We present a new product with explicit aerosol corrections, POMINO-TROPOMI, for tropospheric nitrogen dioxide (NO₂) vertical column densities (VCDs) over East Asia, based on the newly launched TROPOspheric Monitoring Instrument with an unprecedented high horizontal resolution. Compared to the official TM5-MP-DOMINO (OFFLINE) product, POMINO-TROPOMI shows stronger concentration gradients near emission source locations and better agrees with MAX-DOAS measurements (R²=0.75; NMB=0.8 % versus R²=0.68, $\mathrm{NMB}=-\mathrm{41.9}$ %). Sensitivity tests suggest that implicit aerosol corrections, as in TM5-MP-DOMINO, lead to underestimations of NO₂ columns by about 25 % over the polluted northern East China region. Reducing the horizontal resolution of a priori NO₂ profiles would underestimate the retrieved NO₂ columns over isolated city clusters in western China by 35 % but with overestimates of more than 50 % over many offshore coastal areas. The effect of a priori NO₂ profiles is more important under calm conditions.

Tropospheric NO₂ columns retrieved from ozone monitoring instrument (OMI) are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO₂ data over China by using machine learning and an adaptive weighted temporal fitting method with NO₂ measurements from Global Ozone Monitoring Experiment–2B, and surface measurements. The reconstructed NO₂ has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with Multi-AXis Differential Optical Absorption Spectroscopy measurements (R: 0.75–0.85). Finally, the mean of reconstructed NO₂ vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over Mainland China. This novel finding is expected to contribute to both air quality and climate studies.

Rapid urbanization and broad use of biomass burning have led to important changes in NO_x [sum of nitrogen dioxide and nitrous oxide] emissions across South, Southeast, and East Asia, frequently occurring on day-to-day time scales and over areas not identified by existing emissions databases. Here we compute NO_x emissions using remotely sensed NO₂ [nitrogen dioxide] and a model-free mass-conserving inverse method, resulting respectively in 61 kt d⁻¹ and 40 kt d⁻¹ from biomass burning in Northern and Southern Continental Southeast Asia, and 14.3 kt d⁻¹ and 3.7 kt d⁻¹ from urbanization in China and Eastern South Asia, a net increase more than double existing inventories. Three observationally based physical constraints consistent with theory are found which current chemical transport models cannot match: more NO₂ per unit of NO_x emissions, longer and more variable in-situ lifetime, and longer-range transport. This result provides quantitative support for mitigation efforts targeting specific events, processes, or geographies.

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO₂) retrieved from sun-synchronous satellite instruments have provided abundant NO₂ data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO₂ at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO₂ VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO₂ algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO₂ slant column densities (SCDs). We then derive tropospheric NO₂ air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O₂–O₂ algorithm by using ancillary parameters consistent with those used in NO₂ AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO₂ VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO₂ VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO₂ retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO₂ Standard Product) v4 (R=0.87; NMB = −16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83; NMB = −1.5 %) NO₂ products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO₂ VCD data at nine sites (NMB = −11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO₂ concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78; NMB = −26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO₂.

This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO₂ from the Tropospheric Monitoring Instrument (TROPOMI) to rapidly and flexibly quantify changing and emerging sources of NO_x emissions at high spatial and daily temporal resolution. The inverted NO_x emissions and optimized underlying ranges include quantification of the underlying atmospheric in situ processing, transport, and physics. The results are presented over three changing regions in China, including Shandong and Hubei, which are rapidly urbanizing and not frequently addressed in the global literature. The day-to-day and grid-by-grid emissions are found to be 1.96 ± 0.27 µg m⁻² s⁻¹ on pixels with available a priori values (1.94 µg m⁻² s⁻¹), while 1.22 ± 0.63 µg m⁻² s⁻¹ extra emissions are found on pixels in which the a priori inventory is lower than 0.3 µg m⁻² s⁻¹. Source attribution based on the thermodynamics of combustion temperature, atmospheric transport, and in situ atmospheric processing successfully identifies five different industrial source types. Emissions from these industrial sites adjacent to the Yangtze River are found to be 161. ± 68.9 Kt yr⁻¹ (163 % higher than the a priori), consistent with missing light and medium industries located along the river, contradicting previous studies attributing water as the source of NO_x emissions. Finally, the results reveal pixels with an uncertainty larger than day-to-day variability, providing quantitative information for placement of future monitoring stations. It is hoped that these findings will drive a new approach to top-down emissions estimates, in which emissions are quantified and updated continuously based consistently on remotely sensed measurements and associated uncertainties that actively reflect land-use changes and quantify misidentified emissions, while quantifying new datasets to inform the bottom-up emissions community.

China's Shanxi Province accounts for 12 % of global coal output and therefore is responsible for a very large fraction of the total global methane (CH₄) emissions, as well as being a large source of uncertainty due to the lack of in situ and field measurements. This work introduces the first comprehensive attempt to compute the coal mine methane (CMM) emissions throughout Shanxi, using a mixture of bottom-up and top-down approaches. First, public and private data from 636 individual coal mines in Shanxi Province were analyzed following the IPCC Tier 2 approach, using three to five sets of observed emission factors and rank information based on methods issued by the National Coal Mine Safety Administration and the National Energy Administration, to compile a range of bottom-up CMM on a mine-by-mine basis. An eddy covariance tower is set up near the output flue of a well-characterized high-rank coal mine in Changzhi and used to produce an average observed CH₄ flux over two 2-month-long periods (Winter 2021 and Autumn 2022). The observed half-hourly CH₄ flux variability is found to be roughly stable over the entire observed time and is subsequently used to produce a set of scaling factors (ratio correction) to update the preliminary bottom-up coal mine methane emissions to account for both bias and high-frequency temporal variability. The resulting emissions dataset has been compared against commonly used global CMM datasets including EDGAR and GFEI v2, and there are three unique scientific conclusions. First, their total CH₄ emissions over Shanxi lie between this work's 50th percentile and 70th percentile range, meaning they are slightly high. Second, both datasets have a very large amount of emissions which occur where there are no coal mines and no CH₄-emitting industry, indicating that there are significant spatial disparities, with the overlapped portion of CMM emissions where mines exist consistently close to the 30th percentile of this work's emissions, meaning they underestimate CMM in general on a mine-by-mine basis. Third, some of the mines have average emissions values which are more than the 90th percentile of the computed mine-by-mine emissions, while many are far below the 10th percentile, showing that there is a significant issue with the sampling not capturing the observed temporal variability. It is hoped that this mine-by-mine and high-frequency approximation of CMM emissions can both improve top-down observation campaigns and provide quantitative support and identification of mitigation opportunities.