Global, regional and national trends of atmospheric ammonia derived from a decadal (2008–2018) satellite record

Ammonia (NH₃) is the most abundant alkaline component of our atmosphere. Agricultural activities are responsible for the majority of its emissions [1], with volatilization from livestock manure and losses from synthetic fertilizer application accounting for over 80 % of the total emissions in, e.g. Europe [2], United States (U.S.) [3] and China [4]. For 2015, the Emission Database for Global Atmospheric Research (EDGAR) v5.0 reports a global emission total of 49.1  Tg NH₃, with 85.7 % originating from agriculture [5, 6]. Other sources include oceans and soils, waste water treatment, wild animals, human excreta, traffic and biomass burning [1, 7]. The latter was estimated to amount to 4.9 Tg in 2015 by the Global Fire Emissions Database (GFED) v4.1 s [8]. Recently, emissions from industry have also been identified as an important and largely underestimated source of atmospheric NH₃ [9].

High NH₃ levels negatively affect ecosystems by depleting biodiversity and degrading soil and water quality [10, 11]. Atmospheric NH₃ has a remarkable short atmospheric lifetime of the order of hours [9, 12]. Once emitted, a large part of NH₃ is rapidly deposited on terrestrial and aquatic ecosystems, resulting in adverse acidifying and eutrophying effects [13, 14]. In combination with nitrogen (NO_x) and sulfur oxides (SO_x), NH₃ plays a significant role in fine particulate matter (PM_2.5) formation and related health impacts [15, 16]. Its contribution to PM_2.5 formation is however still underexposed (e.g. [17–19]) and, as regulations are mostly geared towards restricting NO_x and SO_x emissions, the world is currently 'ammonia-rich' [20]. In Europe, China and the U.S. in particular, reduction in emissions of nitrogen and sulfur oxides have demonstrably resulted in an increased amount of atmospheric gas-phase NH₃ during the last decade [21–24]. Several studies have concluded that reducing NH₃ emissions would be a cost-effective strategy to reduce PM_2.5 concentrations [17, 25]. It has been estimated that a 50 % reduction of the NH₃ emissions in northwestern Europe would lead to a 24 % reduction in the PM_2.5 concentration [26]. In China, the same reduction rate on NH₃ emissions, joined with a 15 % reduction on NO_x and SO_x emissions, would reduce PM_2.5 pollution by 11 %–17 % and nitrogen deposition by 34 %, but would worsen acid rain [27]. Through its role in aerosol formation and the impact of its deposition on plant productivity and carbon uptake, NH₃ also affects climate [28, 29].

For the first decade of the 21st century, the EDGAR emissions model reports a 20 % increase of the global NH₃ emissions, but with large variations at regional and national scales [30]. Countries in Europe have committed to modest reductions of NH₃ emissions in the framework of the Gothenburg Protocol, which is part of the convention on Long-Range Transboundary Air Pollution (LRTAP) and the National Emissions Ceilings (NEC) Directive [31]. The success of this and other ammonia-control initiatives has traditionally been difficult to assess as the uncertainty in NH₃ emissions is the largest among all pollutants [1, 5]. For more than a decade now, satellite missions offer global observations of NH₃ abundance [32–35]. In particular, satellite-based datasets have already been used to identify and quantify main NH₃ point sources [9, 12, 36], to derive first changes in atmospheric NH₃ [37, 38], to constrain deposition flux estimates [39–41] and, recently, to perform inverse modeling of NH₃ emissions [42, 43].

The present study uses the reanalyzed NH₃ dataset recently obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite over 11 years (2008–2018) to derive decadal trends throughout the world. In the next section, the satellite data are presented along with the method to derive trends and associated uncertainties. In section 3, these trends are presented, discussed and interpreted at global, regional and national scales. In the last section, a special focus is given to the case of the Netherlands, a country that received a lot of attention end of 2019 due to the 'Dutch Nitrogen crisis' which substantially affected the national economy [44].

2.1. Satellite measurements

Even though IASI's main goal is to provide temperature and humidity measurements for improved weather forecasts, its instrumental characteristics enable global bi-daily measurements of a series of atmospheric constituents. In particular, its relatively high spatial resolution (12 km at nadir), scanning mode (2100 km swath) and good spectral performance (0.5 cm⁻¹ spectral resolution apodized and low radiometric noise) [45] have proven to be most useful for characterizing the spatiotemporal variability and budget of NH₃ [9, 32, 46–51]. The IASI mission consists of a suite of three identical instruments embarked on the Metop-A, -B and -C platforms, launched in 2006, 2012 and 2018, respectively. Together, these provide consistent global satellite measurements, allowing us to derive trends at global, regional and national scale. Eleven years of morning overpass IASI/Metop-A measurements have been considered here for the calculation of the global trends, while merged IASI/Metop-A (2008–2018) and -B (2013–2018) data have been used for the case study over the Netherlands. Only morning observations have been kept as their uncertainties are lower thanks to a more favorable thermal state of the atmosphere for the remote sensing of its lowest layers [46, 47].

