Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia...

Pendergrass, D. C., S. Zhai, J. Kim, J. Koo, S. Lee, M. Bae, S. Kim, H. Liao, and D. Jacob (2022), Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6 × 6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data, Atmos. Meas. Tech., 15, 1075-1091, doi:10.5194/amt-15-1075-2022.

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 a continuous 6 × 6 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, including information encoded in GOCI AOD retrieval failure and trained with PM2.5 observations from the three national networks. The predicted 24 h GOCI PM2.5 concentrations for sites entirely withheld from training in a 10-fold cross-validation procedure correlate highly with network observations (R 2 = 0.89) with a single-value precision of 26 %– 32 %, depending on the country. Prediction of the annual mean values has R 2 = 0.96 and a single-value precision of 12 %. GOCI PM2.5 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 GOCI 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 GOCI PM2.5 fields for South Korea identifies hot spots 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 the monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations in PM2.5 , even though AOD and PM2.5 often have opposite seasonalities. The 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 and spatial patterns on the 6 km scale. A comparison to a Community Multiscale Air Quality (CMAQ) simulation for the Korean peninsula demonstrates the value of the continuous GOCI PM2.5 fields for testing air quality models, including over North Korea, where they offer a unique resource.

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Research Program: 
Atmospheric Composition Modeling and Analysis Program (ACMAP)