DOI: 10.1016/j.atmosenv.2015.08.091
Scopus记录号: 2-s2.0-84942237838
论文题名: Use of high-order sensitivity analysis and reduced-form modeling to quantify uncertainty in particulate matter simulations in the presence of uncertain emissions rates: A case study in Houston
作者: Zhang W ; , Trail M ; A ; , Hu Y ; , Nenes A ; , Russell A ; G
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 122 起始页码: 103
结束页码: 113
语种: 英语
英文关键词: CMAQ
; Emission uncertainty
; High-order DDM sensitivity analysis
; Model uncertainty analysis
; PM2.5
; Reduced form model
Scopus关键词: Air quality
; Air quality standards
; Balloons
; Intelligent systems
; Monte Carlo methods
; Particulate emissions
; Quality control
; Sensitivity analysis
; CMAQ
; Emission uncertainties
; High-order
; Model uncertainties
; Reduced-form modeling
; Uncertainty analysis
; aerodynamics
; aerosol
; air quality
; atmospheric pollution
; concentration (composition)
; diameter
; emission inventory
; ground-based measurement
; model validation
; Monte Carlo analysis
; nitrogen oxides
; particulate matter
; sensitivity analysis
; sulfur emission
; uncertainty analysis
; air quality
; analytical parameters
; Article
; comparative study
; control strategy
; Monte Carlo method
; particulate matter
; precursor
; priority journal
; probability
; reduced form model
; secondary organic aerosol
; sensitivity analysis
; uncertainty
; Brazoria County
; Galveston
; Houston
; Texas
; 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: Regional air quality models are widely used to evaluate control strategy effectiveness. As such, it is important to understand the accuracy of model simulations to establish confidence in model performance and to guide further model development. Particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is regulated as one of the criteria pollutants by the National Ambient Air Quality Standards (NAAQS), and PM2.5 concentrations have a complex dependence on the emissions of a number of precursors, including SO2, NOx, NH3, VOCs, and primary particulate matter (PM). This study quantifies how the emission-associated uncertainties affect modeled PM2.5 concentrations and sensitivities using a reduced-form approach. This approach is computationally efficient compared to the traditional Monte Carlo simulation. The reduced-form model represents the concentration-emission response and is constructed using first- and second-order sensitivities obtained from a single CMAQ/HDDM-PM simulation. A case study is conducted in the Houston-Galveston-Brazoria (HGB) area. The uncertainty of modeled, daily average PM2.5 concentrations due to uncertain emissions is estimated to fall between 42% and 52% for different simulated concentration levels, and the uncertainty is evenly distributed in the modeling domain. Emission-associated uncertainty can account for much of the difference between simulation and ground measurements as 60% of observed PM2.5 concentrations fall within the range of one standard deviation of corresponding simulated PM2.5 concentrations. Uncertainties in meteorological fields as well as the model representation of secondary organic aerosol formation are the other two key contributors to the uncertainty of modeled PM2.5. This study also investigates the uncertainties of the simulated first-order sensitivities, and found that the larger the first-order sensitivity, the lower its uncertainty associated with emissions. Sensitivity of PM2.5 to primary PM has the lowest uncertainty while sensitivity of PM2.5 to VOC has the highest uncertainty associated with emission inputs. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81412
Appears in Collections: 气候变化事实与影响
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作者单位: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, United States; School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States; School of Chemical and oBiomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Institute of Chemical Engineering Sciences, Foundation for Research and Technology, Hellas, Patras, Greece; Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece; Department of Environmental Sciences, Emory University, Atlanta, United States
Recommended Citation:
Zhang W,, Trail M,A,et al. Use of high-order sensitivity analysis and reduced-form modeling to quantify uncertainty in particulate matter simulations in the presence of uncertain emissions rates: A case study in Houston[J]. Atmospheric Environment,2015-01-01,122