DOI: 10.1016/j.atmosenv.2020.117457
论文题名: Source apportionment for online dataset at a megacity in China using a new PTT-PMF model
作者: Gao J. ; Dong S. ; Yu H. ; Peng X. ; Wang W. ; Shi G. ; Han B. ; Wei Y. ; Feng Y.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 229 语种: 英语
英文关键词: Air pollution
; Air pollution control
; Coal dust
; Linear transformations
; Silicon
; Sulfur compounds
; Artificial datasets
; On-line measurement data
; Physical significance
; Receptor modelling
; research methods
; Secondary nitrates
; Source apportionment
; Source contributions
; Coal combustion
; ammonia
; calcium ion
; coal
; iron
; lead
; magnesium ion
; manganese
; nitrate
; organic compound
; potassium
; sodium ion
; sulfate
; zinc ion
; atmospheric modeling
; coal combustion
; data set
; megacity
; pollution control
; pollution policy
; source apportionment
; accuracy
; ambient air
; analytic method
; Article
; biomass
; biomass burning
; chemical analysis
; chemical composition
; China
; combustion
; dust
; exhaust gas
; information processing
; model
; Monte Carlo method
; online analysis
; partial target transformation positive matrix factor model
; particulate matter
; priority journal
; source apportionment method
; China
学科: Multilinear engine 2 (ME2)
; Online dataset
; Partial target transformation-PMF (PTT-PMF)
; Positive matrix factor (PMF)
; Source apportionment
; Target transformation factor
中文摘要: Air pollution negatively impact human health and the environment. Source apportionment is an important research method for designing effective air pollution control policies. In this study, a new PTT-PMF (Partial Target Transformation-Positive matrix factor) source apportionment method (based on Multilinear Engine 2 program) was developed, to better recognize the extracted source categories, for online dataset. By incorporating measured source profiles, this model can automatically extract factors that have physical significance and close to actual source profiles. The accuracy of the PTT-PMF method was first evaluated by using several artificial datasets, and the results were favorable. Results of the new model can better explain similar sources such as dust and coal combustion (when lack of markers such as Ai and Si), comparing with the base run obtained using PMF. Then, this model was applied to online measurement data collected in Tianjin, China. Six factors were obtained and they were close to actual source profiles: dust (6.06%), coal combustion (4.70%), secondary sulfate sources (26.36%), secondary nitrate sources (42.91%), vehicle exhaust (9.97%) and biomass burning & SOC (10.01%). Time series of source contributions can also help to identify the source categories. The work presented here may advance receptor modelling techniques and could also enhance the validity of source apportionment results. © 2020 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160823
Appears in Collections: 气候变化与战略
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作者单位: State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States; College of Computer Science, Nankai University, Tianjin, 300350, China; Tianjin Key Laboratory for Air Traffic Operation Planning and Safety Technology, Civil Aviation University of China, Tianjin, 300300, China
Recommended Citation:
Gao J.,Dong S.,Yu H.,et al. Source apportionment for online dataset at a megacity in China using a new PTT-PMF model[J]. Atmospheric Environment,2020-01-01,229