# Trends and seasonal variability in ammonia across major biomes in western and central Africa inferred from long-term series of ground-based and satellite measurements

Money Ossohou, Jonathan Edward Hickman, Lieven Clarisse, Pierre-François Coheur, Martin van Damme, Marcellin Adon, Véronique Yoboué, Eric Gardrat, Maria Dias Alvès, Corinne Galy-Lacaux

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Money Ossohou, Jonathan Edward Hickman, Lieven Clarisse, Pierre-François Coheur, Martin van Damme, et al. Trends and seasonal variability in ammonia across major biomes in western and central Africa inferred from long-term series of ground-based and satellite measurements. Atmospheric Chemistry and Physics, 2023, 23, pp.9473 - 9494. 10.5194/acp-23-9473-2023. insu-04553239

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# Trends and seasonal variability in ammonia across major biomes in western and central Africa inferred from long-term series of ground-based and satellite measurements

Money Ossohou, Jonathan Edward Hickman, Lieven Clarisse, Pierre-François Coheur, Martin van Damme, Marcellin Adon, Véronique Yoboué, Eric Gardrat, Maria Dias Alvès, Corinne Galy-Lacaux

► To cite this version:

Money Ossohou, Jonathan Edward Hickman, Lieven Clarisse, Pierre-François Coheur, Martin van Damme, et al. Trends and seasonal variability in ammonia across major biomes in western and central Africa inferred from long-term series of ground-based and satellite measurements. Atmospheric Chemistry and Physics, 2023, 23, pp.9473 - 9494. 10.5194/acp-23-9473-2023. insu-04553239

HAL Id: insu-04553239

https://insu.hal.science/insu-04553239v1

Submitted on 19 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
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teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
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Distributed under a Creative Commons Attribution 4.0 International License
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# Trends and seasonal variability in ammonia across major biomes in western and central Africa inferred from long-term series of ground-based and satellite measurements

Money Ossohou<fnref n="1" />,<fnref n="2" />, Jonathan Edward Hickman<fnref n="3" />, Lieven Clarisse<fnref n="4" />, Pierre-François Coheur<fnref n="4" />, Martin Van Damme<fnref n="4" />,<fnref n="5" />, Marcellin Adon<fnref n="2" />,<fnref n="6" />, Véronique Yoboué<fnref n="1" />, Eric Gardrat<fnref n="7" />, Maria Dias Alvès<fnref n="7" />, and Corinne Galy-Lacaux<fnref n="7" />

**Correspondence:** Money Ossohou (ossohoumoney@gmail.com)

Received: 22 November 2022 – Discussion started: 16 January 2023

Revised: 4 July 2023 – Accepted: 11 July 2023 – Published: 28 August 2023

**Abstract.** Ammonia (NH₃) is the most abundant alkaline component in the atmosphere. Changes in NH₃ con- centrations have important implications for atmospheric chemistry, air quality, and ecosystem integrity. We present a long-term ammonia (NH₃) assessment in the western and central African regions within the frame- work of the International Network to study Deposition and Atmospheric chemistry in Africa (INDAAF) pro- gramme. We analyse seasonal variations and trends in NH₃ concentrations and total column densities along an African ecosystem transect spanning dry savannas in Banizoumbou, Niger, and Katibougou, Mali; wet sa- vannas in Djougou, Benin, and Lamto, Côte d'Ivoire; and forests in Bomassa, Republic of the Congo, and Zoétélé, Cameroon. We use a 21-year record of observations (1998–2018) from INDAAF passive samplers and an 11-year record of observations (2008–2018) of atmospheric vertical column densities from the Infrared Atmo- spheric Sounding Interferometer (IASI) to evaluate NH₃ ground-based concentrations and total column densities, respectively. Climatic data (air temperature, rainfall amount, and leaf area index), as well as ammonia emis- sion data of biomass combustion from the fourth version of the Global Fire Emissions Database (GFED4) and anthropogenic sources from the Community Emissions Data System (CEDS), were compared with total NH₃ concentrations and total columns over the same periods. Annual mean ground-based NH₃ concentrations are around 5.7–5.8 ppb in dry savannas, 3.5–4.7 ppb in wet savannas, and 3.4–5.6 ppb in forests. Annual IASI NH₃ total column densities are 10.0–10.7 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in dry savanna, 16.0–20.9 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in wet savanna, and 12.4–13.8 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in forest stations. Non-parametric statistical Mann–Kendall trend tests applied to annual data show that ground-based NH₃ concentrations increase at Bomassa (+2.56 % yr⁻<fnref n="1" />) but decrease at Zoétélé (−2.95 % yr⁻<fnref n="1" />) over the 21-year period. The 11-year period of IASI NH₃ total column density measurements show yearly increasing trends at Katibougou (+3.46 % yr⁻<fnref n="1" />), Djougou (+2.24 % yr⁻<fnref n="1" />), and Zoétélé (+3.42 % yr⁻<fnref n="1" />). From the outcome of our investigation, we conclude that air temperature, leaf area

