Excess atmospheric ammonia (NH₃) leads to deleterious effects on biodiversity, ecosystems, air quality and health, and it is therefore essential to monitor its budget and temporal evolution. Hyperspectral infrared satellite sounders provide daily NH₃ observations at global scale for over a decade. Here we use the version 3 of the Infrared Atmospheric Sounding Interferometer (IASI) NH₃ dataset to derive global, regional and national trends from 2008 to 2018. We find a worldwide increase of 12.8 ± 1.3 % over this 11-year period, driven by large increases in east Asia (5.80 ± 0.61% increase per year), western and central Africa (2.58 ± 0.23 % yr⁻¹), North America (2.40 ± 0.45 % yr⁻¹) and western and southern Europe (1.90 ± 0.43 % yr⁻¹). These are also seen in the Indo-Gangetic Plain, while the southwestern part of India exhibits decreasing trends. Reported national trends are analyzed in the light of changing anthropogenic and pyrogenic NH₃ emissions, meteorological conditions and the impact of sulfur and nitrogen oxides emissions, which alter the atmospheric lifetime of NH₃. We end with a short case study dedicated to the Netherlands and the ‘Dutch Nitrogen crisis’ of 2019.

Recently, Whitburn et al.(2016) presented a neural-network-based algorithm for retrieving atmospheric ammonia (NH₃) columns from Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. In the past year, several improvements have been introduced, and the resulting new baseline version, Artificial Neural Network for IASI (ANNI)-NH₃-v2.1, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over land and sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH₃-v2.1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard near-real-time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH₃ time series, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).

Ammonia (NH₃) is an essential reactive nitrogen species in the biosphere and through its use in agriculture in the form of fertilizer (important for sustaining humankind). The current emission levels, however, are up to 4 times higher than in the previous century and continue to grow with uncertain consequences to human health and the environment. While NH₃ at its current levels is a hazard to environmental and human health, the atmospheric budget is still highly uncertain, which is a product of an overall lack of measurements. The capability to measure NH₃ with satellites has opened up new ways to study the atmospheric NH₃ budget. In this study, we present the first estimates of NH₃ emissions, lifetimes and plume widths from large ($>\sim \mathrm{5}$ kt yr⁻¹) agricultural and industrial point sources from Cross-track Infrared Sounder (CrIS) satellite observations across the globe with a consistent methodology. The same methodology is also applied to the Infrared Atmospheric Sounding Interferometer (IASI) (A and B) satellite observations, and we show that the satellites typically provide comparable results that are within the uncertainty of the estimates. The computed NH₃ lifetime for large point sources is on average 2.35±1.16 h. For the 249 sources with emission levels detectable by the CrIS satellite, there are currently 55 locations missing (or underestimated by more than an order of magnitude) from the current Hemispheric Transport Atmospheric Pollution version 2 (HTAPv2) emission inventory and only 72 locations with emissions within a factor of 2 compared to the inventories. The CrIS emission estimates give a total of 5622 kt yr⁻¹, for the sources analyzed in this study, which is around a factor of ∼2.5 higher than the emissions reported in HTAPv2. Furthermore, the study shows that it is possible to accurately detect short- and long-term changes in emissions, demonstrating the possibility of using satellite-observed NH₃ to constrain emission inventories.

In this paper, we describe a new flexible and robust NH₃ retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH₃ columns via a neural network. It is an extension of the method presented in Van Damme et al. (2014a) who used lookup tables (LUT) for the radiance‐concentration conversion. The new method inherits the advantages of the LUT‐based method while providing several significant improvements. These include the following: (1) Complete temperature and humidity vertical profiles can be accounted for. (2) Third‐party NH₃ vertical profile information can be used. (3) Reported positive biases of LUT retrieval are reduced, and finally (4) a full measurement uncertainty characterization is provided. A running theme in this study, related to item (2), is the importance of the assumed vertical NH₃ profile. We demonstrate the advantages of allowing variable profile shapes in the retrieval. As an example, we analyze how the retrievals change when all NH₃ is assumed to be confined to the boundary layer. We analyze different averaging procedures in use for NH₃ in the literature, introduced to cope with the variable measurement sensitivity and derive global averaged distributions for the year 2013. A comparison with a GEOS‐Chem modeled global distribution is also presented, showing a general good correspondence (within ±3 × 10¹⁵ molecules.cm⁻²) over most of the Northern Hemisphere. However, IASI finds mean columns about 1–1.5 × 10¹⁶ molecules.cm⁻² (∼50–60%) lower than GEOS‐Chem for India and the North China plain.

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.

Aerosol is an important component of the Earth's atmosphere, affecting weather, climate, and diverse elements of the biosphere. Satellite sounders are an essential tool for measuring the highly variable distributions of atmospheric aerosol. Here we present a new algorithm for estimating atmospheric dust optical depths and associated retrieval uncertainties from spectral radiance measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval is based on the calculation of a dust index and on a neural network trained with synthetic IASI spectra. It has an inherent high sensitivity to dust and efficiently discriminates dust from other aerosols. In particular, over remote dust‐free areas, the retrieved levels of optical depth have a low bias. Over sea, noise levels are markedly lower than over land. Performance over deserts is comparable to that of other land surfaces. We use ground‐based coarse mode aerosol measurements from the AErosol RObotic NETwork to validate the new product. The overall assessment is favorable, with standard deviations in line with estimated uncertainties, low biases, and high correlation coefficients. However, a systematic relative bias occurs between sites dominated by African and Asian dust sources respectively, likely linked to differences in mineralogy. The retrieval has been performed on over a decade of IASI data, and the resulting data set is now publicly available. We present a global seasonal dust climatology based on this record and compare it with those obtained from independent satellite measurements (Moderate Resolution Imaging Spectroradiometer and a third‐party IASI product) and dust optical depth from the ECMWF model.