.
authors.forename: Xing
authors.initials: X
authors.lastname: Peng
authors.affiliation: 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.
authors.forename: Jiao
authors.initials: J
authors.lastname: Xu
authors.affiliation: College of Software, Nankai University, No. 94 Weijin Road, Tianjin 300071, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Tianjin Key Laboratory for Air Traffic Operation Planning and Safety Technology, Civil Aviation University of China, Tianjin 300300, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Han
authors.affiliation: 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. Electronic address: fengyc@nankai.edu.cn.
authors.forename: Yin-Chang
authors.initials: YC
authors.lastname: Feng
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005163:Factor Analysis, Statistical; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Improved three-way factor analysis model; PM(10); PM(2.5); Size-distribution analysis; Source apportionment