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Limitations in representation of physical processes prevent successful simulation of PM_(2.5) during KORUS-AQ

Travis, Katherine R. and Crawford, James H. and Chen, Gao and Jordan, Carolyn E. and Nault, Benjamin A. and Kim, Hwajin and Jimenez, Jose L. and Campuzano-Jost, Pedro and Dibb, Jack E. and Woo, Jung-Hun and Kim, Younha and Zhai, Shixian and Wang, Xuan and McDuffie, Erin E. and Luo, Gan and Yu, Fangqun and Kim, Saewung and Simpson, Isobel J. and Blake, Donald R. and Chang, Limseok and Kim, Michelle J. (2022) Limitations in representation of physical processes prevent successful simulation of PM_(2.5) during KORUS-AQ. Atmospheric Chemistry and Physics, 22 (12). pp. 7933-7958. ISSN 1680-7324. doi:10.5194/acp-22-7933-2022.

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High levels of fine particulate matter (PM_(2.5)) pollution in East Asia often exceed local air quality standards. Observations from the Korea–United States Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM_(2.5). Atmospheric models often have difficulty simulating PM_(2.5) chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM_(2.5) composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by −64 % but overestimates nitrate by +36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of 5 increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape, resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH₃ could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO₂ hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N₂O₅ hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model's inability to simulate the buildup of PM_(2.5) during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO₂ is added to the model, which previously only considered aqueous production of sulfate from SO₂ in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO₂ simulation, implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM_(2.5). During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO₂ decreases the fraction of PM_(2.5) attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM_(2.5), implying that local emissions controls could have a larger effect than previously thought. However, this additional uptake of SO₂ is coupled to the model nitrate prediction, which affects the aerosol liquid water abundance and chemistry driving sulfate–nitrate–ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO₂ to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and this results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate + nitrate + ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM_(2.5) in South Korea to ensure continued air quality improvements.

Item Type:Article
Related URLs:
URLURL TypeDescription Information Itemprecipitation data Itemcloud observations
Travis, Katherine R.0000-0003-1628-0353
Crawford, James H.0000-0002-6982-0934
Jordan, Carolyn E.0000-0001-8164-5967
Nault, Benjamin A.0000-0001-9464-4787
Kim, Hwajin0000-0001-6138-6443
Jimenez, Jose L.0000-0001-6203-1847
Campuzano-Jost, Pedro0000-0003-3930-010X
Dibb, Jack E.0000-0003-3096-7709
Kim, Younha0000-0002-5053-5068
Wang, Xuan0000-0002-8532-5773
McDuffie, Erin E.0000-0002-6845-6077
Luo, Gan0000-0002-9588-7008
Yu, Fangqun0000-0001-8862-4835
Kim, Saewung0000-0003-4847-3272
Simpson, Isobel J.0000-0002-4211-1126
Blake, Donald R.0000-0002-8283-5014
Chang, Limseok0000-0002-3296-546X
Kim, Michelle J.0000-0002-4922-4334
Alternate Title:Limitations in representation of physical processes prevent successful simulation of PM2.5 during KORUS-AQ
Additional Information:© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 12 Nov 2021 – Discussion started: 07 Jan 2022 – Revised: 23 May 2022 – Accepted: 27 May 2022 – Published: 20 Jun 2022. We acknowledge Gangwoong Lee for his leadership in managing the campaign efforts at Olympic Park. We acknowledge Andrew Weinheimer for the use of his NO and NO2 data from KORUS-AQ. We acknowledge Ron Cohen for the use of the TD-LIF data. We acknowledge Glenn Diskin for the use of DACOM CO and DLH RH data. We acknowledge Bill Brune for the use of his ATHOS OH data. We acknowledge Paul Wennberg and John Crounse for the use of their CIT-CIMS HNO3 data. We acknowledge L. Greg Huey for the use of his SO2 data. We acknowledge James J. Szykman for the use of ceilometer data at Olympic Park. We acknowledge Seogjo Cho for the MARGA data at Olympic Park. We acknowledge Ke Li and Yingying Yan for their help implementing aromatic chemistry in GEOS-Chem. We thank Jerome Fast, Rahul Zaveri, and David Peterson for helpful discussions. Katherine R. Travis and Benjamin A. Nault were supported by NASA grant 80NSSC22K0283. Pedro Campuzano-Jost and Jose L. Jimenez were supported by NASA grants 80NSSC18K0630 and 80NSSC19K0124. The GEOS-FP data used in this study/project have been provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. This research has been supported by the National Aeronautics and Space Administration (grant nos. 80NSSC18K0630, 80NSSC19K0124 and 80NSSC22K0283). Author contributions. The original draft preparation was completed by KRT, with review and editing by JHC, BAN, CEJ, HK, and GC. JHC, CEJ, GC, BAN, HK, and KRT contributed to project conceptualization. Modeling work was done by KRT, with additional support from SZ, XW, EM, GL, and FY. Formal analysis was completed by KRT and BAN. The observational data for this project were provided by BAN, HK, JLJ, PCJ, JED, MJK, SK, IJS, DRB, and LC. JHW and YK provided the KORUS-AQ emissions. Code availability. The model code used in this work is available at (Travis, 2022). Data availability. The KORUS-AQ data archive (KORUS-AQ Science Team, 2019) includes both the aircraft and ground-based measurements from AirKorea, Olympic Park, and KIST. The precipitation data are available at (NOAA, 2021). Cloud observations (RKSS ASOS station) are available here: (Iowa Environmental Mesonet, 2020). The supplement related to this article is available online at:
Funding AgencyGrant Number
Issue or Number:12
Record Number:CaltechAUTHORS:20220712-629732000
Persistent URL:
Official Citation:Travis, K. R., Crawford, J. H., Chen, G., Jordan, C. E., Nault, B. A., Kim, H., Jimenez, J. L., Campuzano-Jost, P., Dibb, J. E., Woo, J.-H., Kim, Y., Zhai, S., Wang, X., McDuffie, E. E., Luo, G., Yu, F., Kim, S., Simpson, I. J., Blake, D. R., Chang, L., and Kim, M. J.: Limitations in representation of physical processes prevent successful simulation of PM2.5 during KORUS-AQ, Atmos. Chem. Phys., 22, 7933–7958,, 2022.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115514
Deposited By: George Porter
Deposited On:13 Jul 2022 19:59
Last Modified:13 Jul 2022 19:59

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