The renewed increase in atmospheric methane (CH₄) concentrations since 2007, culminating in record growth rates in 2021, poses a critical challenge to achieving global climate targets. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite provides unprecedented daily global observations of CH₄ at a spatial resolution (7 × 7 km², improved to 5.5 × 7 km² since August 2019), enabling substantial advances in space-based CH₄ monitoring and emission quantification. Here, we synthesize and categorize 133 published studies spanning global, regional and local scales and covering both anthropogenic and natural CH₄ sources. Collectively, these studies demonstrate TROPOMI's capability to quantify emissions across diverse spatiotemporal scales, as well as its synergy with other satellite instruments for detecting and attributing facility-level sources, such as fossil fuel infrastructure and landfills. However, emission estimates remain challenged by uncertainties in column-averaged CH4 (XCH₄) retrievals related to surface albedo effects and persistent cloud cover, particularly in tropical and high-latitude regions. These limitations can be mitigated through improved retrieval algorithms, refined quality filtering, multisatellite fusion, and integration with ground-based observations and airborne campaigns. Furthermore, we assess the suitability of different quantification approaches for specific source types, such as Gaussian plume models for large isolated emitters and inverse modeling for spatially diffuse emissions. Finally, we outline key methodological priorities and opportunities in the context of the recent MetOp-SG-A satellite, which will complement TROPOMI with a morning overpass. By consolidating current applications of TROPOMI XCH₄ observations, this review provides guidance for enhancing space-based methane monitoring and supports targeted mitigation strategies aligned with achieving Sustainable Development Goal 13.

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.

Satellite retrieval of vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO₂) is critical for NO_x pollution and impact evaluation. For regions with high aerosol loadings, the retrieval accuracy is greatly affected by whether aerosol optical effects are treated implicitly (as additional effective clouds) or explicitly, among other factors. Our previous POMINO algorithm explicitly accounts for aerosol effects to improve the retrieval, especially in polluted situations over China, by using aerosol information from GEOS-Chem simulations with further monthly constraints by MODIS/Aqua aerosol optical depth (AOD) data. Here we present a major algorithm update, POMINO v1.1, by constructing a monthly climatological dataset of aerosol extinction profiles, based on level 2 CALIOP/CALIPSO data over 2007-2015, to better constrain the modeled aerosol vertical profiles. We find that GEOS-Chem captures the month-to-month variation in CALIOP aerosol layer height (ALH) but with a systematic underestimate by about 300-600 m (season and location dependent), due to a too strong negative vertical gradient of extinction above 1 km. Correcting the model aerosol extinction profiles results in small changes in retrieved cloud fraction, increases in cloud-top pressure (within 2 %-6 % in most cases), and increases in tropospheric NO₂ VCD by 4 %-16 % over China on a monthly basis in 2012. The improved NO₂ VCDs (in POMINO v1.1) are more consistent with independent ground-based MAX-DOAS observations (R²=0.80, NMB =-3.4 %, for 162 pixels in 49 days) than POMINO (R²=0.80, NMB =-9.6 %), DOMINO v2 (R²=0.68, NMB =-2.1 %), and QA4ECV (R²=0.75, NMB =-22.0 %) are. Especially on haze days, R² reaches 0.76 for POMINO v1.1, much higher than that for POMINO (0.68), DOMINO v2 (0.38), and QA4ECV (0.34). Furthermore, the increase in cloud pressure likely reveals a more realistic vertical relationship between cloud and aerosol layers, with aerosols situated above the clouds in certain months span id=page2 instead of always below the clouds. The POMINO v1.1 algorithm is a core step towards our next public release of the data product (POMINO v2), and it will also be applied to the recently launched S5P-TROPOMI sensor.