We used version 3 of the IASI-NH₃ dataset, which was built using the ANNI (artificial neural network for IASI) retrieval framework. ANNI has been developed to perform global retrievals of NH₃ [52, 53] and was recently expanded to retrieve several other trace gases (e.g. [54–56]). Two IASI-NH₃ datasets are available: a near-real time dataset, for which the retrieval relies on meteorological information directly obtained from the IASI measurements [57] and a reanalyzed dataset that is based on data from the European Centre for Medium-Range Weather Forecasts (ECMWF) climate reanalysis [58]. The latter, named ANNI-NH₃-v3R-ERA5, has been developed specifically for trend studies and is the one used here. Its 2008–2018 globally averaged distribution is shown in figure 1. Note that the satellite NH₃ values are reported as total columns, representing the total number of NH₃ molecules in a column from the ground surface to the top of the atmosphere expressed per unit of surface.

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The general NH₃ retrieval algorithm is detailed in [52–54]. A description of the changes that were implemented for version 3 is provided in appendix A. A careful analysis of the initial dataset revealed some spurious trends and offsets in the long-term trends over remote oceans. These included (a) two offsets that coincide with changes to the instrument, (b) a slow decreasing trend most likely due to increasing CO₂ concentrations and (c) a residual dependence on H₂O. Therefore, for the final version of the product, several debiasing procedures were applied (see again appendix A). The only potential remaining source of temporal inhomogeneity stems from the use of the IASI near real-time cloud detection algorithm, as currently no official reanalyzed cloud product is available. This most notably affects observations over the Southern Ocean and South Pacific Ocean before 2011 [59]. IASI-NH₃ measurements have been compared with ground-based and airborne independent observations in [60, 61]. More recently, a dedicated validation study was performed for version 3 of the product. A good correlation was found between in-situ vertical profiles and IASI-NH₃ total columns for both v3 datasets, with slightly better statistics for the reanalysis than for the near-real time product [62].

2.2. Trend analysis method, figures and tables

To determine the NH₃ trends and their uncertainty, the method developed by Gardiner et al [63] has been applied to the IASI observational time series. It relies on least squares regression and bootstrap resampling [64] to fit daily time series data to the following function:

$$\begin{eqnarray} &&\textrm{NH}_3 (t) = c t + \sum_{n = 0}^3 [a_n \sin( 2 \pi n t) + b_n \cos( 2\pi n t)]. \end{eqnarray} \tag{ 1 }$$

The first term in this equation characterizes the long-term linear trend in the data, with the sought-after annual trend c. The other terms constitute a third-order Fourier series representing the periodic seasonal variations. This statistical method provides separate 2σ (or p = 0.05) lower and upper bound uncertainties of the trend values, but as the differences between both are very small, we used similarly to [63] the mean uncertainty. Following the nomenclature of that paper too, we call trends 'significant' if the change in NH₃ total columns exceeds their uncertainty (i.e. is significantly different from zero). Trends were computed at grid cell, country, regional and global scales in absolute (in molec cm⁻² yr⁻¹) terms. From these, we calculated total relative changes from 2008 to 2018 (i.e. the relative decadal NH₃ changes with respect to 2008, in % 10yr⁻¹) and average yearly relative trends assuming compound change rates (in % yr⁻¹). All uncertainties on the trend numbers, relative or absolute, have been reported with two significant figures.

The global distribution of the NH₃ trends at 0.5° × 0.5° resolution (56 km × 56 km at the equator) is shown in figure 2(a) in absolute value. Here the trend calculation was applied on each grid cell separately. The same figure is shown (figure B1) but with stippled cells for non-significant trends. The national trends presented in tables 1 and B1 and in figure 2(b) were computed based on the daily average time series at the national scale. Examples of such daily time series are given in appendix B, figure B3. These figures also show separately the linear and periodic terms of the fit, together with a standard ordinary least squares regression fit. Trends calculated with the latter were generally found to be in good agreement with the trends calculated with the more robust bootstrapping method. For selected countries we show in figure 3 yearly normalized NH₃ time series which were calculated from daily averages. Global and subcontinental trends (table 1, figures 2(c) and B2) have been calculated based on the national numbers, weighted by the area of each country. In figures 2(b) and (c), countries or subcontinents with non-significant trends in atmospheric NH₃ have been hatched. These thus correspond to regions where either the uncertainty on the trend is too large or where the estimated trend is close to zero.

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Table 1. Absolute (molec cm⁻² yr⁻¹), relative (% yr⁻¹) and decadal NH₃ trends (% 10yr⁻¹) calculated for selected countries and regions based on national daily average time series (2008–2018) measured by IASI/Metop-A. The relative trend is expressed as compound growth rate from 2008. The regions are shown in figure 2(c). Table B1 reports trend values for each country.

