Preprints
https://doi.org/10.5194/gmd-2022-253
https://doi.org/10.5194/gmd-2022-253
Submitted as: development and technical paper
25 Jan 2023
Submitted as: development and technical paper | 25 Jan 2023
Status: this preprint is currently under review for the journal GMD.

Dynamic Meteorology-Induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis

Bok H. Baek1, Carlie Coats1, Siqi Ma1,2, Chi-Tsan Wang1, Jia Xing1, Daniel Tong1,2, Soontae Kim4, and Jung-Hun Woo3 Bok H. Baek et al.
  • 1Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
  • 2Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USA
  • 3Civil and Environmental Engineering, College of Engineering, Konkuk University, Seoul, Republic of Korea
  • 4Environmental Engineering, College of Engineering, Ajou University, Suwon, Republic of Korea

Abstract. The main focus of this study is to develop a dynamic-coupling “inline” air quality modeling system for the meteorology-induced emissions with simulated meteorological data. To improve the spatiotemporal representations and accuracy of onroad vehicle emissions, which are largely senstivie to local meteorology, we developed the “inline” coupler module called “MetEmis” for Meteorology-Induced Emission sources within the Community Multiscale Air Quality (CMAQ) version 5.3.2 modeling system. It can dynamically estimate meteorology-induced hourly gridded emissions within the CMAQ modeling system using modeled meteorology. The CMAQ air quality modeling system is applied over the continental U.S. for two months (January and July 2019) for two emissions scenarios: a) current “offline” based onroad vehicle emissions, and b) “inline” CMAQ-MetEmis onroad vehicle emissions. Overall, the “MetEmis” coupler allows us to dynamically simulate onroad vehicle emissions from the MOVES onroad emission model for CMAQ with a better spatio-temporal representation compared to the “offline” scenario based on static temporal profiles. With an instance interpolation calculation approach, the new “inline” approach significantly enhances the computational efficiency and accuracy of estimating mobile source emissions, compared to the existing “offline” approach that yields almost identical hourly emission estimation. The domain total of daily VOC emissions from the “inline” scenario shows the largest impacts from the local meteorology, which is approximately 10 % lower than the ones from the “offline” scenario. Especially, the major difference of VOC estimates was shown over the California region. These local meteorology impacts on onroad vehicle emissions via CMAQ-MetEmis revealed an improvement in hourly NO2, daily maximum ozone, and daily average PM2.5 patterns with a higher agreement and correlation with daily ground observations.

Bok H. Baek et al.

Status: open (until 22 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Bok H. Baek et al.

Bok H. Baek et al.

Viewed

Total article views: 107 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
86 17 4 107 1 1
  • HTML: 86
  • PDF: 17
  • XML: 4
  • Total: 107
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 25 Jan 2023)
Cumulative views and downloads (calculated since 25 Jan 2023)

Viewed (geographical distribution)

Total article views: 107 (including HTML, PDF, and XML) Thereof 107 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Jan 2023
Download
Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an “inline” meteorology-induce emissions coupler module within the US EPA’s CMAQ modeling system, called “Meteorologically-induced anthropogenic Emissions: CMAQ-MetEmis”, to dynamically model the complex MOVES onroad mobile emissions inline without a separate dedicated emissions processing model like SMOKE.