Interpretation of geostationary satellite aerosol optical depth (AOD) over East Asia in relation to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and seasonality

Abstract

Abstract. Geostationary satellite sensors over East Asia (GOCI and AHI) are now providing continuous mapping of aerosol optical depth (AOD) at 550 nm to improve monitoring of fine particulate matter (PM2.5) air quality. Here we evaluate our understanding of the physical relationships between AOD and PM2.5 over East Asia by using the GEOS-Chem atmospheric chemistry model to simulate observations from multiple sources: 1) the joint NASA-NIER Korea – United States Air Quality aircraft campaign over South Korea (KORUS-AQ; May–June 2016); 2) AODs from the AERONET ground-based network; 3) AOD from a new GOCI/AHI fused product; and 4) surface PM2.5 networks in South Korea and China. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate-nitrate-ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and the high hygroscopicity of the PBL aerosols. Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. A broader analysis of the GOCI/AHI AOD data over East Asia in different seasons shows agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter, and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation of PBL depth and lack of residential coal emissions. Physical interpretation of the satellite AOD data in terms of surface PM2.5 is sensitive to accurate information on aerosol size distributions, PBL depths, RH, the role of coarse particles, and diurnal variation of PM2.5.

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

“…Figure 1 shows the validation example of all MLR models (M1, M2-1, M2-2, M3-1, and M3-2) using AODs retrieved from the MODIS-DT algorithm. Considering the reverse pattern of AOD and PM 2.5 in East Asia [38], the relationship between observed and estimated PM 2.5 from the M1 model (i.e., only AOD is considered) is acceptable, having a linear shape and moderate correlation coefficient (R). However, the slope was 0.15, indicating that the estimated PM 2.5 cannot follow the variation range of observed PM 2.5 .…”

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

“…al., 2006;. Here we use scaled monthly mean GEOS-Chem AODs from a simulation by Zhai et al [2021] for 2016 in East Asia with 0.5 o x 0.625 o resolution. That simulation reported a low mean bias relative to AERONET; we correct this for each year in the study period by using annual mean GOCI AODs on the 6x6 km 2 grid.…”

“…The three rapidly decreasing spots on the southern coast are Gwangyang, Sacheon, and Changwon, which house industrial complexes related to the South Korean shipbuilding industry that has recently declined [Jung-a 2016]. AOD and PM2.5 in East Asia tend to have opposite seasonalities driven by boundary layer depth and RH [Zhai et al, 2021]. 2015-2019 but also shows that winter and summer PM2.5 in the region have opposite and roughly equal trends, with winter growing more polluted while summers become cleaner.…”

Parameterization of Size of Organic and Secondary Inorganic Aerosol for Efficient Representation of Global Aerosol Optical Properties

“…term aerosol composition and scattering measurements provided by the IMPROVE network across the continental U.S. (Latimer & Martin, 2019). However, subsequent analysis by Zhai et al (2021) found that this aerosol size underestimated the aerosol mass scattering efficiency and the aerosol extinction coefficients during an aircraft campaign over South Korea (KORUS-AQ). Thus, neglect of aerosol microphysical processes that shape aerosol size distributions can be a significant source of uncertainty in aerosol optical properties in a CTM.…”

“…Methods are needed to better represent aerosol size in CCMs or CTMs without a microphysics scheme (referred to as bulk models). These bulk models usually use prescribed relationships to obtain size-resolved aerosol properties (Croft et al, 2005;Karydis et al, 2011;Zhai et al, 2021), which may insufficiently represent the temporal and spatial variation (Kodros & Pierce, 2017). For example, in the GEOS-Chem CTM, a fixed dry aerosol geometric mean radius (Rg) is assumed for organic matter (OM) and sulfate-nitrate-ammonium (SNA), which is based on analysis of long-https://doi.org/10.5194/egusphere-2022-1292 Preprint.…”