	Absolute (molec cm−2 yr−1)	Relative (% yr−1)	Decadal (% 10yr−1)
Bolivia	(−18.1 ± 6.7) × 1013	−3.4 ± 1.0	−29 ± 11
China	(24.7 ± 2.1) × 1013	6.25 ± 0.68	83.3 ± 7.0
India	(0.8 ± 1.0) × 1014	0.39 ± 0.49	4.0 ± 5.0
Indonesia	(10.1 ± 5.1) × 1013	2.7 ± 1.4	30 ± 15
Netherlands	(2.1 ± 1.1) × 1014	3.6 ± 1.9	42 ± 21
Nigeria	(49.4 ± 7.9) × 1013	3.38 ± 0.62	39.4 ± 6.3
Russia	(−7.1 ± 1.7) × 1013	−4.11 ± 0.80	−34.2 ± 8.3
Spain	(7.6 ± 2.8) × 1013	2.08 ± 0.82	22.9 ± 8.5
Turkey	(6.0 ± 1.4) × 1013	3.31 ± 0.89	38.5 ± 9.3
United States	(11.4 ± 1.7) × 1013	3.42 ± 0.59	39.9 ± 6.1
Northern Europe	(−0.4 ± 1.4) × 1013	−0.22 ± 0.81	−2.2 ± 8.4
Western and southern Europe	(6.7 ± 1.4) × 1013	1.90 ± 0.43	20.8 ± 4.3
Eastern Europe and Russia	(−6.3 ± 1.6) × 1013	−3.37 ± 0.70	−29.0 ± 7.3
Northern Africa	(2.5 ± 1.0) × 1013	1.11 ± 0.47	11.6 ± 4.8
Western and central Africa	(20.3 ± 1.6) × 1013	2.58 ± 0.23	29.0 ± 2.3
Eastern Africa	(30.1 ± 8.8) × 1012	0.65 ± 0.19	6.7 ± 1.9
Southern Africa	(−2.9 ± 7.6) × 1012	−0.19 ± 0.48	−1.9 ± 4.9
Northern America	(6.1 ± 1.0) × 1013	2.40 ± 0.45	26.8 ± 4.5
Central America	(1.3 ± 1.2) × 1013	0.48 ± 0.44	4.9 ± 4.5
South America	(3.0 ± 1.7) × 1013	0.60 ± 0.34	6.2 ± 3.5
Western Asia	(11.4 ± 9.6) × 1012	0.94 ± 0.80	9.8 ± 8.3
Central Asia	(−5.4 ± 1.5) × 1013	−1.72 ± 0.44	−15.9 ± 4.5
East Asia	(20.5 ± 1.7) × 1013	5.80 ± 0.61	75.7 ± 6.3
South Asia	(6.1 ± 5.2) × 1013	0.45 ± 0.38	4.6 ± 3.9
Southeastern Asia	(5.3 ± 2.4) × 1013	1.25 ± 0.59	13.2 ± 6.1
Oceania	(−32.7 ± 3.8) × 1012	−4.54 ± 0.43	−37.1 ± 4.4
Antarctica	(21.7 ± 3.2) × 1012	1.03 ± 0.16	10.8 ± 1.6
Global	(45.6 ± 4.6) × 1012	1.21 ± 0.13	12.8 ± 1.3

Apart from IASI-derived trends, we also obtained trends based on yearly emission from the aforementioned EDGAR bottom-up emission inventory (for 2008–2015) and the GFED inventory for pyrogenic NH₃ emission (2008–2018). These were calculated using a standard least squares linear regression fit and are shown in appendix B, figures B4 and B5.

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East Asia stands out as the region in the world with the largest increase over 2008–2018 with a decadal increase of 75.7 ± 6.3 % and an annual growth rate of 5.80 ± 0.61 % yr⁻¹, mostly due to increases observed in the North China Plain and the Chengdu (Sichuan, China) area (figures 2(a) and B1). For China as a whole, we estimate an annual trend of 6.25 ± 0.68 % (figure 2(b)) and a decadal change of 83.3 ± 7.0 %. The increased columns are likely driven by a rise in emissions, which [4] and [65] estimated to be 1.9 and 1.7 % yr⁻¹ over 2000–2015 and 2008–2016, respectively. While agriculture still contributes to over 80 % of the emissions, recent emission-based [65] and satellite-based [9] studies have pointed out the increasing importance of non-agricultural sources, especially of industrial emitters. The contribution of fossil-fuel combustion sources, including traffic, has been lately highlighted especially during severe haze episodes [66–68]. Surprisingly, as shown in figure B4, the EDGAR v5.0 global database [6] reports a moderately slow decline in emissions over eastern China during the 2008–2015 period, which appears to be mostly due to a sharp decline in the estimates of the year 2014 and which is not observed in the satellite data. Other studies also reported relative stable emissions during the past decade (e.g. [69]).

