Spaceborne NO₂ column observations from two high‐resolution instruments, Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel‐5 Precursor and Ozone Monitoring Instrument (OMI) on Aura, reveal unprecedented NO₂ decreases over China, South Korea, western Europe, and the United States as a result of public health measures enforced to contain the coronavirus disease outbreak (Covid‐19) in January–April 2020. The average NO₂ column drop over all Chinese cities amounts to −40% relative to the same period in 2019 and reaches up to a factor of ~2 at heavily hit cities, for example, Wuhan, Jinan, while the decreases in western Europe and the United States are also significant (−20% to −38%). In contrast with this, although Iran is also strongly affected by the disease, the observations do not show evidence of lower emissions, reflecting more limited health measures.

The global emissions of isoprene are calculated at 0.5° resolution for each year between 1995 and 2006, based on the MEGAN (Model of Emissions of Gases and Aerosols from Nature) version 2 model (Guenther et al., 2006) and a detailed multi-layer canopy environment model for the calculation of leaf temperature and visible radiation fluxes. The calculation is driven by meteorological fields – air temperature, cloud cover, downward solar irradiance, windspeed, volumetric soil moisture in 4 soil layers – provided by analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The estimated annual global isoprene emission ranges between 374 Tg (in 1996) and 449 Tg (in 1998 and 2005), for an average of ca. 410 Tg/year over the whole period, i.e. about 30% less than the standard MEGAN estimate (Guenther et al., 2006). This difference is due, to a large extent, to the impact of the soil moisture stress factor, which is found here to decrease the global emissions by more than 20%. In qualitative agreement with past studies, high annual emissions are found to be generally associated with El Niño events. The emission inventory is evaluated against flux measurement campaigns at Harvard forest (Massachussets) and Tapajós in Amazonia, showing that the model can capture quite well the short-term variability of emissions, but that it fails to reproduce the observed seasonal variation at the tropical rainforest site, with largely overestimated wet season fluxes. The comparison of the HCHO vertical columns calculated by a chemistry and transport model (CTM) with HCHO distributions retrieved from space provides useful insights on tropical isoprene emissions. For example, the relatively low emissions calculated over Western Amazonia (compared to the corresponding estimates in the inventory of Guenther et al., 1995) are validated by the excellent agreement found between the CTM and HCHO data over this region. The parameterized impact of the soil moisture stress on isoprene emissions is found to reduce the model/data bias over Australia, but it leads to underestimated emissions near the end of the dry season over subtropical Africa.

We use ground-based observations of CO mixing ratios and vertical column abundances together with tropospheric NO₂ columns from the GOME satellite instrument as constraints for improving the global annual emission estimates of CO and NO_x for the year 1997. The agreement between concentrations calculated by the global 3-dimensional CTM IMAGES and the observations is optimized using the adjoint modelling technique, which allows to invert for CO and NO_x fluxes simultaneously, taking their chemical interactions into account. Our analysis quantifies a total of 39 flux parameters, comprising anthropogenic and biomass burning sources over large continental regions, soil and lightning emissions of NO_x, biogenic emissions of CO and non-methane hydrocarbons, as well as the deposition velocities of both CO and NO_x. Comparison between observed, prior and optimized CO mixing ratios at NOAA/CMDL sites shows that the inversion performs well at the northern mid- and high latitudes, and that it is less efficient in the Southern Hemisphere, as expected due to the scarsity of measurements over this part of the globe. The inversion, moreover, brings the model much closer to the measured NO₂ columns over all regions. Sensitivity tests show that anthropogenic sources exhibit weak sensitivity to changes of the a priori errors associated to the bottom-up inventory, whereas biomass burning sources are subject to a strong variability. Our best estimate for the 1997 global top-down CO source amounts to 2760 Tg CO. Anthropogenic emissions increase by 28%, in agreement with previous inverse modelling studies, suggesting that the present bottom-up inventories underestimate the anthropogenic CO emissions in the Northern Hemisphere. The magnitude of the optimized NO_x global source decreases by 14% with respect to the prior, and amounts to 42.1 Tg N, out of which 22.8 Tg N are due to anthropogenic sources. The NO_x emissions increase over Tropical regions, whereas they decrease over Europe and Asia. Our inversion results have been evaluated against independent observations from aircraft campaigns. This comparison shows that the optimization of CO emissions constrained by both CO and NO₂ observations leads to a better agreement between modelled and observed values, especially in the Tropics and the Southern Hemisphere, compared to the case where only CO observations are used. A posteriori estimation of errors on the control parameters shows that a significant error reduction is achieved for the majority of the anthropogenic source parameters, whereas biomass burning emissions are still subject to large errors after optimization. Nonetheless, the constraints provided by the GOME measurements allow to reduce the uncertainties on savanna burning emissions of both CO and NO_x, suggesting thus that the incorporation of these data in the inversion yields more robust results for carbon monoxide.

This study compares recent CO, NO_x, NMVOC, SO₂, BC, and OC anthropogenic emissions from several state‐of‐the‐art top‐down estimates to global and regional bottom‐up inventories and projections from five Shared Socioeconomic Pathways (SSPs) in several regions. Results show that top‐down emissions derived in several recent studies exhibit similar uncertainty as bottom‐up inventories in some regions for certain species and even less in the case of Chinese CO emissions. In general, the largest discrepancies are found outside of regions such as the United States, Europe, and Japan where the most accurate and detailed information on emissions is available. In some regions such as China, which has recently undergone dynamical economic growth and changes in air quality regulations, the top‐down estimates better capture recent emission trends than global bottom‐up inventories. These results show the potential of top‐down estimates to complement bottom‐up inventories and to aide in the development of emission scenarios, particularly in regions where global inventories lack the necessary up‐to‐date and accurate information regarding regional activity data and emission factors such as Africa and India. Areas of future work aimed at quantifying and reducing uncertainty are also highlighted. A regional comparison of recent CO and NO_x trends in the five SSPs indicate that SSP126, a strong pollution control scenario, best represents the trends from the top‐down and regional bottom‐up inventories in the United States, Europe, and China, while SSP460, a low‐pollution control scenario, lies closest to actual trends in West Africa. This analysis can be useful for air quality forecasting and near‐future pollution control/mitigation policy studies.

Retrieving concentrations of minor atmospheric trace gases from satellite observations is challenging due to their weak spectral signature. Here we present a new version of the ANNI (Artificial Neural Network for Infrared Atmospheric Sounding Interferometer, IASI) retrieval framework, which relies on a hyperspectral range index (HRI) for the quantification of the gas spectral signature and on an artificial feedforward neural network to convert the HRI into a gas total column. We detail the different steps of the retrieval method, especially where they differ from previous work, and apply the retrieval to three important volatile organic compounds: methanol (CH₃OH), formic acid (HCOOH), and peroxyacetyl nitrate (PAN). The comparison of the retrieved columns with those from an optimal estimation inversion retrieval shows an overall excellent agreement: differences occur mainly when the sensitivity to the target gas is low and are consistent with the conceptual differences between the two approaches. We present retrieval examples over selected regions, comparison with previously developed products, and the global seasonal distributions including the first global distributions of PAN on a daily basis. The ANNI retrieval has been carried out on the whole time series of IASI observations (2007–2018), so that currently over 10 years of twice‐daily global CH₃OH, HCOOH, and PAN total column distributions have been produced. This unique data set opens avenues for tackling important questions related to sources, transport, and transformation of volatile organic compounds in the global atmosphere.