A case study of surface ozone source contributions in the Seoul metropolitan area using the adjoint of CMAQ

ABSTRACT

To quantitatively investigate the transboundary behaviors and source attributions of ozone (O₃) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ modeling system (the CMAQ adjoint). Our focus is on the Seoul Metropolitan Area (SMA) in South Korea, which is the receptor region of this study. We examine the contributions of both local and transported emissions to an O₃ exceedance episode observed on June 3, 2019, estimating up to four days in advance. By using the CMAQ adjoint, we can determine the sensitivity of O₃ remaining in the SMA to changes in O₃ precursor emissions (emissions-based sensitivity) and concentrations (concentrations-based sensitivity) along the long-range transport pathways and emission source regions overseas. These include Beijing-Tianjin-Hebei (BTH), Shandong, Yangtze River Delta (YRD), and Central China. CMAQ adjoint-derived source attributions suggest that overseas precursor emissions and O₃ contributed significantly to the O₃ exceedance event in SMA. The emissions-based sensitivities revealed that precursor emissions originating from Shandong, YRD, Central China, and BTH contributed 11.42 ppb, 4.28 ppb, 1.24 ppb, 0.9 ppb, respectively, to the O₃ exceedance episode observed in the SMA. Meanwhile, Korean emissions contributed 31.1 ppb. Concentrations-based sensitivities indicated that 19.3 ppb of contributions originated in regions beyond eastern China and directly affected the O₃ level in the SMA in the form of background O₃. In addition to capturing the transboundary movements of air parcels between the source and receptor regions, we performed HYSPLIT backward trajectory analyses. The results align with the trajectories of O₃ and its precursors that we obtained from the adjoint method. This study represents a unique effort in employing the adjoint technique to examine the impacts of regional O₃ on South Korea, utilizing a combination of emissions-based and concentrations-based sensitivities.

Implications: This research brings to light the critical role of both local and regional precursor emissions in contributing to an ozone (O₃) exceedance event in the Seoul Metropolitan Area (SMA), South Korea. Utilizing the CMAQ adjoint technique, a novel approach in the context of South Korea’s O₃ investigations, we were able to delineate the quantitative contributions of different regions, both within South Korea and from overseas areas such as Beijing, Shandong, Shanghai, and Central China. Importantly, the results underscore the substantial influence of transboundary pollutant transport, emphasizing the need for international collaboration in addressing air quality issues. As metropolitan areas around the globe grapple with similar challenges, the methodology and insights from this study offer a potent tool and framework for regions seeking to understand and mitigate the impacts of O₃ on human health and the environment. By integrating different sensitivity types, coupled with HYSPLIT backward trajectory analyses, this research equips policymakers with comprehensive data to design targeted interventions, emphasizing the significance of collaborative efforts in tackling regional air pollution challenges. However, it’s important to note the limitation of this study, which is a case study conducted over a short time period. This constraint may impact the generalizability of the findings and suggests a need for further research to validate and expand upon these results.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data is available upon request from the corresponding author.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10962247.2024.2361021.

Additional information

Funding

This research was partially supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT [2020M3G1A1114617]. The authors acknowledge the supersite PM measurement data provided by NIER South Korea (project code: NIER-2021-03-03-001). We are grateful for the support of the Research Computing Data Core at the University of Houston for assistance with the calculations carried out in this work.

Notes on contributors

Arash Kashfi Yeganeh

Arash Kashfi Yeganeh is a PhD candidate in atmospheric sciences at the University of Houston. His research focuses on numerical modeling of air quality.

Mahmoudreza Momeni

Mahmoudreza Momeni is a PhD candidate in atmospheric sciences at the University of Houston. His research focuses on numerical modeling of air quality.

Yunsoo Choi

Yunsoo Choi is a Professor of Atmospheric Sciences and leads the Air Quality Forecasting (AQF) and Machine Learning group at the University of Houston. His research encompasses numerical modeling, AI and deep learning modeling, and digital twin development in various domains, including air quality, weather, climate change, energy usage, and remote sensing applications.

Jincheol Park

Jincheol Park is a PhD candidate in atmospheric sciences at the University of Houston. His research focuses on numerical modeling of air quality.

Jia Jung

Jia Jung is a research scientist at NASA Ames Research Center. She specializes in data assimilation and inverse modeling techniques for applications in air quality and climate studies.