Abstract

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).

Highlights

  • Air quality has long been considered an important issue in climate change, visibility and public health, and it is strongly dependent upon meteorological conditions, emissions and the transport of air pollutants

  • The air quality prediction system was developed by preparing the initial conditions (ICs) for Community Multiscale Air Quality (CMAQ) model simulations using Geostationary Ocean Color Imager (GOCI) aerosol optical depths (AODs) and ground-based observations of PM10, carbon monoxide (CO), ozone and SO2 during the period of the KORUS-AQ campaign (1 May–12 June 2016) in South Korea

  • The major advantages of the developed air quality prediction system are its comprehensiveness in predicting the ambient concentrations of both gaseous and particulate species and its powerfulness in terms of computational cost

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Summary

Introduction

Air quality has long been considered an important issue in climate change, visibility and public health, and it is strongly dependent upon meteorological conditions, emissions and the transport of air pollutants. Air quality predictions are another crucial element for protecting public health through the forecasting of high air pollution episodes in advance and alerting citizens about these high episodes In this context, reliable and robust air quality forecasts are necessary to avoid any confusion caused by poor predictions given by CTM simulations. In the present study, the air quality prediction system named as Korean Air Quality Prediction System version 1 (KAQPS v1) was developed by preparing ICs via DA for the Community Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006; Byun and Ching, 1999) using satellite- and ground-based observations for particulate matter (PM) and atmospheric gases such as CO, O3 and SO2.

Methodology
WRF model simulations
CMAQ model simulations
Satellite-based observations
Ground-based observations
Air quality prediction system
AOD calculations
Results and discussions
Time-series analysis
Spatial distribution
Statistical analysis
Sensitivity test of DA time interval
PM and gases
Summary and conclusions
Full Text
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