Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues
Authors: Lee Kyunghwa , Yu Jinhyeok , Lee Sojin , Park Mieun , Hong Hun , Park Soon Young , Choi Myungje , Kim Jhoon , Kim Younha , Woo Jung-Hun , Kim Sang-Woo , Song Chul H. 
Published: March 2020

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).