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 to 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 are 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 GFEI but more than 2 times higher than EDGAR and CEDS in 2019. Large discrepancies in the 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 to the east of Riyadh seems to be largely overestimated by EDGAR and CEDS, while our estimates as well as GFEI and DECSO NOx indicate much lower emissions from industrial activities. On the other hand, regions like Iran, Iraq, and Oman are dominated by sources from oil and gas exploitation that probably include more irregular releases of methane, with the result that our estimates, which include only invariable sources, are lower than the bottom-up emission inventories.

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-The-Art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025°ĝ€¯×ĝ€¯0.025° covering an area surrounding the North Sea for the years 2019-2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

We present a new divergence method to estimated methane (CH₄) emissions from satellite observed mean mixing ratio of methane (XCH₄) by deriving the regional enhancement of XCH₄ in the Planetary Boundary Layer (PBL). The applicability is proven by comparing the estimated emissions with its known emission inventory from a 3-month GEOS-Chem simulation. When applied to TROPOspheric Monitoring Instrument observations, sources from well-known oil/gas production areas, livestock farms and wetlands in Texas become clearly visible in the emission maps. The calculated yearly averaged total CH₄ emission over the Permian Basin is 3.06 (2.82, 3.78) Tg a⁻¹ for 2019, which is consistent with previous studies and double that of EDGAR v4.3.2 for 2012. Sensitivity tests on PBL heights, on the derived regional background and on wind speeds suggest our divergence method is quite robust. It is also a fast and simple method to estimate the CH₄ emissions globally.