气候变化领域集成服务门户(CCPortal): Source apportionment for online dataset at a megacity in China using a new PTT-PMF model

CCPortal

DOI	10.1016/j.atmosenv.2020.117457
	Source apportionment for online dataset at a megacity in China using a new PTT-PMF model
	Gao J.; Dong S.; Yu H.; Peng X.; Wang W.; Shi G.; Han B.; Wei Y.; Feng Y.
发表日期	2020
ISSN	1352-2310
卷号	229
英文摘要	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
英文关键词	Multilinear engine 2 (ME2); Online dataset; Partial target transformation-PMF (PTT-PMF); Positive matrix factor (PMF); Source apportionment; Target transformation factor
语种	英语
scopus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
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129541
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
推荐引用方式 GB/T 7714	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],2020,229.
APA	Gao J..,Dong S..,Yu H..,Peng X..,Wang W..,...&Feng Y..(2020).Source apportionment for online dataset at a megacity in China using a new PTT-PMF model.Atmospheric Environment,229.
MLA	Gao J.,et al."Source apportionment for online dataset at a megacity in China using a new PTT-PMF model".Atmospheric Environment 229(2020).