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<p>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 (PM<span class="inline-formula">₁₀</span>, PM<span class="inline-formula">_2.5</span>, CO, <span class="inline-formula">O₃</span> and <span class="inline-formula">SO₂</span>). 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 (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">SO</mi><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="29pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="28cd4f8c12cf9ef751545712573a522a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00001.svg" width="29pt" height="17pt" src="gmd-13-1055-2020-ie00001.png"/></svg:svg></span></span>), nitrate (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="25pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="a33a7d42b70ca1fe513ac92c5832eec2"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00002.svg" width="25pt" height="16pt" src="gmd-13-1055-2020-ie00002.png"/></svg:svg></span></span>), ammonium (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NH</mi><mn mathvariant="normal">4</mn><mo>+</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="24pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="3226c502fdca30fe88bf9305df4b3716"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00003.svg" width="24pt" height="15pt" src="gmd-13-1055-2020-ie00003.png"/></svg:svg></span></span>), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, <span class="inline-formula">O₃</span> and <span class="inline-formula">SO₂</span>). 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 PM<span class="inline-formula">₁₀</span> (index of agreement IOA <span class="inline-formula">=0.60</span>; mean bias MB <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M12" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>=</mo><mo>-</mo><mn mathvariant="normal">13.54</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="47pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="0e61fe4ba3975d982d187bcfdb907efb"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00004.svg" width="47pt" height="10pt" src="gmd-13-1055-2020-ie00004.png"/></svg:svg></span></span>) and PM<span class="inline-formula">_2.5</span> (IOA <span class="inline-formula">=0.71</span>; MB <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>=</mo><mo>-</mo><mn mathvariant="normal">2.43</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="41pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="8eb0fc81e3dd3b118873e4e8633e81a8"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00005.svg" width="41pt" height="10pt" src="gmd-13-1055-2020-ie00005.png"/></svg:svg></span></span>) as compared to the BASE RUN for PM<span class="inline-formula">₁₀</span> (IOA <span class="inline-formula">=0.51</span>; MB <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>=</mo><mo>-</mo><mn mathvariant="normal">27.18</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="47pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="c125769e230eabddd32a58d6d35f16ea"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00006.svg" width="47pt" height="10pt" src="gmd-13-1055-2020-ie00006.png"/></svg:svg></span></span>) and PM<span class="inline-formula">_2.5</span> (IOA <span class="inline-formula">=0.67</span>; MB <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M21" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>=</mo><mo>-</mo><mn mathvariant="normal">9.9</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="35pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="2a96f4332ab1beb03adc40567f055e72"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-13-1055-2020-ie00007.svg" width="35pt" height="10pt" src="gmd-13-1055-2020-ie00007.png"/></svg:svg></span></span>). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of <span class="inline-formula">−0.27</span> (BASE RUN) was greatly enhanced to <span class="inline-formula">−0.036</span> (DA RUN). In the cases of <span class="inline-formula">O₃</span> and <span class="inline-formula">SO₂</span>, 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 <span class="inline-formula">NO₂</span> mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, <span class="inline-formula">HNO₃</span> and peroxyacetyl nitrates – PANs – in the ground-observed <span class="inline-formula">NO₂</span> mixing ratios). It was also discussed that, in order to ensure accurate nocturnal<span id="page1056"/> 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).</p>