Sensitivity analysis of simulated SOA loadings using a variance-based statistical approach: SENSITIVITY ANALYSIS OF SOA

Journal Article · Fri Apr 08 00:00:00 EDT 2016 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1002/2015MS000554· OSTI ID:1333446

Shrivastava, Manish ^[1]; Zhao, Chun ^[1]; Easter, Richard C. ^[1]; Qian, Yun ^[1]; Zelenyuk, Alla ^[1]; Fast, Jerome D. ^[1]; Liu, Ying ^[1]; Zhang, Qi ^[2]; Guenther, Alex ^[3]

---

1. Pacific Northwest National Laboratory, Richland Washington USA
2. Department of Environmental Toxicology, University of California Davis, California USA
3. Department of Earth System Science, University of California, Irvine California USA

We investigate the sensitivity of secondary organic aerosol (SOA) loadings simulated by a regional chemical transport model to 7 selected tunable model parameters: 4 involving emissions of anthropogenic and biogenic volatile organic compounds, anthropogenic semi-volatile and intermediate volatility organics (SIVOCs), and NOx, 2 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 tunable parameters. Analysis of SOA variance from all 250 simulations shows that the volatility transformation parameter, which controls whether particle-phase transformation of SOA from semi-volatile SOA to non-volatile is on or off, is the dominant contributor to variance of simulated surface-level daytime SOA (65% domain average contribution). We also split the simulations into 2 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 non-volatile 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. 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 transformation of SOA volatility, which is neglected in most previous models.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1333446
• Report Number(s): PNNL-SA-113017; KP1701000
• Journal Information: Journal of Advances in Modeling Earth Systems, Vol. 8, Issue 2; ISSN 1942-2466
• Publisher: American Geophysical Union (AGU)
• Country of Publication: United States
• Language: English