NH₃ columns are affected both by changes in sources and sinks. For China in particular, the large increases observed by IASI after 2013 (figure 3(a)) are also likely caused in part by a longer atmospheric lifetime of NH₃, linked to a decrease of emissions of acidifying compounds (mostly SO_x and NO_x; e.g. [24, 70]) following China's Clean Air Action in 2013 [69]. Despite the decline in the emissions of sulfur and nitrogen oxides, China is still facing major air quality issues and has only recently started to dedicate efforts to mitigate NH₃ emissions [27]. North and South Korea present the largest relative positive growths at the national scale (14.7 ± 4.6 and 14.6 ± 3.6 % yr⁻¹, respectively) in Asia, followed by Japan (7.7 ± 3.3 % yr⁻¹). While anthropogenic NH₃ emissions have increased by around 1.5 % yr⁻¹ in South Korea according to the OECD [71] and EDGAR database [6], the much larger relative growth estimated for these countries may also be linked in part to an increasing eastward transport of atmospheric NH₃ from China, as previously shown for particulate matter [72, 73] and dust [74]. In excess conditions, NH₃ atmospheric lifetime can be larger than a few hours and up to a few days (e.g. [9] and references therein and [50]).

After South Korea, Pakistan exhibits the highest absolute trend of Asia. Agriculture in this country is characterized by low and declining nitrogen use efficiencies due to excessive application of synthetic fertilizers [75]. Shahzad et al [76] highlighted how nitrogen use and surplus increased at much faster rates than the production yield during the 1961–2014 period. This overconsumption of synthetic fertilizers in Pakistan leads to a significant increase of NH₃ in the atmosphere [77]. Its neighboring country India is as a whole characterized by a non-significant trend close to zero (0.39 ± 0.49 % yr⁻¹) but it is important to recognize that this is due to a contrasted pattern with a high upward trend in the Indo-Gangetic Plain and in the northwestern part of the country in general, while the southeastern part shows decreasing NH₃ columns (figure 2(a)). Similar results were found with the previous version of the IASI-NH₃ product over the 2008–2016 period [78]. In the last decade, India has undertaken several measures to reduce nitrogen pollution. In 2015 for instance, the government forced urea manufacturers to produce urea coated with neem oil, a natural nitrification inhibitor, to improve nitrogen use efficiency [79]. However, soil pH affects the efficiency of such inhibitors and their use could also lead to enhanced NH₃ volatilization over alkaline soils [80, 81]. Interestingly, the soil pH map of India presents the same spatial patterns as the calculated trend distribution, with alkaline soils in the northwestern part of the country and more acidic soils in eastern India [82]. Obviously, further analyses are needed to assess the impact of changing nitrogen fertilizer use and consumption on NH₃ volatilization in India.

In southeastern Asia, Myanmar presents a negative trend of −3.19 ± 0.70 % yr⁻¹. A likely explanation is a decrease in biomass burning activity for the considered time period, as seen from the GFED v4.1s trend analysis (see figure B5). In contrast, the extreme NH₃ emissions from peat fires in 2015 (see figure 3(b)) artificially drive the trend distribution in Indonesia towards high positive values over the eastern part of Sumatra [50]. The spatial patterns of the NH₃ trends in Russia can also be explained to some extent by the biomass burning events that occurred during the 2008–2018 period. This is clear from the comparison of figures 2(a) with the trends calculated from GFED (figure B5), as well as from an analysis of the time series over selected regions. The 2014 and 2018 fire episodes in the northeastern parts of Siberia in particular are responsible for the positive trends over this remote region. For example, during the summer of 2018, NH₃ emissions from fires in Russian Federation's Republic of Sakha were so large that they could be tracked down to eastern Canada [83, 84]. The negative trends reported in the western part of the country is partly due to the exceptional amounts of NH₃ released in the atmosphere by the fires around Moscow in 2010 (see figure 3(b)) [48, 85]. This single event has a pronounced impact on the downward annual rate calculated for the whole Russian Federation (−4.11 ± 0.80 % yr⁻¹), which would however, still be negative (−2.33 ± 0.48 % yr⁻¹) if the fire period (27 July–27 August 2010) is removed from the 11-year time series. Conversely, central Asia shows a significant decrease in NH₃ which does not appear to be due to a decrease in biomass burning emissions. From the IASI measurements, we estimate downward trends around −2 % yr⁻¹ in Tajikistan, Turkmenistan and Kazakhstan. Further information on on-ground activities in this part of the world are needed to confirm and interpret this evolution.

