Satellite aerosol optical depth (AOD) data assimilation (DA) using numerical air quality forecast models has shown a limited improvement due to large uncertainties in the AOD observation operator. This study employed a machine learning (ML) algorithm to estimate the ground‐level particulate matter (PM) from the Geostationary Ocean Color Imager (GOCI) AOD through the random forest with high accuracy. Analysis fields were subsequently produced by applying PM estimations to the Weather Research and Forecasting‐Chemistry/three‐dimensional variational DA system. Initialization of the model with the new analysis remarkably reduced the analysis error and increased the forecast skill. The PM₁₀ prediction showed significant benefits for up to 24 forecast hours, whereas PM_2.5 prediction was improved for up to six forecast hours. Considering a broad spatial coverage by satellites, the synergistic use of DA and ML can maximize the effectiveness of satellite DA for air quality forecasts at the ground.

This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea–US Air Quality (KORUS-AQ) intensive observing period. Observing system experiments (OSEs) are conducted to examine the changes in quality of assimilation and forecast skills sensitive to the assimilated observational input data. The baseline model simulation underestimates AOD and surface PM concentration in most regions, in which the assimilation of satellite and in-situ data improves the mean biases and spatial distribution. Moreover, it improves the forecast skill of the surface concentration of PM₁₀ and PM_2.5. The results from the OSEs indicate that the assimilation of GOCI AOD only slightly enhances the forecast skill. However, most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The marginal improvement in the PM₁₀ forecasts by the GOCI AOD suggests the non-negligible difference between column-representing AOD and the surface PM concentration.

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM_2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM_2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM_2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

A numerical simulation system is developed to predict the dispersion of hazardous air pollutants (HAPs) over a populated city due to accidental release. Ulsan, as one of the megacities in Korea, is chosen as an ideal testbed for the simulation, as it is located in complex terrain and hosts a national industrial complex on the outskirts of the city. The system is based on the California puff model (CALPUFF) for simulating a HAP’s dispersion, in which the three-dimensional atmospheric circulation derived from the observed weather station data is specified at a fine horizontal resolution of 200 m. A test scenario is developed for the accidental release of benzene during the daytime and nighttime, respectively, by a fictitious explosion of a storage container, and the injection amount is determined arbitrarily yet comparable to those in the past accidents. In attempting a quantitative assessment and zoning the level of potential risk over the impacted area, multiple simulations have been conducted each day with different hourly varying meteorological conditions in August. The dispersion characteristics of the air pollutant depend largely on the local wind patterns that vary substantially from day to day. Nevertheless, the composite analysis sufficiently identifies the impacted area by the HAP’s dispersion due to the local prevailing wind such as the land–sea breeze circulation. An immediate hazardous area is determined based on the vulnerability map constructed by zoning the level of risk determined by the spatial distribution of the HAPs’ concentration and the harmfulness standard to the human body.