DOI: 10.1002/2015MS000554
Scopus记录号: 2-s2.0-84962867199
论文题名: Sensitivity analysis of simulated SOA loadings using a variance-based statistical approach
作者: Shrivastava M ; , Zhao C ; , Easter R ; C ; , Qian Y ; , Zelenyuk A ; , Fast J ; D ; , Liu Y ; , Zhang Q ; , Guenther A
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 2 起始页码: 499
结束页码: 519
语种: 英语
英文关键词: Aerosols
; Atmospheric composition
; Chemical analysis
; Chemical compounds
; Deposition
; Monte Carlo methods
; Oligomerization
; Oligomers
; Uncertainty analysis
; Volatile organic compounds
; Biogenic volatile organic compounds
; Generalized linear model
; Quasi-monte carlo samplings
; Regional chemical transport model
; Secondary organic aerosols
; Transformation parameters
; Uncertainty quantifications
; Variance-based sensitivity analysis
; Sensitivity analysis
; aerosol
; anthropogenic effect
; atmospheric pollution
; biogenic emission
; dry deposition
; Monte Carlo analysis
; numerical model
; sensitivity analysis
; simulation
; statistical analysis
; uncertainty analysis
; volatile organic compound
英文摘要: We investigate the sensitivity of secondary organic aerosol (SOA) loadings simulated by a regional chemical transport model to seven selected model parameters using a modified volatility basis-set (VBS) approach: four involving emissions of anthropogenic and biogenic volatile organic compounds, anthropogenic semivolatile and intermediate volatility organics (SIVOCs), and NOx; two involving dry deposition of SOA precursor gases, and one involving particle-phase transformation of SOA to low volatility. We adopt a quasi-Monte Carlo sampling approach to effectively sample the high-dimensional parameter space, and perform a 250 member ensemble of simulations using a regional model, accounting for some of the latest advances in SOA treatments based on our recent work. We then conduct a variance-based sensitivity analysis using the generalized linear model method to study the responses of simulated SOA loadings to the model parameters. Analysis of SOA variance from all 250 simulations shows that the volatility transformation parameter, which controls whether or not SOA that starts as semivolatile is rapidly transformed to nonvolatile SOA by particle-phase processes such as oligomerization and/or accretion, is the dominant contributor to variance of simulated surface-level daytime SOA (65% domain average contribution). We also split the simulations into two subsets of 125 each, depending on whether the volatility transformation is turned on/off. For each subset, the SOA variances are dominated by the parameters involving biogenic VOC and anthropogenic SIVOC emissions. Furthermore, biogenic VOC emissions have a larger contribution to SOA variance when the SOA transformation to nonvolatile is on, while anthropogenic SIVOC emissions have a larger contribution when the transformation is off. NOx contributes less than 4.3% to SOA variance, and this low contribution is mainly attributed to dominance of intermediate to high NOx conditions throughout the simulated domain. However, we note that SOA yields have a more complex nonlinear dependence on NOx levels, which needs to be addressed by more integrated model-measurement approaches focused on gaining a better process-level understanding of anthropogenic-biogenic interactions. The two parameters related to dry deposition of SOA precursor gases also have very low contributions to SOA variance. This study highlights the large sensitivity of SOA loadings to the particle-phase processes such as oligomerization that rapidly cause large decrease in the volatility of SOA, which is neglected in most previous models. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75905
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Pacific Northwest National Laboratory, Richland, WA, United States; Department of Environmental Toxicology, University of California DavisCA, United States; Department of Earth System Science, University of California, Irvine, CA, United States
Recommended Citation:
Shrivastava M,, Zhao C,, Easter R,et al. Sensitivity analysis of simulated SOA loadings using a variance-based statistical approach[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(2)