The increase in the western and southern parts of Europe is rather homogeneous with countries like Belgium, the Netherlands, France, Germany, Poland, Italy and Spain all increasing between 2 and 4.2 % yr⁻¹. As a whole, this region presents a decadal change of 20.8 ± 4.3 %. The exceptional weather conditions of 2018 in terms of temperature and drought [86] likely explain a non-negligible part of this high trend value, as confirmed for the Netherlands (see section 4 and [87, 88]). While the EDGAR emission data is not available for 2018, the reported evolution in the 2008–2015 period is not consistent with what IASI observes. In particular, the EDGAR data exhibits heterogeneous trends over Europe, with large decreases in France and Poland, and increases in the other countries, especially in Germany. These are evidently driven by the underlying country-scale data and show the limitation of bottom-up inventories that rely on country-scale statistics, which are not always calculated and reported uniformly. According to the European Environmental Agency (EEA) [89], NH₃ emissions have been decreasing in the EU-28 since 1990 with a total decline of 24 % by 2008. From that year, reported NH₃ emissions were relatively stable, with a decline of 4 % in the period 2008–2012, followed by a new increase of 3 % from 2013 to 2017 [89, 90]. In 2018, reported emissions were lower thanks to alleged reductions of emissions in Germany, Italy, Spain, France and Slovakia [2]. This is, however, inconsistent with the substantial increase in NH₃ columns that is observed from space in 2018 (see figure 3(a)), underlining the urgent need of taking into account meteorological factors in the current state-of-the-art bottom-up emissions inventories [1]. Declining emissions of acidifying compounds, as much as 62 % in the 2008–2018 period for SO_x and 28 % for NO_x in EU-28 [89], also increased the atmospheric lifetime of NH₃ and impacted the trend in the region [23, 91].

In the Middle East, Israel, Jordan and Turkey are characterized by relatively large positive trends over 3 % yr⁻¹, which likely originate from increased emissions. For example, Turkey experienced an important intensification of its agricultural production during the past two decades [92]. During the 2008–2018 period, agricultural use of nitrogen nutrients in the country grew by 2.8 % yr⁻¹ [93], similarly to Israel, while the total anthropogenic emissions increased sharply by 4.8 % yr⁻¹ [89]. While Syria shows a moderate positive trend, several grid cells around Damascus and south of Homs in figure 2 exhibit a downward trend reflecting the decline of atmospheric emissions due to the civil war that started in 2011 [12]. In northern Africa, only Tunisia and Egypt present significant positive changes in NH₃ columns. The latter, characterized by an upward trend of 2.39 ± 0.82 % yr⁻¹ due to intensive agriculture in the Nile Delta and River, is known to be the largest fertilizer consumer in Africa and to have one of the highest nitrogen application rates in the world [94]. Elrys et al [94] also discusses the strong increase in gaseous NH₃ emissions in 2014–2016 following the enhanced nitrogen use on croplands in the country. Figure 2(a) shows that significant increasing trends are also found along the coast of Algeria and especially Morocco, even though for these countries as a whole the trends are not significant.

Western and central Africa are characterized by a strong upward trend in atmospheric NH₃ total columns that is in absolute terms of a similar magnitude than east Asia ((20.3 ± 1.6) × 10¹³ molec cm⁻² yr⁻¹), but lower in relative (2.58 ± 0.23 % yr⁻¹) (see table 1). This region is dominated by biomass burning emissions associated with agricultural practices [95]. For example, Nigeria presents an upward trend of 3.38 ± 0.62 % yr⁻¹. Using the 2008–2017 data record from a previous version of the IASI-NH₃ dataset, a national increase of 6 % yr⁻¹ has been reported for the February–March period which was attributed to agricultural preparation in slash-and-burn cropping systems [96]. In addition, it is worth noting that the agricultural use of nitrogen nutrient in the country increased strongly by 12 % yr⁻¹ during the 2008–2018 period [93]. In eastern Africa, South Sudan stands out with a downward trend of −0.77 ± 0.47 % yr⁻¹. This is likely related to changes in wetland extent in the Sudd, a vast swamp located in this country [96]. The regional conflict that broke out in 2013 also drastically affected agricultural activities, with a cereal production reduced by 25 % in 2017 and a drop in livestock populations [97, 98]. The entire eastern Africa presents a very slight upward trend likely driven by increased pyrogenic emissions in the northeastern part of Democratic Republic of the Congo and in the southwestern part of Ethiopia.

The relatively small decadal change in NH₃ total columns reported in South America (6.2 ± 3.5 %) hides regional and national disparities (figure 2). The northwestern coastline, extending from Venezuela to Peru, is the region with the largest positive rates. This is also seen in the EDGAR derived trends, for which these increases relate to agricultural emissions. The growing poultry production along the Peruvian coast is for instance well documented [9]. The positive trend in Brazil is the result of more intense pyrogenic emissions in the central part of the country and, according to EDGAR, increases in anthropogenic emissions in the southeastern region around Sao Paulo (see figure B4). Jankowski et al [99] also describes how intensification of the Amazon agriculture worsens nitrogen pollution. Bolivia and Paraguay exhibit negative trends around −3 % yr⁻¹ related to important biomass burning episodes that occurred in 2010 (figures 3(b) and B5).

