Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

Comparison of Total Column and Surface Mixing Ratio of Carbon Monoxide Derived from the TROPOMI/Sentinel-5 Precursor with In-Situ Measurements from Extensive Ground-Based Network over South Korea

Atmospheric carbon monoxide (CO) significantly impacts climate change and human health, and has become the focus of increased air quality and climate research. Since 2018, the Troposphere Monitoring Instrument (TROPOMI) has provided total column amounts of CO (CTROPOMI) with a high spatial resolution to monitor atmospheric CO. This study compared and assessed the accuracy of CTROPOMI measurements using surface in-situ measurements (SKME) obtained from an extensive ground-based network over South Korea, where CO level is persistently affected by both local emissions and trans-boundary transport. Our analysis reveals that the TROPOMI effectively detected major emission sources of CO over South Korea and efficiently complemented the spatial coverage of the ground-based network. In general, the correlations between CTROPOMI and SKME were lower than those for NO2 reported in a previous study, and this discrepancy was partly attributed to the lower spatiotemporal variability. Moreover, vertical CO profiles were sampled from the ECMWF CAMS reanalysis data (EAC4) to convert CTROPOMI to surface mixing ratios (STROPOMI). STROPOMI showed a significant underestimation compared with SKME by approximately 40%, with a moderate correlation of approximately 0.51. The low biases of STROPOMI were more significant during the winter season, which was mainly attributed to the underestimation of the EAC4 CO at the surface. This study can contribute to the assessment of satellite and model data for monitoring surface air quality and greenhouse gas emissions.

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

Global coupled chemistry-climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea-United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42% in the control run and by 12% with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80% for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O₃ datasets and observationally constrained box model simulations of OH and HO₂. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29% for CO, 18% for ozone, 11% for HO₂, and 27% for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O₃, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO _x controls, can improve ozone pollution over East Asia.

Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997

In September and October 2015 widespread forest and peatland fires burned over large parts of maritime southeast Asia, most notably Indonesia, releasing large amounts of terrestrially-stored carbon into the atmosphere, primarily in the form of CO2, CO and CH4. With a mean emission rate of 11.3 Tg CO2 per day during Sept-Oct 2015, emissions from these fires exceeded the fossil fuel CO2 release rate of the European Union (EU28) (8.9 Tg CO2 per day). Although seasonal fires are a frequent occurrence in the human modified landscapes found in Indonesia, the extent of the 2015 fires was greatly inflated by an extended drought period associated with a strong El Niño. We estimate carbon emissions from the 2015 fires to be the largest seen in maritime southeast Asia since those associated with the record breaking El Niño of 1997. Compared to that event, a much better constrained regional total carbon emission estimate can be made for the 2015 fires through the use of present-day satellite observations of the fire's radiative power output and atmospheric CO concentrations, processed using the modelling and assimilation framework of the Copernicus Atmosphere Monitoring Service (CAMS) and combined with unique in situ smoke measurements made on Kalimantan.