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https://doi.org/10.5194/egusphere-2026-2380
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-2380
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 May 2026
| 08 May 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Assessing Air Pollution Drivers in Asia Through Multi-Species Data Assimilation During NASA's ASIA-AQ Campaign
Abstract. Asia accounts for approximately half of global anthropogenic emissions of major pollutants, yet emission inventories remain uncertain and ground-based monitoring is sparse across much of the region. To address these challenges, this study applies a multi-species satellite data assimilation framework to estimate emissions and concentrations of key chemical species during the NASA's ASIA-AQ campaign. The assimilation improves agreement with airborne observations for O3, NOx, CO, and CH2O, with the largest gains for CO (correlation increasing from 0.63–0.64 to 0.77) and CH2O (biases reduced by 41–70 %). Domain-wide, the optimized emissions show increases of 15 % for NOx and 9 % for CO, and a 52 % reduction in isoprene. Comparisons with multiple emission inventories reveal large discrepancies, with normalized standard deviation ranging from 11 % for NOx in mainland China to 68 % for CO in Taiwan. Over Thailand, the assimilation increases fire emissions from 3.6 Tg (GFASv1.2) to 8.3 Tg, while FINNv2.7 produces estimates roughly twice as high, highlighting persistent divergence among fire emission estimates. Source-receptor analysis reveals strong meteorological control on transboundary pollutant: long-range transport contributes up to 62 % of surface O3 in Manila during strong monsoon conditions, whereas Seoul exhibits local NOx-saturated chemistry. During a strong transport episode, Indian emissions account for 72–78 % of the free tropospheric O3 response over Taipei and Chiang Mai, and 24 % over Seoul, highlighting an overlooked transport pathway affecting Asian air quality. These results highlight the value of satellite data assimilation and the need for improved inventories and coordinated action on local and transboundary pollution.
How to cite. Choi, J., Miyazaki, K., Sekiya, T., Yu, J., Ogino, S.-Y., Eskes, H., Rijdijk, P., Bennett, R., Roiger, A., Knez, L., Diskin, G. S., Miech, J. A., DiGangi, J. P., Choi, Y., Franchin, A., Cho, C., Apel, E. C., Emmons, L. K., Maneenoi, N., St. Clair, J. M., Hannun, R. A., Wolfe, G. M., Delaria, E., Sebol, A., Blake, D. R., Crippa, M., Hoesly, R. M., Smith, S. J., Woo, J.-H., Kuai, L., and Macatangay, R.: Assessing Air Pollution Drivers in Asia Through Multi-Species Data Assimilation During NASA's ASIA-AQ Campaign, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-2380, 2026.
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Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Kazuyuki Miyazaki
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Joint Institute for Regional System Science and Engineering (JIFRESSE), University of California, Los Angeles, Los Angeles, CA, USA
Takashi Sekiya
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Jinhyeok Yu
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Shin-Ya Ogino
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Henk Eskes
Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
Pieter Rijdijk
Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
Ryan Bennett
Bay Area Environmental Research Institute, NASA Ames Research Center, Moffett Field, CA, USA
Anke Roiger
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Leon Knez
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Glenn S. Diskin
NASA Langley Research Center, Hampton, VA, USA
Jason A. Miech
NASA Langley Research Center, Hampton, VA, USA
Oak Ridge Associated Universities, Oak Ridge, TN, USA
Joshua P. DiGangi
NASA Langley Research Center, Hampton, VA, USA
Yonghoon Choi
NASA Langley Research Center, Hampton, VA, USA
Analytical Mechanics Associates, Hampton, VA, USA
Alessandro Franchin
Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Changmin Cho
Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Eric C. Apel
Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Louisa K. Emmons
Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Nattamon Maneenoi
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA
Jason M. St. Clair
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
University of Maryland, Baltimore County, Baltimore, MD, USA
Reem A. Hannun
Earth Science Division, NASA Ames Research Center, Moffett Field, CA, USA
Glenn M. Wolfe
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Erin Delaria
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA
Abby Sebol
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Donald R. Blake
Department of Chemistry, University of California, Irvine, CA, USA
Monica Crippa
European Commission, Joint Research Centre (JTC), Ispra, Italy
Rachel M. Hoesly
Joint Global Change Research Institute, Pacific Northwest National Lab, College Park, MD, USA
Steven J. Smith
Joint Global Change Research Institute, Pacific Northwest National Lab, College Park, MD, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Jung-Hun Woo
Department of Environmental Management, Seoul National University, Seoul, South Korea
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Joint Institute for Regional System Science and Engineering (JIFRESSE), University of California, Los Angeles, Los Angeles, CA, USA
Ronald Macatangay
Center for Atmospheric Science Research, National Astronomical Research Institute of Thailand, Chiang Mai, Thailand
Short summary
Asia produces half of the world's air pollution, yet emission estimates from different sources disagree widely. We assimilated satellite observations of six pollutant species to correct both concentrations and emissions. Aircraft measurements from a 2024 NASA campaign across four Asian countries confirmed the improvements. Our results reveal underreported emissions in some countries, overestimated vegetation emissions, unresolved fire estimates, and weather control on cross-border pollution.
