DOI: 10.1016/j.atmosenv.2017.06.039
Scopus记录号: 2-s2.0-85024919986
论文题名: Identification of biased sectors in emission data using a combination of chemical transport model and receptor model
作者: Uranishi K ; , Ikemori F ; , Nakatsubo R ; , Shimadera H ; , Kondo A ; , Kikutani Y ; , Asano K ; , Sugata S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 166 起始页码: 166
结束页码: 181
语种: 英语
英文关键词: Brute-force method
; CMAQ
; Emission uncertainty
; PM2.5
; PMF
; Source apportionment
Scopus关键词: Biomass
; Combustion
; Crude oil
; Factorization
; Heavy oil production
; Monte Carlo methods
; Motor transportation
; Petroleum transportation
; Roads and streets
; Brute force
; Chemical transport models
; CMAQ
; Community multi-scale air quality models
; Emission uncertainties
; Positive matrix factorization models
; Source apportionment
; Temporal and spatial variation
; Air quality
; nitrate
; oil
; sulfate
; air quality
; algorithm
; atmospheric modeling
; atmospheric pollution
; chemical pollutant
; concentration (composition)
; emission
; particulate matter
; pollutant transport
; source apportionment
; source identification
; uncertainty analysis
; air monitoring
; air quality
; Article
; biomass
; brute force method
; China
; combustion
; Community Multiscale Air Quality model
; controlled study
; dust
; Japan
; particulate matter
; Positive Matrix Factorization model
; priority journal
; season
; ship
; simulation
; traffic and transport
; Honshu
; Japan
; Kinki
; Tokai [Honshu]
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: This study presented a comparison approach with multiple source apportionment methods to identify which sectors of emission data have large biases. The source apportionment methods for the comparison approach included both receptor and chemical transport models, which are widely used to quantify the impacts of emission sources on fine particulate matter of less than 2.5 μm in diameter (PM2.5). We used daily chemical component concentration data in the year 2013, including data for water-soluble ions, elements, and carbonaceous species of PM2.5 at 11 sites in the Kinki–Tokai district in Japan in order to apply the Positive Matrix Factorization (PMF) model for the source apportionment. Seven PMF factors of PM2.5 were identified with the temporal and spatial variation patterns and also retained features of the sites. These factors comprised two types of secondary sulfate, road transportation, heavy oil combustion by ships, biomass burning, secondary nitrate, and soil and industrial dust, accounting for 46%, 17%, 7%, 14%, 13%, and 3% of the PM2.5, respectively. The multiple-site data enabled a comprehensive identification of the PM2.5 sources. For the same period, source contributions were estimated by air quality simulations using the Community Multiscale Air Quality model (CMAQ) with the brute-force method (BFM) for four source categories. Both models provided consistent results for the following three of the four source categories: secondary sulfates, road transportation, and heavy oil combustion sources. For these three target categories, the models’ agreement was supported by the small differences and high correlations between the CMAQ/BFM- and PMF-estimated source contributions to the concentrations of PM2.5, SO4 2−, and EC. In contrast, contributions of the biomass burning sources apportioned by CMAQ/BFM were much lower than and little correlated with those captured by the PMF model, indicating large uncertainties in the biomass burning emissions used in the CMAQ simulations. Thus, this comparison approach using the two antithetical models enables us to identify which sectors of emission data have large biases for improvement of future air quality simulations. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82753
Appears in Collections: 气候变化事实与影响
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作者单位: Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan; Nagoya City Institute for Environmental Sciences, 5-16-8 Toyota, Minami-ku, Nagoya, Aichi, Japan; Hyogo Prefectural Institute of Environmental Sciences, Hyogo Environmental Advancement Association, 3-1-18 Yukihira-cho, Suma-ku, Kobe, Hyogo, Japan; Landscape and Environment Bureau, Nara Prefectural Government, 30 Noborioji-cho, Nara, Nara, Japan; Nara Prefecture Landscape and Environment Center, 1000 Odono, Sakurai, Nara, Japan; National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, Japan
Recommended Citation:
Uranishi K,, Ikemori F,, Nakatsubo R,et al. Identification of biased sectors in emission data using a combination of chemical transport model and receptor model[J]. Atmospheric Environment,2017-01-01,166