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We use 2011-2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km² resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R² = 0.89) with single-value precision of 26-32% depending on country. Prediction of annual mean values has R² = 0.96 and single-value precision of 12%. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015-2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015-2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource. [ABSTRACT FROM AUTHOR]

For the purpose of providing reliable and robust air quality predictions, an operational 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 Multi-scale 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, nitrate, ammonium, 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 operational 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 (IOA = 0.60; 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 operational 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 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 (MLH) 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 three-dimensional variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean operational air quality forecasting system. [ABSTRACT FROM AUTHOR]

Retrievals of Total Column NO2 (TCNO2) are compared for 14 sites from the Ozone Measuring Instrument (OMI using OMNO2-NASA v3.1) on the AURA satellite and from multiple ground-based PANDORA spectrometer instruments making direct-sun measurements. Six of these sites with multi-year PANDORA data records are in the Northern Hemisphere (Busan, Seoul, Washington DC, Waterflow New Mexico, Boulder Colorado, and Mauna Loa) and one site in the Southern Hemisphere (Buenos Aires Argentina). The first four of these sites and Buenos Aires frequently have high TCNO2 (TCNO2 > 0.5 DU) and are likely have significant air quality problems that can affect human health. Eight additional sites have shorter term data records in the US and South Korea. One of these is a one-year data record from a highly polluted site at City College in New York City with pollution levels comparable to Seoul, South Korea. The result is that on a weekly or monthly average basis, OMI almost always underestimates the amount TCNO2 by 50 to 100 %, while occasionally the daily OMI value exceeds that measured by PANDORA at very clean sites. OMI estimated air mass factor, surface reflectivity, and the OMI 24 x 13 km² FOV (field of view) are three factors that can cause OMI to underestimate TCNO2. Because of the local inhomogeneity of NO2 emissions, the large OMI FOV is the most likely factor when comparing OMI TCNO2 to retrievals from the small PANDORA effective FOV calculated from the solar diameter of 0.5°. As part of air quality assessments, OMI always misses the frequently much higher values of TCNO2 that occur after the OMI overpass time. [ABSTRACT FROM AUTHOR]