KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May-June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM<span classCombining double low line"inline-formula">2.5</span>, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical<span idCombining double low line"page6456"/> properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8-16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m<span classCombining double low line"inline-formula">2</span> g<span classCombining double low line"inline-formula">-1</span> observed average) and light-scattering enhancement factor (<span classCombining double low line"inline-formula">f</span>(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm<span classCombining double low line"inline-formula">-3</span> in the model vs. observed average of 1.5 g cm<span classCombining double low line"inline-formula">-3</span>) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %-28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation..
Bibliographical noteFunding Information:
This research has been supported by the NASA Tropospheric Composition Program and NASA grants NNX11AI52G, NNX16AD96G, NNX15AT96G, 20 NNX15AT88G, NNH15AB60I, and 80NSSC19K0124. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Jhoon Kim and Myungje Choi were supported by the program Technology development for Practical Applications of Multi-Satellite data to maritime issues funded by the Ministry of Ocean and Fisheries, Korea
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© Author(s) 2020.
All Science Journal Classification (ASJC) codes
- Atmospheric Science