Asia produces half of the world's air pollution, yet emission estimates from different sources...
General Comments:
This manuscript presents a comprehensive study assimilating satellite observations of six key chemical species into the MIROC-Chem model to simultaneously optimize atmospheric concentrations and surface emissions. The assimilated results are extensively validated against the recent NASA ASIA-AQ airborne observations, demonstrating a substantial reduction in model biases. The optimized emissions reveal significant discrepancies in current bottom-up inventories for anthropogenic sources, biogenic sources, and biomass burning. Furthermore, the study provides a detailed source-receptor analysis quantifying the relative contributions of local emissions versus long-range transport to air quality in various Asian cities. Overall, the methodology is solid and robust, and the conclusions are well-supported by the analysis. The manuscript is clearly written and well-structured. I recommend publication after the following specific comments and clarifications are addressed.
Specific Comments:
1. The current inversion framework attributes all simulated concentration biases to errors in surface emissions. How can we be certain that these biases do not stem from inherent deficiencies in the model's physical or chemical mechanisms (e.g., boundary layer mixing, deposition schemes, or chemical reaction rates)? I suggest adding a brief discussion on the potential uncertainties introduced by model structural errors and how they might alias into the optimized top-down emissions.
2. Please elaborate in the methodology section on how the data assimilation system handles this strong non-linear chemical feedback (for example, the strong non-linearity in ozone chemistry). Does this non-linearity pose a risk for optimizing NOx and VOCs emissions during the assimilation process?
3. The study assimilates six gas-phase species but does not mention aerosols (e.g., PM2.5 or AOD). Asia, particularly India and Thailand during the fire season, experiences extremely high aerosol loadings. Heavy aerosol plumes can significantly alter photolysis rates. I recommend evaluating, or at least discussing, the potential errors introduced into the gas-phase photochemistry and the subsequent emission inversions due to the lack of aerosol assimilation (or unconstrained AOD).
4. How is the injection height of Southeast Asian wildfires parameterized in the model? Since the vertical distribution of emissions is critical for simulating long-range transport, did the ASIA-AQ airborne observations provide any constraints or validation for the modeled plume injection heights? Please provide more information on this in the text.
5. Line 376-385: The model scales down biogenic isoprene emissions by a striking 52%. Is this massive reduction primarily due to an overestimation of basal emission factors in the prior model (e.g., MEGAN), or is it caused by inadequate parameterization of the vegetation's response to meteorology (e.g., drought stress) for specific Asian land cover types? I suggest providing a more in-depth mechanistic explanation, possibly by linking the results to meteorological fields (e.g., temperature, PAR) and land cover data.
6. Section 5: The analysis of local and transboundary transport based on regional emission perturbation studies is highly appreciated, as it requires significant computational effort. However, the manuscript does not seem to address the non-linear interactions between emission changes from different regions. For instance, in the pie charts (e.g., Figs. 13 and 14), the sum of individual regional contributions theoretically shouldn't perfectly equal 100% due to chemical non-linearities. Are these interaction terms lumped into the "rest of SE Asia" categories? Please clarify how these non-linear interactions are accounted for in the fractional attributions.
Technical Corrections:
7. Please replace "country" with "country/region" where appropriate (for example, in the colorbar of Fig. 2h or similar figures and texts) to maintain geopolitical neutrality and adhere to standard scientific publication guidelines regarding disputed territories.