<footnote n="1">¹Department of Physics, University of Man, Man, Côte d'Ivoire</footnote>

<footnote n="2">²Laboratoire des Sciences de la Matière, de l'Environnement et de l'Energie Solaire, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire</footnote>

<footnote n="3">³Center for Climate Systems Research, Columbia Climate School, Columbia University, New York, NY, USA</footnote>

<footnote n="4">⁴Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Université Libre de Bruxelles (ULB), Brussels, Belgium</footnote>

<footnote n="5">⁵Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium</footnote>

<footnote n="6">⁶Laboratoire des Sciences et Techniques de l'Environnement, Université Jean Lorougnon Guédé, Daloa, Côte d'Ivoire</footnote>

<footnote n="7">⁷Laboratoire d'Aérologie, Université Toulouse III Paul Sabatier, CNRS, Toulouse, France</footnote>
<figure><img src="image_2.png" /></figure>

Trends and seasonal variability in ammonia across
major biomes in western and central Africa inferred
from long-term series of ground-based and
satellite measurements

Money Ossohou<fnref n="1" />,<fnref n="2" />, Jonathan Edward Hickman<fnref n="3" />, Lieven Clarisse<fnref n="4" />, Pierre-François Coheur<fnref n="4" />,
Martin Van Damme<fnref n="4" />,<fnref n="5" />, Marcellin Adon<fnref n="2" />,<fnref n="6" />, Véronique Yoboué<fnref n="1" />, Eric Gardrat<fnref n="7" />, Maria Dias Alvès<fnref n="7" />, and
Corinne Galy-Lacaux<fnref n="7" />

**Correspondence:** Money Ossohou (ossohoumoney@gmail.com)

Received: 22 November 2022 – Discussion started: 16 January 2023

Revised: 4 July 2023 – Accepted: 11 July 2023 – Published: 28 August 2023

**Abstract.** Ammonia (NH₃) is the most abundant alkaline component in the atmosphere. Changes in NH₃ con-
centrations have important implications for atmospheric chemistry, air quality, and ecosystem integrity. We
present a long-term ammonia (NH₃) assessment in the western and central African regions within the frame-
work of the International Network to study Deposition and Atmospheric chemistry in Africa (INDAAF) pro-
gramme. We analyse seasonal variations and trends in NH₃ concentrations and total column densities along
an African ecosystem transect spanning dry savannas in Banizoumbou, Niger, and Katibougou, Mali; wet sa-
vannas in Djougou, Benin, and Lamto, Côte d'Ivoire; and forests in Bomassa, Republic of the Congo, and
Zoétélé, Cameroon. We use a 21-year record of observations (1998–2018) from INDAAF passive samplers and
an 11-year record of observations (2008–2018) of atmospheric vertical column densities from the Infrared Atmo-
spheric Sounding Interferometer (IASI) to evaluate NH₃ ground-based concentrations and total column densities,
respectively. Climatic data (air temperature, rainfall amount, and leaf area index), as well as ammonia emis-
sion data of biomass combustion from the fourth version of the Global Fire Emissions Database (GFED4) and
anthropogenic sources from the Community Emissions Data System (CEDS), were compared with total NH₃
concentrations and total columns over the same periods. Annual mean ground-based NH₃ concentrations are
around 5.7–5.8 ppb in dry savannas, 3.5–4.7 ppb in wet savannas, and 3.4–5.6 ppb in forests. Annual IASI NH₃
total column densities are 10.0–10.7 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in dry savanna, 16.0–20.9 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in wet
savanna, and 12.4–13.8 × 10¹<fnref n="5" /> molec. cm⁻<fnref n="2" /> in forest stations. Non-parametric statistical Mann–Kendall trend
tests applied to annual data show that ground-based NH₃ concentrations increase at Bomassa (+2.56 % yr⁻<fnref n="1" />)
but decrease at Zoétélé (−2.95 % yr⁻<fnref n="1" />) over the 21-year period. The 11-year period of IASI NH₃ total column
density measurements show yearly increasing trends at Katibougou (+3.46 % yr⁻<fnref n="1" />), Djougou (+2.24 % yr⁻<fnref n="1" />),
and Zoétélé (+3.42 % yr⁻<fnref n="1" />). From the outcome of our investigation, we conclude that air temperature, leaf area

