DOI: 10.1016/j.atmosenv.2014.11.043
Scopus记录号: 2-s2.0-84920964335
论文题名: A spatially varying coefficient model for mapping PM10 air quality at the European scale
作者: Hamm N ; A ; S ; , Finley A ; O ; , Schaap M ; , Stein A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 102 起始页码: 393
结束页码: 405
语种: 英语
英文关键词: Geostatistics
; LOTOS-EUROS
; Model evaluation
; PM10
; Spatially varying coefficient (SVC)
Scopus关键词: Air quality
; Health risks
; Hierarchical systems
; Markov processes
; Geo-statistics
; LOTOS-EUROS
; Model evaluation
; PM10
; Spatially varying coefficients
; Mapping
; nonoxinol 9
; air quality
; Bayesian analysis
; database
; geostatistics
; in situ measurement
; mapping method
; model validation
; monitoring
; particulate matter
; spatial variation
; air quality
; Article
; Bayesian learning
; covariance
; Eastern Europe
; geostatistical analysis
; measurement accuracy
; Monte Carlo method
; particulate matter
; prediction
; priority journal
; probability
; simulation
; spatially varying coefficient
; statistical model
; statistical parameters
; uncertainty
; Europe
; Spring viremia of carp virus
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: Particulate matter (PM) air quality in Europe has improved substantially over the past decades, but it still poses a significant threat to human health. Accurate regional scale maps of PM10 concentrations are needed for monitoring progress in mitigation strategies and monitoring compliance with statutory limit values. Chemistry transport models (CTM) use emission databases and simulate the transport and deposition of pollutants. They deliver such maps but are known to be inaccurate. A promising approach is to use geostatistics to model the relationship between the in situ observations and the CTM. This has been shown to be more accurate than using either observations or CTM's alone. This paper presents a spatially varying coefficients (SVC) geostatistical model as an extension of the standard spatially varying intercept (SVI) geostatistical model. SVC allowed the regression coefficient to vary spatially according to a covariance function, the parameters of which were estimated from the data. It was built as a Bayesian hierarchical model and implemented using Markov chain Monte Carlo. The procedure was applied to Airbase PM10 observations and LOTOS-EUROS simulated PM10 for central, southern and eastern Europe. Model-fit diagnostics showed that SVC delivered a better fit to the data than SVI. Mapping the spatially varying coefficients allowed identification of the locations where the CTM performed well or poorly. This could be used for objective CTM evaluation purposes. The posterior predictive simulations were also used to map median PM10 concentrations as well as the probability of exceeding the 50μgm-3 EU daily PM10 concentration threshold. Although posterior median prediction accuracy was similar for SVI and SVC, SVC better modelled the process and yielded narrower credible intervals. As such, SVC was more appropriate for quantifying uncertainty and for mapping threshold exceedances. The resulting maps may be used to guide air quality assessment and mitigation strategies, including those related to health impacts. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82057
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, Enschede, AE, Netherlands; Departments of Forestry and Geography, Michigan State University, East Lansing, MI, United States; Department of Climate, Air and Sustainability, TNO Built Environment and Geosciences, PO Box 80015, Utrecht, TA, Netherlands
Recommended Citation:
Hamm N,A,S,et al. A spatially varying coefficient model for mapping PM10 air quality at the European scale[J]. Atmospheric Environment,2015-01-01,102