title: Source insights into the 11-h daytime and nighttime fine ambient particulate matter in China as well as the synthetic studies using the new Multilinear Engine 2-species ratios (ME2-SR) method.
abstract: Modeled results are very important for environmental management. Unreasonable modeled result can lead to wrong strategy for air pollution management. In this work, an improved physically constrained source apportionment (PCSA) technology known as Multilinear Engine 2-species ratios (ME2-SR) was developed to the 11-h daytime and nighttime fine ambient particulate matter in urban area. Firstly, synthetic studies were carried out to explore the effectiveness of ME2-SR. The estimated source contributions were compared with the true values. The results suggest that, compared with the positive matrix factorization (PMF) model, the ME2-SR method could obtain more physically reliable outcomes, indicating that ME2-SR was effective, especially when apportioning the datasets with no unknown source. Additionally, 11-h daytime and nighttime PM2.5 samples were collected from Tianjin in China. The sources of the 11-h daytime and nighttime fine ambient particulate matter in China were identified using the new method and the PMF model. The calculated source contributions for ME2-SR for daytime PM2.5 samples are resuspended dust (38.91 μg m(-3), 26.60%), sulfate and nitrate (38.60 μg m(-3), 26.39%), vehicle exhaust and road dust (38.26 μg m(-3), 26.16%) and coal combustion (20.14 μg m(-3), 13.77%), and those for nighttime PM2.5 samples are resuspended dust (18.78 μg m(-3), 12.91%), sulfate and nitrate (41.57 μg m(-3), 28.58%), vehicle exhaust and road dust (38.39 μg m(-3), 26.39%), and coal combustion (36.76 μg m(-3), 25.27%). The comparisons of the constrained versus unconstrained outcomes clearly suggest that the physical meaning of the ME2-SR results is interpretable and reliable, not only for the specified species values but also for source contributions. The findings indicate that the ME2-SR method can be a useful tool in source apportionment studies, for air pollution management.
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: Guoliang
authors.initials: G
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.
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
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; Environment Monitoring Center of Ningbo, Ningbo, 315012, China. Electronic address: nklgr@hotmail.com.
authors.forename: Guirong
authors.initials: G
authors.lastname: Liu
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: Haiting
authors.initials: H
authors.lastname: Wang
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: Yingze
authors.initials: Y
authors.lastname: Tian
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: Yinchang
authors.initials: Y
authors.lastname: Feng
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D002681:China; D002947:Cities; D003031:Coal; D016208:Databases, Factual; D004391:Dust; D004784:Environmental Monitoring / Q000379:methods; D008962:Models, Theoretical; D009566:Nitrates / Q000032:analysis; D052638:Particulate Matter / Q000032:analysis*; D013431:Sulfates / Q000032:analysis; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000393:Air Pollutants; D003031:Coal; D004391:Dust; D009566:Nitrates; D052638:Particulate Matter; D013431:Sulfates; D001335:Vehicle Emissions
keywords: ME2-SR; PMF; Source apportionment; Source management; Species ratios