DOI: 10.1016/j.atmosenv.2015.03.062
Scopus记录号: 2-s2.0-84926633723
论文题名: A pragmatic approach to estimate the number of days in exceedance of PM<inf>10</inf> limit value
作者: Beauchamp M ; , Malherbe L ; , de Fouquet C
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 111 起始页码: 79
结束页码: 93
语种: 英语
英文关键词: Air pollution
; Exceedances of air quality limit values
; Geostatistics
; Kriging
; Number of exceedances
Scopus关键词: Air pollution
; Air quality
; Atmospheric chemistry
; Atmospheric movements
; Interpolation
; Laws and legislation
; Mapping
; Poisson distribution
; Probability
; Atmospheric concentration
; Chemistry transport model
; Cross-validation analysis
; Geo-statistics
; Kriging
; Number of exceedances
; Quality limits
; Translated Poisson distribution
; Probability distributions
; air quality
; ambient air
; atmospheric pollution
; computer simulation
; concentration (composition)
; geostatistics
; kriging
; legislative implementation
; measurement method
; numerical model
; particulate matter
; pollution monitoring
; Article
; concentration (parameters)
; environmental monitoring
; France
; kernel method
; kriging
; methodology
; particulate matter
; Poisson distribution
; pollution monitoring
; priority journal
; probability
; uncertainty
; variance
; Europe
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: European legislation on ambient air quality requests that Member States report the annual number of exceedances of short-term concentration regulatory thresholds for PM10 and delimit the concerned areas. Measurements at the monitoring stations do not allow to fully describe those areas. We present a methodology to estimate the number of exceedances of the daily limit value over a year, that can be extended to any similar issue. This methodology is applied to PM10 concentrations in France for which the daily limit value is 50μgm-3, not to be exceeded more that 35 days. A probabilistic model is built using preliminary mapping of daily mean concentrations.First, daily atmospheric concentration fields are estimated at 1km resolution by external drift kriging, combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. Setting a conventional Gaussian hypothesis for the estimation error, the kriging variance is used to compute the probability of exceeding the daily limit value and to identify three areas: those where we can suppose as certain that the concentrations exceed or not the daily limit value and those where the situation is indeterminate because of the estimation uncertainty.Then, from the set of 365 daily mappings of the probability to exceed the daily limit value, the parameters of a translated Poisson distribution is fitted on the annual number of exceedances of the daily limit value at each grid cell, which enables to compute the probability for this number to exceed 35. The methodology is tested for three years (2007, 2009 and 2011) which present numerous exceedances of the daily limit concentration at some monitoring stations. A cross-validation analysis is carried out to check the efficiency of the methodology. The way to interpret probability maps is discussed. A comparison is made with simpler kriging approaches using indicator kriging of exceedances. Lastly, estimation of the population exposed to PM10 exceedances is discussed. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81782
Appears in Collections: 气候变化事实与影响
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作者单位: Institut National de l'Environnement Industriel et des Risques (INERIS), Direction des Risques Chroniques, Parc Technologique Alata, Verneuil-en-Halatte, France; Mines ParisTech, Géosciences, Equipe Géostatistique, 35 rue Saint Honoré, Fontainebleau, France
Recommended Citation:
Beauchamp M,, Malherbe L,, de Fouquet C. A pragmatic approach to estimate the number of days in exceedance of PM<inf>10</inf> limit value[J]. Atmospheric Environment,2015-01-01,111