In the U.S., IASI NH₃ columns rose by 3.42 ± 0.59 % yr⁻¹. This result is in line with the trends obtained from the AIRS satellite (2.6 % yr⁻¹ over 2002–2016 [37]) and from ground-based measurements (e.g. [21]). Modeling studies have provided evidence that the upward trend of gas-phase NH₃ in the U.S. is partly due to reduced SO_x and NO_x emissions [100, 101]. However, it has also been shown that changing meteorological factors (e.g. drought, temperature) play a role in the increase of NH₃ concentrations in the region [101, 102]. Reported national emissions decreased from 2008 to 2014 by 3.4 % yr⁻¹, but showed an upturn in the following years to reach the same level in 2017–2018 as in 2008 [103]. At the state scale, the National Emissions Inventory (NEI) from the Environmental Protection Agency (EPA) reports a generally increasing emission trend in the western states, but a declining trend in the central-eastern states [104]. Satellite observations present nonetheless a positive trend over the entire country (figures 2(a) and (b)). The peak in 2012 in figure 3(a) could be related to higher temperatures in the summer and a related increase in NH₃ volatilization from soils, as reported for NO_x soil emissions [101]. At present, NH₃ plays a key role in nitrogen deposition in the country (contributing up to 65 % in some places), and these deposition fluxes will be difficult to mitigate without reducing emissions [105]. A significant positive trend of 1.53 ± 0.83 % yr⁻¹ is also measured in Canada (note that Yamanouchi et al [106] recently reported a trend of 8.38 ± 0.77 % yr⁻¹ at the city scale of Toronto using the same IASI dataset). While the national emission inventory reports more or less constant anthropogenic emissions over the 2008–2018 period [107], biomass burning sustains the increasing trend in NH₃ total columns at northern latitudes [108, 109]. EDGAR presents a pronounced discontinuity between the trend reported for the U.S. and Canada (figure B4).

The calculated trends for Australia are in relative terms quite large at −4.53 ± 0.45 % yr⁻¹. It is however important to note that in absolute terms this decline is almost negligible and artificial. In fact, inspection of figure 2 shows declines below 0.5 × 10¹⁵ molec cm⁻² yr⁻¹ in most of the Southern hemisphere at the latitude of Australia. These could be related to the misclassification of clouds during the early 2008–2018 period (see section 2.1 and [59]), or due to an imperfect CO₂ trend correction (see appendix A). For the same reason, trends in Argentina, Chile and South Africa are to be interpreted with caution. The trends over the ice sheets of Antarctica and Greenland are spurious, and exacerbated by the general poorer performance of the NH₃ retrieval over cold surfaces (see again appendix A).

From the national trends we have calculated a worldwide decadal increase in atmospheric NH₃ total columns of 12.8 ± 1.3 %, which corresponds to a positive growth rate of 1.21 ± 0.13 % yr⁻¹. Note that these numbers are for land only. Trends over coastal areas follow in general those observed over the nearby land regions located upwind. For example, a significant positive trend in transported NH₃ is clearly identifiable in the Gulf of Guinea (southern coast of western Africa), in the Yellow Sea (east coast of China) and in the Caribbean Sea (northern coast of Colombia). Conversely, following the decline in NH₃ total columns observed in southeastern India, a negative trend is calculated over the Bay of Bengal and the Arabian Sea.

The Netherlands was one of the first countries worldwide to implement NH₃ abatement measures in the 1980s. This included regulation of manure application rates, introducing the mineral accounting system, introduction of emission poor housing systems, manure storage coverage and injection of manure in the soil. Since the early 1990s, NH₃ is measured hourly at eight locations in the country from the ground-based stations of the National Air Quality Monitoring Network (or LML standing for 'Landelijk Meetnet Luchtkwaliteit'), which was set up to monitor the Dutch NH₃ emissions abatement policies [22, 110]. In 2005, the LML network was extended by measurements with passive samplers in the Measuring Ammonia in Nature (MAN) network to follow the NH₃ concentrations in nature areas [111, 112].

More than 20 years ago, a discrepancy was observed between these NH₃ measurements and expected levels derived from estimated NH₃ emissions in the Netherlands [113]. Different reasons were found for this mismatch: (a) a changing chemical climate which affected the conversion rate of NH₃ to NH$_4^+$; (b) a reduction of acidifying compounds such as SO₂ and NO_x both in the atmosphere as well as on the surface leading to more NH₃ in the atmosphere; (c) less effective abatement measures in practice as compared to measured lab reductions; (d) fraud with manure transports and (e) the contribution of unknown sources such as the sea and the senescence of leaves [114–116].

LML NH₃ concentrations measured at the surface show a downward trend of 36 % for the 1993–2004 period, while an upward trend of 19 % is reported for 2005–2014 [22]. In contrast, the official NH₃ emissions reported in the framework of the Gothenburg Protocol decreased for the entire period in the Netherlands and are currently 63.1 % lower than in 1990, even though since 2010, these have leveled off [89]. This is illustrated in the top panel of figure 4 which shows the evolution of the reported emissions (1990–2018, Gg yr⁻¹, black) as well as yearly NH₃ surface concentrations from the LML (1992–2018, µg m⁻³, orange) and the MAN network (2005–2018, µg m⁻³, dashed orange). van Zanten et al [22] have shown that the comparison between the emission and concentration trend improves when the influence of meteorological conditions on the concentrations is taken into account.

