Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung Hun Woo, Sang Woo Kim, Chul H. Song

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May-12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA = 0.60; mean bias MB = -13.54) and PM2.5 (IOA = 0.71; MB = -2.43) as compared to the BASE RUN for PM10 ((IOA = 0.51; MB = -27.18) and PM2.5 (IOA = 0.67; MB = -9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of -0.27 (BASE RUN) was greatly enhanced to -0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates - PANs - in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

Original languageEnglish
Pages (from-to)1055-1073
Number of pages19
JournalGeoscientific Model Development
Volume13
Issue number3
DOIs
Publication statusPublished - 2020 Mar 10

Bibliographical note

Funding Information:
Financial support. This research was supported by the National

Funding Information:
Strategic Project-Fine Particle of the National Research Foundation of Korea (NRF) of the Ministry of Science and ICT (MSIT), the Ministry of Environment (MOE), and the Ministry of Health and Welfare (MOHW) (grant no. NRF-2017M3D8A1092022). This work was also funded by the GEMS program of the MOE of the Republic of Korea as part of the Eco-Innovation Program of KEITI (grant no. 2012000160004) and was supported by a grant from the National Institute of Environment Research (NIER), funded by the MOE of the Republic of Korea (grant no. NIER-2019-01-01-028).

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Earth and Planetary Sciences(all)

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