Abstract
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.
Highlights
35 Exposure to outdoor fine particulate matter (PM2.5) is a global public health issue, accounting for 8.9 million deaths in 2015 [Burnett et al, 2018]
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 15 and trained with PM2.5 observations from the three national networks
1, we considered as additional variables the population density, the GOCI fine mode fraction (FMF), and the GOCI multiple prognostic expected error (MPEE), but we found that they worsened accuracy of the fit and so we did not retain them
Summary
35 Exposure to outdoor fine particulate matter (PM2.5) is a global public health issue, accounting for 8.9 million deaths in 2015 [Burnett et al, 2018]. 45 (ML) algorithm to provide continuous long-term reliable mapping of 24-h PM2.5 at 6x6 km spatial resolution. Approaches to relate AOD observations to surface PM2.5 used chemical transport models (CTMs) to estimate local PM2.5/AOD ratios [Liu et al, 2004; van Donkelaar et al, 2006], with more recent studies adding ancillary satellite data on the vertical distribution of aerosol extinction [Geng et al, 2015; van Donkelaar et al, 2016; 55 van Donkelaar et al, 2019]. Other approaches have used PM2.5 network data to infer PM2.5/AOD ratios [Wang and Christopher, 2003], with statistical models based on meteorological and land-use predictor variables to enable spatial extrapolation [Gupta and Christopher, 2009; Liu et al, 2009; Kloog et al, 2012; 2014]. We apply a RF algorithm to 2011-2019 GOCI AOD data to construct a continuous dataset of 24-h PM2.5 concentrations at 6x6 km resolution for eastern China, South Korea, and Japan trained with the surface network data. We exploit the continuity of the dataset to determine trends of PM2.5 air quality in East Asia over the past half decade
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