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Using 11 years (2008–2018) of IASI satellite daily observations of NH₃ columns, we calculate an increasing trend of 3.6 ± 1.9 % yr⁻¹ in the Netherlands. Over the same time-period, the daily ground-based NH₃ concentrations measured at five LML sites exhibit a consistent 2.5 ± 0.5 % yr⁻¹ growth rate. The bottom panel of figure 4 presents the annual NH₃ time series for IASI/Metop-A (molec cm⁻², blue), IASI/Metop-B (molec cm⁻², dashed blue) and LML (µg m⁻³, orange). A sharp increase in the annual mean is measured in 2018, due to the exceptionally warm, sunny and dry weather conditions during that year, as NH₃ volatilization strongly increases with temperature and as deposition rates are lower when it is drier [1, 86–88].

In 2018, the European Court of Justice advised that the current Dutch legislation was not strict enough to protect Natura 2000 areas from nitrogen deposition [117], as required by the European Habitat Directive (EHD) (directive 92/43/EEG). This led to several rulings by the Dutch Council of State in 2019, putting on hold more than 18 000 projects on building houses and roads and in the agricultural sector and thus leading to the 'Dutch Nitrogen crisis'. The proposed policy to halve the country's livestock population to reduce nitrogen deposition caused massive demonstrations from farmers [44]. A special commission was put in place, the Commission Remkes, to advice about the long-term policies to reduce nitrogen. They recommended that emissions should be reduced by 50 % in 10 years to protect 75 % of the Natura 2000 against excess nitrogen deposition and that on a local scale, further reductions are necessary.

Using the data record from the IASI sounder we have obtained and characterized the evolution of atmospheric NH₃ at global, regional and national scales from 2008 to 2018. We have reported large increases of NH₃ in several subcontinental regions over the last decade, especially in east Asia (75.7 ± 6.3 %) but also in western and central Africa (29.0 ± 2.3 %), North America (26.8 ± 4.5 %) and western and southern Europe (20.8 ± 4.3 %). The upward trends observed in many countries can be attributed to a combination of increasing emissions and a longer residence time of NH₃ in the atmosphere due to declining emissions of sulfur and nitrogen oxides. Regions dominated by biomass burning emissions exhibit decreasing or increasing trends depending on when the strongest events took place. Apart from declines related to fires, notable declines were also found in the southwestern part of India and central Asia.

In view of the major role of NH₃ for the loss of biodiversity, for air quality and human health, emissions need to be reduced urgently. A series of options exists to control the loss of NH₃ from agricultural activities to the atmosphere (e.g. [118]). Limiting these atmospheric NH₃ losses would also have co-benefits for our climate [119]. Recent studies have shown that the abatement costs to reduce NH₃ emissions is much lower than the economical and societal benefits (see [120] for Europe and [121] for China), which should trigger our willingness for action. Current and planned infrared satellite missions provide the necessary observational means to monitor the effect of implemented policies (e.g. [122, 123]) to support the goals of the Sustainable Nitrogen Management resolution (UNEP/EA.4/Res.14) adopted by the United Nations Environment Assembly on 15 March 2019 [124].

IASI has been developed and built under the responsibility of the Centre National d'Études Spatiales (CNES, France). It is flown on board the Metop satellites as part of the EUMETSAT Polar System. The IASI L1c data are received through the EUMETCast near real-time data distribution service. National and regional maps have been made with Natural Earth (naturalearthdata.com). The research was funded by the F.R.S.-FNRS and the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement IASI.FLOW). M Van Damme is Postdoctoral Researcher (Chargé de Recherche) and L Clarisse is Research Associate (Chercheur Qualifié) both supported by the Belgian F.R.S.-FNRS. M A Sutton acknowledges support from the Global Environment Facility (GEF) through the UN Environment Programme for the Towards INMS project. C Clerbaux is grateful to CNES for scientific collaboration and financial support.

The IASI-NH₃ datasets are available from the Aeris data infrastructure (http://iasi.aeris-data.fr/NH3). It is also planned to be operationally distributed by EUMETCast under the auspices of the EUMETSAT Atmospheric Monitoring Satellite Application Facility (AC-SAF; http://ac-saf.eumetsat.int).

The ANNI-NH₃-v3 IASI product builds on the heritage of version 1 [52], version 2 [53], and recent improvements in the neural network (NN) retrieval setup introduced in Franco et al [54] for the retrieval of volatile organic compounds (VOCs). We refer to the above-mentioned papers for a detailed description of the retrieval methodology. The specific changes from v2.2 to v3 for NH₃ are outlined in detail below.

A.1. Changes to the HRI and debiasing procedures

The Hyperspectral Range Index (HRI) has been set up following the iterative procedure outlined in [54]. The spectral range has been slightly reduced (812–1126 cm⁻¹) to minimize interferences from other species and/or local variation in surface emissivity. The end result is that the HRI is more sensitive to NH₃ and less affected by interferences.

Analysing the initial time series of the mean HRI over remote oceans, we noticed (a) offsets that coincided with changes to the IASI instrument, (b) a slowly decreasing trend and (c) a residual dependence on H₂O. In the rest of the section we outline the first order corrections that were introduced to account for all of these.

