DOI: 10.1016/j.atmosenv.2014.06.024
Scopus记录号: 2-s2.0-84902681508
论文题名: Simultaneous statistical bias correction of multiple PM2.5 species from a regional photochemical grid model
作者: Crooks J ; L ; , Özkaynak H
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 95 起始页码: 126
结束页码: 141
语种: 英语
英文关键词: Air pollution
; Bayesian
; CMAQ
; Multi-pollutant
; Splines
Scopus关键词: Air pollution
; Pollution
; Splines
; Ambient air pollution
; Ambient concentrations
; Bayesian
; CMAQ
; Community multi-scale air qualities
; Epidemiological studies
; Mass conservation
; Multi-pollutant
; Air quality
; atmospheric pollution
; methodology
; particulate matter
; photochemistry
; pollution monitoring
; seasonal variation
; spatiotemporal analysis
; statistical analysis
; air monitoring
; air quality standard
; ambient air
; article
; bias corrected cmaq model
; concentration (parameters)
; cross-sectional study
; human
; kriging
; photochemistry
; pregnancy outcome
; priority journal
; seasonal variation
; spatiotemporal analysis
; statistical analysis
; statistical bias
; United States
; validation study
; New Jersey
; United States
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: In recent years environmental epidemiologists have begun utilizing regional-scale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to data from fixed-site ambient monitors. The advantages of using such models include better spatio-temporal coverage and the capability to predict concentrations of unmonitored pollutants. However, there are also drawbacks, chief among them being that these models can exhibit systematic spatial and temporal biases. In order to use these models in epidemiological investigations it is very important to bias-correct the model surfaces. We present a novel statistical method of spatio-temporal bias correction for the Community Multi-scale Air Quality (CMAQ) model that allows simultaneous bias adjustment of PM2.5 mass and its major constituent species using publically available speciated data from ambient monitors. The method uses mass conservation and the more widespread unspeciated PM2.5 mass observations to constrain the sum of the PM2.5 species' concentrations in locations without speciated monitors. We develop the model in the context of an epidemiological study investigating the association between PM2.5 species' ambient concentrations and birth outcomes throughout the state of New Jersey. Since our exposures of interest are multi-month averages we focus specifically on modeling seasonal bias trends rather than daily biases. Using a cross-validation study we find that our bias-corrected CMAQ results are more accurate than either the original CMAQ output or a spline fit without CMAQ. More interestingly, we find that our model clearly performs better when mass conservation is enforced, and furthermore that our model is competitive with Kriging in a comparison in which the latter has the advantage. © 2014. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80988
Appears in Collections: 气候变化事实与影响
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作者单位: U.S. Environmental Protection Agency, Office of Research and Development, 109 TW. Alex. Dr., Mail Code: 58-C, Research Triangle Park, NC 27709, United States; U.S. Environmental Protection Agency, Office of Research and Development, 109 TW. Alex. Dr. MailCode:E205-01, Research Triangle Park, NC 27709, United States
Recommended Citation:
Crooks J,L,, Özkaynak H. Simultaneous statistical bias correction of multiple PM2.5 species from a regional photochemical grid model[J]. Atmospheric Environment,2014-01-01,95