DOI: 10.1016/j.atmosenv.2015.07.027
Scopus记录号: 2-s2.0-84939132283
论文题名: Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
作者: Gómez-Losada A ; , Pires J ; C ; M ; , Pino-Mejías R
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 117 起始页码: 271
结束页码: 281
语种: 英语
英文关键词: Apportionments
; Hidden Markov Model
; PM10
; Sahara
Scopus关键词: Arid regions
; Chemical analysis
; Chemical speciation
; Estimation
; Landforms
; Markov processes
; Time series
; Trellis codes
; Apportionments
; Concentration values
; Hidden markov models (HMMs)
; Sahara
; Source apportionment
; Source contributions
; Time consuming analysis
; Time series clustering
; Hidden Markov models
; atmospheric pollution
; cluster analysis
; concentration (composition)
; cost analysis
; desert
; Markov chain
; particulate matter
; pollutant source
; quantitative analysis
; speciation (chemistry)
; time series
; air monitoring
; air pollutant
; air pollution
; air quality
; ambient air
; Article
; beta radiation
; comparative study
; controlled study
; desertification
; hidden Markov model
; North African
; particulate matter
; Portugal
; priority journal
; radiation attenuation
; Spain
; time series analysis
; Canary Islands
; Portugal
; Spain
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: Source apportionment studies use prior exploratory methods that are not purpose-oriented and receptor modelling is based on chemical speciation, requiring costly, time-consuming analyses. Hidden Markov Models (HMMs) are proposed as a routine, exploratory tool to estimate PM10 source contributions. These models were used on annual time series (TS) data from 33 background sites in Spain and Portugal. HMMs enable the creation of groups of PM10 TS observations with similar concentration values, defining the pollutant's regimes of concentration. The results include estimations of source contributions from these regimes, the probability of change among them and their contribution to annual average PM10 concentrations. The annual average Saharan PM10 contribution in the Canary Islands was estimated and compared to other studies. A new procedure for quantifying the wind-blown desert contributions to daily average PM10 concentrations from monitoring sites is proposed. This new procedure seems to correct the net load estimation from deserts achieved with the most frequently used method. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81601
Appears in Collections: 气候变化事实与影响
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作者单位: Departmento de Estadística e Investigación Operativa, Facultad de Matemáticas, Universidad de Sevilla, Avda. Reina Mercedes s/n, Sevilla, Spain; LEPABE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, Porto, Portugal
Recommended Citation:
Gómez-Losada A,, Pires J,C,et al. Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts[J]. Atmospheric Environment,2015-01-01,117