The declining trend over remote areas that was identified in the HRI of NH₃ is apparent in the top panel of figure A1. As the trend is linear, and as there are a couple of weak CO₂ absorption bands in the 812–1126 cm⁻¹ spectral range, this trend is most likely due to the ever increasing concentrations of CO₂. To correct this bias, we analyzed monthly averaged HRI from IASI spectra measured over a remote location in the Pacific Ocean (17° N–22° N; 153° W–158° W) versus time (figure A2). The linear regression ($y = -8.69 \times 10^{-5}x+63.75$, r =−0.84, with x and y being the time (in months) and the HRI (no unit), respectively) models the relationship well and was therefore used to apply a first-order correction to the calculated HRI.

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On 7 June 2017, a minor change in the configuration parameters for the apodization function of IASI/Metop-A instrument had a clear impact on the calculated HRI (figure A1, panels (a)–(c)). This recalibration made IASI/Metop-A more in line with IASI/Metop-B instrument. As the HRI is based on a covariance matrix from spectra of the year 2013, the HRI calculated after the recalibration for IASI/Metop-A have to be adjusted, as well as the entire time series of IASI/Metop-B. Comparison of the HRI values on 6 June with the ones from 8 June 2017, revealed a temperature dependence in the offset. A satisfactory correction was obtained using a linear regression ($y = -3.5 \times 10^{-3} x -0.69$, r = 0.89, with x being the temperature of the baseline (in K) and y the median of the HRI difference between the 6 and the 8 June 2017 (no unit); see figure A1, panel (d)).

Another change in the IASI Level 1C occurred on 18 May 2010 [125] and corresponds to an improvement of the spectral calibration [126]. An empirical correction was introduced as a function of latitude and day of the year. The precise offsets were computed as the difference between the median HRI calculated before and after the 18 May 2010, the median being calculated in 1° latitude bins from all the HRI with an absolute longitude above 160° and an absolute value below 5. This difference was calculated for each day of the year and applied to the HRI calculated before the 18 May 2010 (figure A1, panel (c)).

Finally, a H₂O correction similar to the one applied in the previous ANNI–NH₃ version (already described in [53]) was implemented. This does not change the behavior of the HRI over time, but helps to de-bias it (i.e. after the correction, the mean HRI over remote oceans is closer to zero). Panel (e) of figure A1 presents the corrected monthly time series of HRI over three remote locations. It shows that the corrections allow us to obtain a coherent time series over the IASI operating period, centered around zero and as expected without noticeable jumps or trends.

A.2. Changes to the neural network architecture and training

The following series of changes have been introduced:

• The size of the network was increased from one computational layer of 15 neurons [52] to two layers of 12 nodes.
• In terms of input variables, similarly to the treatment of VOCs [54], we now use a coarse H₂O profile as input to the network, as opposed to the total column that was used before. In addition, three extra temperature levels are introduced in the lower troposphere (at 0.5, 1.5 and 2.5 km above the surface). Especially in the evening, when thermal inversion can occur, it is expected that this change results in a more accurate retrieval. Finally, the surface temperature is kept as an input parameter to the network instead of a baseline temperature used for the VOCs.
• The range of thermal contrast situations in the training set was artificially increased to better train the network. In addition, the total number of samples in the training set increased from 450 000 to close to 500 000 (also because now two networks are trained, as explained in the next point).
• Similarly to the previous versions of the NN retrieval of NH₃, the vertical profile of NH₃ was parameterized with a Gaussian function for the forward simulations. It is now defined as:
  $$\begin{eqnarray} &&\textrm{NH}_3 (\textrm{vmr}) = \textrm{ScalFact} \cdot \textrm{e}^{-(\frac{(z-z_0)^2}{2\sigma^2})}. \end{eqnarray} \tag{ A1 }$$
  Two different training sets have been built:(a)One representative for observations close to emission sources (thus with the peak concentration at the surface), where z₀ was fixed to 0 km and where sigma (σ) was assigned a random number between 100 m and 6 km.(b)One representative for transported NH₃, with a peak concentration above the surface. Here z₀ was assigned a random number between 0 and 20 km.
• The training performance is evaluated in figure A3 and shows similar good performances as in the previous versions.

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A.3. Changes to the input data and post-filtering

A.4. Example

Overall and on average, the v3 does not differ significantly from v2, although differences can be large on individual observations: for columns above 4 × 10¹⁵ molec cm⁻², 80 % of the data agree to within 20 % [62]. As an illustration, figure A4 presents the IASI-NH₃ 10-year averaged distributions from the four datasets (v2.2, v2.2R, v3 and v3R). The averaged columns are slightly larger in the reanalyzed versions, and higher for v3 than for v2. One notable regression in v3 is the performance over ice sheets at high latitude, which yield a larger mean NH₃ column than in v2.2. This is likely related to the fact that the current post-filter is less stringent and was tuned for the tropics and mid-latitudes.

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