<p><strong>Abstract.</strong> Emission datasets of nitrogen oxides (NO<sub><i>x</i></sub>) at high horizontal resolutions (e.g., 0.05° × 0.05°) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often lacking or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO<sub>2</sub>), here we develop a computationally efficient method to inverting NO<sub><i>x</i></sub> emissions in major urban areas at the 0.05° × 0.05° resolution. The inversion accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a 2-dimensional Peking University High-resolution Lifetime-Emission-Transport (PHLET) model, its adjoint model (PHLET-A), and a Satellite Conversion Metrix approach to relate emissions, simulated NO<sub>2</sub>, and satellite NO<sub>2</sub> data. The inversion method is applied to summer months of 2012–2016 in the Yangtze River Delta area (YRD, 118 °E–123 °E, 29 °N–34 °N), a major polluted region of China, using the POMINO NO<sub>2</sub> vertical column density product retrieved from the Ozone Monitoring Instrument. A systematic analysis of inversion errors is performed, including using an Observing System Simulation Experiment-like test. Across the YRD area, the inverted summer average emission ranges from 0 to 12.0 kg km<sup>−2</sup> h<sup>−1</sup>, and the lifetime (due to chemical loss and deposition) from 1.4 to 3.6 h. Our inverted emission dataset reveals fine-scale spatial information tied to nighttime light, population density, road network, and maritime shipping. Many of the inverted fine-scale emission features are not well represented or not included in the widely used Multi-scale Emissions Inventory of China. Our inversion method can be applied to other regions and other satellite sensors such as the TROPOspheric Monitoring Instrument.</p>

High-resolution (e.g., 5 km) emission data of nitrogen oxides (NO x = NO + NO2) provide localized knowledge of pollution sources for targeted regulations, yet such data are lacking or inaccurate over most regions at present. Here we improve our PHLET-based inversion method to derive NO x emissions in China at a 5-km resolution in summer 2019, based on the TROPOMI-POMINO satellite product of nitrogen dioxide (NO2) columns. With low computational costs, our inversion explicitly accounts for the effects of horizontal transport and nonlinear chemistry. We find numerous small-to-medium sources related to minor roads and small human settlements at relatively low affluence levels, in addition to clear emission signals along major transportation lines, consistent with road line density and Tencent location data. Many small-to-medium sources and transportation emissions are unclear or missing in the spatial distributions of four widely used emission inventories. Our emissions offer a unique reference for targeted emission control.

<p><strong>Abstract.</strong> Eastern China (27° N–41° N, 110° E–123° E) is heavily polluted by nitrogen dioxide (NO<sub>2</sub>), particulate matter with aerodynamic diameter below 2.5 μm (PM<sub>2.5</sub>) and other air pollutants. These pollutants vary in a variety of temporal and spatial scales, with many temporal scales non-periodic and non-stationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis-visualization package to evaluate the spatiotemporal variability of ground-level NO<sub>2</sub>, PM<sub>2.5</sub>, and their associations with meteorological processes over Eastern China in Fall–Winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China-wide synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or buildup of pollutants from one day to another. <br><br> We further evaluate simulations of GEOS-Chem and WRF/CMAQ in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO<sub>2</sub> by about 17 μg/m<sup>3</sup> and PM<sub>2.5</sub> by 35 μg/m<sup>3</sup> on average. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China-synchronous mode (especially for NO<sub>2</sub>). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dusts. CMAQ overestimates the diurnal cycle of pollutants due to too weak boundary layer mixing – especially in the nighttime, CMAQ overestimates NO<sub>2</sub> by about 30 μg/m<sup>3</sup> and PM<sub>2.5</sub> by 60 μg/m<sup>3</sup>. For the day-to-day variability, CMAQ reproduces the observed Eastern-China synchronous mode but not the north-south opposing mode of NO<sub>2</sub>. Both models capture the day-to-day variability of PM<sub>2.5</sub> better than that of NO<sub>2</sub>. These results shed light on model improvement. The EOF-EEMD package is freely accessible.</p>

Abstract. Nitrogen dioxide (NO2) is a major air pollutant. Tropospheric NO2 vertical column densities (VCDs) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented high temporal resolution. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO-GEMS. We develop a hybrid retrieval method combining GEMS and TROPOMI observations as well as GEOS-Chem simulations to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for the anisotropy of surface reflectance and aerosol optical effects, through 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. Initial retrieval of POMINO-GEMS tropospheric NO2 VCDs for June–August 2021 reveals strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO-GEMS NO2 VCDs agree well with our POMINO-TROPOMI v1.2.2 product (R = 0.97, and NMB = 3.6 %) over Asia. Comparison with ground-based MAX-DOAS VCD data at nine sites shows a small bias of POMINO-GEMS (NMB = –15.7 %); however, the correlation for diurnal variation varies from -0.66 to 0.90, suggesting location-dependent performance. Surface NO2 concentrations estimated from POMINO-GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China at 855 sites (NMB = –24.1 %, and R = 0.95 for diurnal correlation averaged over all sites). POMINO-GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2.

<p><strong>Abstract.</strong> Recent studies have shown that surface ozone (O<sub>3</sub>) concentrations over Central Eastern China (CEC) have increased significantly during the past decade. We quantified the effects of changes in meteorological conditions and O<sub>3</sub> precursor emissions on surface O<sub>3</sub> levels over CEC between July 2003 and July 2015 using the GEOS-Chem model. The simulated monthly mean maximum daily 8-h average O<sub>3</sub> concentration (MDA8 O<sub>3</sub>) in July increased by approximately 13.6 %, from 65.5 ± 7.9 ppbv (2003) to 74.4 ± 8.7 ppbv (2015), comparable to the observed results. The change in meteorology led to an increase of MDA8 O<sub>3</sub> of 5.8 ± 3.9 ppbv over the central part of CEC, in contrast to a decrease of about −0.8 ± 3.5 ppbv over the eastern part of the region. In comparison, the MDA8 O<sub>3</sub> over the central and eastern parts of CEC increased by 3.5 ± 1.4 ppbv and 5.6 ± 1.8 ppbv due to the increased emissions. The increase in regional averaged O<sub>3</sub> resulting from the emission increase (4.0 ± 1.9 ppbv) was higher than that caused by meteorological changes (3.1 ± 4.9 ppbv) relative to the 2003 standard simulation, while the regions with larger O<sub>3</sub> increases showed a higher sensitivity to meteorological conditions than to emission changes. Sensitivity tests indicate that increased levels of anthropogenic non-methane volatile organic compounds (NMVOCs) dominate the O<sub>3</sub> increase over the eastern part of CEC, and anthropogenic nitrogen oxides (NO<sub><i>x</i></sub>) mainly increase O<sub>3</sub> concentrations over the central and western parts, while decrease O<sub>3</sub> in a few urban areas in the eastern part. Process analysis showed that net photochemical production and meteorological conditions (transport in particular) are two important factors that influence O<sub>3</sub> levels over the CEC. The results of this study suggest a need to further assess the effectiveness of control strategies for O<sub>3</sub> pollution in the context of regional meteorology, transboundary transport, and anthropogenic emission changes.</p>