This study characterizes the degree to which current polar-orbiting satellites can evaluate the daytime change in NO 2 vertical column density (VCD) in urban, suburban, and rural areas. We examine these issues by considering the diurnal cycle of NO 2 over the United States, using the large NO 2 monitoring network supported by states, tribes, and the US Environmental Protection Agency (EPA). Through this analysis, we identify the potential opportunities and limitations of current space-based NO 2 data in capturing diurnal change. Ground-based monitoring data from the US EPA are compared with satellite retrievals of NO 2 from the KNMI Tropospheric Emissions Monitoring Internet Service (TEMIS) for two instruments: GOME-2 with a mid-morning overpass, and OMI with an early afternoon overpass. Satellite data show evidence of higher morning NO 2 in the vicinity of large urban areas. Both satellites and ground monitors show ∼1.5-2x greater NO 2 abundance between morning and afternoon in urban areas. Despite differences in horizontal resolution and overpass time, the two satellite retrievals show similar agreement with ground-based NO 2 measurements. When analyzed on a pixel-by-pixel basis, we find evidence for spatial structure in the diurnal change in NO 2 between city center and surrounding areas in Southern California. Wider analysis of urban-suburban structure in diurnal NO 2 change is hindered by resolution differences in the two satellite instruments, which have the potential to create data artefacts. This study highlights the value of future geostationary instruments to provide comparable satellite retrievals for NO 2 over the course of a day, and research needs related to the effective utilization of NO 2 satellite data for air quality applications.

Abstract. We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as prior estimate the national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to 0.25° × 0.3125° resolution. The optimization is done analytically assuming lognormally distributed errors on prior emissions. Errors and information content on the optimal estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our optimal estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a-1, where parentheses indicate uncertainty range. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a-1 for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21 % higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a-1), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (-15 %). It is also higher than previous inverse studies (43–62 Tg a-1) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 (1.3–1.9) %, which would imply net climate benefits from the current coal-to-gas energy transition in China. However, this small loss rate is somewhat misleading considering China’s high gas imports, including from Turkmenistan where emission per unit of gas production is very high.

Abstract. We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25° × 0.3125° (≈ 25 × 25 km2) resolution by inversion of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of emissions on the 0.25° × 0.3125° grid including error statistics, information content, and visualization code for inspection of results. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI’s capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.