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An improved divergence method has been developed to estimate annual methane (CH4) emissions from TROPOspheric Monitoring Instrument (TROPOMI) observations. It has been applied to the period of 2018 to 2021 over the Middle East, where the orography is complicated, and the mean mixing ratio of methane (XCH4) might be affected by albedos or aerosols over some locations. To adapt to extreme changes of terrain over mountains or coasts, winds are used with their divergent part removed. A temporal filter is introduced to identify highly variable emissions and further exclude fake sources caused by retrieval artifacts. We compare our results to widely used bottom-up anthropogenic emission inventories: Emissions Database for Global Atmospheric Research (EDGAR), Community Emissions Data System (CEDS) and Global Fuel Exploitation Inventory (GFEI) over several regions representing various types of sources. The NOX emissions from EDGAR and Daily Emissions Constrained by Satellite Observations (DECSO), and the industrial heat sources identified by Visible Infrared Imaging Radiometer Suite (VIIRS) are further used to better understand our resulting methane emissions. Our results indicate possibly large underestimations of methane emissions in metropolises like Tehran (up to 50 %) and Isfahan (up to 70 %) in Iran. The derived annual methane emissions from oil/gas production near the Caspian Sea in Turkmenistan are comparable to GEFI but more than two times higher than EDGAR and CEDS in 2019. Large discrepancies of distribution of methane sources in Riyadh and its surrounding areas are found between EDGAR, CEDS, GFEI and our emissions. The methane emission from oil/gas production in the east to Riyadh seems to be largely overestimated by EDGAR and CEDS, while our estimates, and also GFEI and DECSO NOX indicate much lower emissions from industry activities. On the other hand, regions like Iran, Iraq, and Oman are dominated by sources from oil and gas exploitation that probably includes more irregular releases of methane, with the result that our estimates, that include only invariable sources, are lower than the bottom-up emission inventories. [ABSTRACT FROM AUTHOR]

The sensitivity of satellites to air pollution close to the sea surface is decreased by scattering of light in the atmosphere and low sea surface albedo. To reliably retrieve tropospheric nitrogen dioxide (NO2) columns using the TROPOspheric Monitoring Instrument (TROPOMI), it is therefore necessary to have good a priori knowledge of the vertical distribution of NO2. In this study, we use an aircraft of the Royal Belgian Institute of Natural Sciences, which was already equipped with a sniffer sensor system, measuring NO x (= NO + NO2), CO2 and SO2. This instrumentation enables us to evaluate vertical profile shapes from several chemical transport models and to validate TROPOMI tropospheric NO2 columns over the polluted North Sea in the summer of 2021.We observe multiple clear signatures of ship plumes from seconds after emission to multiple kilometers downwind. Besides that, our results show that the chemical transport model TM5-MP, which is used in the retrieval of the operational TROPOMI NO2 data, tends to underestimate surface level pollution while overestimating NO2 at higher levels over the study region. The higher horizontal resolution in the regional CAMS ensemble mean and LOTOS-EUROS model improve the surface level pollution estimates, but the models still systematically overestimate NO2 levels at higher altitudes, indicating exaggerated vertical mixing in the models over the North Sea. When replacing the TM5 a priori NO2 profiles with the aircraft-measured NO2 profiles in the air mass factor (AMF) calculation, we find smaller recalculated AMFs. Subsequently, the retrieved NO2 columns increase by 20 %, indicating a significant negative bias in the operational TROPOMI NO2 data product (up to v2.3.1) over the North Sea. This negative bias has important implications for estimating emissions over the sea. While TROPOMI NO2 negative biases caused by the TM5 a priori profiles have also been reported over land, the reduced vertical mixing and smaller surface albedo over sea makes this issue especially relevant over sea and coastal regions. [ABSTRACT FROM AUTHOR]

Abstract: Top-down CO2 emission estimates based on satellite observations are potentially of great importance for independently verifying the accuracy of reported emissions and emission inventories. Difficulties in verifying these satellite-derived emissions arise from the fact that emission inventories often provide annual mean emissions while estimates from satellites are available only for a limited number of overpasses. Previous studies have derived CO2 emissions for power plants from OCO-2 and OCO-3 observations of their exhaust plumes, but the accuracy and the factors affecting these emissions are uncertain. We have selected only isolated power plants for this study, to avoid complications link to multiple sources in close proximity. We first compare the Gaussian plume model and cross-sectional flux methods for estimating CO2 emission of power plants. Then we examined the sensitivity of the emission estimates to possible choices for the wind field. For verification we have used power plant emissions that are reported on an hourly basis by the Environmental Protection Agency (EPA) in the United States. By using the OCO-2 and OCO-3 observations over the past four years we identified emission signals of isolated power plants and arrived at a total of 50 collocated cases involving 22 power plants. We correct for the time difference between the moment of the emission and the satellite observation. We found the wind field halfway the height of planetary boundary layer (PBL) yielded the best results. We found that the instantaneous satellite estimated emissions of these 50 cases and reported emissions display a weak correlation (R2=0.12). The correlation improves with averaging over multiple observations of the 22 power plants (R2=0.40). The method was subsequently applied to 106 power plants cases worldwide yielded a total emission of 1522 ± 501 Mt CO2/year, estimated to be about 17 % of the power sector emissions of our selected countries. The improved correlation highlights the potential for future planned satellite missions with a greatly improved coverage to monitor a significant fraction of global power plant emissions. [ABSTRACT FROM AUTHOR]