Articles | Volume 19, issue 2
https://doi.org/10.5194/acp-19-1241-2019
https://doi.org/10.5194/acp-19-1241-2019
Research article
 | 
31 Jan 2019
Research article |  | 31 Jan 2019

Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model

Youngseob Kim, Karine Sartelet, and Florian Couvidat

Related authors

Validation and Analysis of the Polair3D v1.11 Chemical Transport Model Over Quebec
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Youngseob Kim, Daniel Yazgi, Andrée-Anne Brown, and Marianne Hatzopoulou
EGUsphere, https://doi.org/10.5194/egusphere-2023-2038,https://doi.org/10.5194/egusphere-2023-2038, 2023
Short summary
A two-way coupled regional urban–street network air quality model system for Beijing, China
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023,https://doi.org/10.5194/gmd-16-5585-2023, 2023
Short summary
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023,https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD
Chao Lin, Yunyi Wang, Ryozo Ooka, Cédric Flageul, Youngseob Kim, Hideki Kikumoto, Zhizhao Wang, and Karine Sartelet
Atmos. Chem. Phys., 23, 1421–1436, https://doi.org/10.5194/acp-23-1421-2023,https://doi.org/10.5194/acp-23-1421-2023, 2023
Short summary
MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022,https://doi.org/10.5194/gmd-15-7371-2022, 2022
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020
Hao Wang, Xiaohong Liu, Chenglai Wu, and Guangxing Lin
Atmos. Chem. Phys., 24, 3309–3328, https://doi.org/10.5194/acp-24-3309-2024,https://doi.org/10.5194/acp-24-3309-2024, 2024
Short summary
Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC
Christof G. Beer, Johannes Hendricks, and Mattia Righi
Atmos. Chem. Phys., 24, 3217–3240, https://doi.org/10.5194/acp-24-3217-2024,https://doi.org/10.5194/acp-24-3217-2024, 2024
Short summary
Seasonal characteristics of emission, distribution, and radiative effect of marine organic aerosols over the western Pacific Ocean: an investigation with a coupled regional climate aerosol model
Jiawei Li, Zhiwei Han, Pingqing Fu, Xiaohong Yao, and Mingjie Liang
Atmos. Chem. Phys., 24, 3129–3161, https://doi.org/10.5194/acp-24-3129-2024,https://doi.org/10.5194/acp-24-3129-2024, 2024
Short summary
Fire–precipitation interactions amplify the quasi-biennial variability in fires over southern Mexico and Central America
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024,https://doi.org/10.5194/acp-24-3115-2024, 2024
Short summary
Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights
Xu Feng, Loretta J. Mickley, Michelle L. Bell, Tianjia Liu, Jenny A. Fisher, and Maria Val Martin
Atmos. Chem. Phys., 24, 2985–3007, https://doi.org/10.5194/acp-24-2985-2024,https://doi.org/10.5194/acp-24-2985-2024, 2024
Short summary

Cited articles

Abramson, E., Imre, D., Beranek, J., Wilson, J., and Zelenyuk, A.: Experimental determination of chemical diffusion within secondary organic aerosol particles, Phys. Chem. Chem. Phys., 15, 2983–2991, https://doi.org/10.1039/C2CP44013J, 2013. a, b
Boucher, O.: Atmospheric Aerosols, Properties and Climate Impact, Springer-Netherlands, https://doi.org/10.1007/978-94-017-9649-1, 2015. a
Bowman, F. M., Odum, J. R., Seinfeld, J. H., and Pandis, S. N.: Mathematical model for gas-particle partitioning of secondary organic aerosols, Atmos. Environ., 31, 3921–3931, https://doi.org/10.1016/S1352-2310(97)00245-8, 1997. a
Boylan, J. W. and Russell, A. G.: PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models, Atmos. Environ., 40, 4946–4959, https://doi.org/10.1016/j.atmosenv.2005.09.087, 2006. a, b
Capaldo, K. P., Pilinis, C., and Pandis, S. N.: A computationally efficient hybrid approach for dynamic gas/aerosol transfer in air quality models, Atmos. Environ., 34, 3617–3627, https://doi.org/10.1016/S1352-2310(00)00092-3, 2000. a
Download
Short summary
Assumptions (ideality and thermodynamic equilibrium) commonly made in 3-dimensional air quality models were reconsidered to evaluate their impacts on secondary organic aerosol (SOA) formation. Non-ideality (short-, medium- and long-range interactions of organics and inorganics) influences SOA concentrations by about 30 % over Europe. If SOA are highly viscous rather than inviscid, hydrophobic SOA concentrations increase by 6 % but can increase by an order of magnitude for volatile compounds.
Altmetrics
Final-revised paper
Preprint