DOI: 10.1016/j.atmosenv.2017.07.033
Scopus记录号: 2-s2.0-85026448416
论文题名: Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification
作者: Xu Y ; , Serre M ; L ; , Reyes J ; M ; , Vizuete W
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 166 起始页码: 374
结束页码: 382
语种: 英语
英文关键词: Bayesian Maximum Entropy
; Chemical transport model
; Data fusion
; Ozone
Scopus关键词: Air pollution
; Data fusion
; Entropy
; Errors
; Estimation
; Forecasting
; Ozone
; Ambient air pollution
; Bayesian maximum entropies
; Chemical transport models
; Horizontal resolution
; Implementation and computation
; Ozone concentration
; Spatial and temporal resolutions
; Spatial variability
; Chemical analysis
; ozone
; ambient air
; atmospheric modeling
; atmospheric pollution
; Bayesian analysis
; classification
; concentration (composition)
; data processing
; error analysis
; maximum entropy analysis
; model test
; monitoring system
; ozone
; prediction
; spatial resolution
; temporal analysis
; transport process
; upscaling
; air monitoring
; air pollution
; ambient air
; analytical error
; Article
; Bayes theorem
; classification
; conceptual framework
; entropy
; environmental exposure
; environmental impact assessment
; prediction
; priority journal
; simulation
; time
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: We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82655
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Environmental Sciences and Engineering, UNC, 135 Dauer Drive, Chapel Hill, United States
Recommended Citation:
Xu Y,, Serre M,L,et al. Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification[J]. Atmospheric Environment,2017-01-01,166