<footnote n="1">¹Department of Physics, University of Man, Man, Côte d'Ivoire</footnote>

<footnote n="2">²Laboratoire des Sciences de la Matière, de l'Environnement et de l'Energie Solaire,
Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire</footnote>

<footnote n="3">³Center for Climate Systems Research, Columbia Climate School, Columbia University, New York, NY, USA</footnote>

<footnote n="4">⁴Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES),
Université Libre de Bruxelles (ULB), Brussels, Belgium</footnote>

<footnote n="5">⁵Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium</footnote>

<footnote n="6">⁶Laboratoire des Sciences et Techniques de l'Environnement,
Université Jean Lorougnon Guédé, Daloa, Côte d'Ivoire</footnote>

<footnote n="7">⁷Laboratoire d'Aérologie, Université Toulouse III Paul Sabatier, CNRS, Toulouse, France</footnote>
index, and rainfall combined with biomass burning, agricultural, and residential activities are the key drivers of atmospheric NH₃ in the INDAAF stations. The results also show that the drivers of trends are (1) agriculture in the dry savanna of Katibougou; (2) air temperature and agriculture in the wet savanna of Djougou and Lamto; and (3) leaf area index, air temperature, residential, and agriculture in the forest of Bomassa.

# 1 Introduction

Atmospheric nitrogen (N) compounds play an important role in all compartments of the critical zone (biosphere–atmosphere–hydrosphere) at the global scale. Since 2002, Bouwman et al. (2002a) have claimed that in the future, both acidification and eutrophication risks due to excess of N could significantly increase in Asia, Africa and South America but decrease in North America and western Europe. Reactive nitrogen (Nr) in the atmosphere, either reduced (NHₓ = NH₃ and NH₄⁺) or oxidized (NOₓ) forms, has a very different role. Ammonia (NH₃), the inorganic form of Nr typically produced through the deprotonation of NH₄⁺, is the most abundant alkaline component in the atmosphere (Behera et al., 2013). In the atmosphere, NH₃ influences the abundance and chemical composition of sulfate particles, primarily from dimethyl sulfide (DMS) emissions arising from planktonic algae (Bouwman and Van Der Hoek, 1997). In the lower troposphere, NH₃ neutralizes a great portion of the acids produced by oxides of sulfur and nitrogen (Adon et al., 2010) and forms fine particulate matter (PM₂.₅) (Malm et al., 2004). Through wet or dry deposition to the surface, NH₃ can be detrimental over time due to an increased toxicity toward sensitive species of plants (Behera et al., 2013; Galoway et al., 2004), ecosystems (Erisman et al., 2013), and soils (Stevens et al., 2018). Different sources contribute to NH₃ emissions on the African continent, which in turn influence the seasonality of atmospheric concentrations and deposition of NH₃. Due to its high reactivity, a significant fraction of the NH₃ emitted is rapidly deposited within a 1 km radius of the source (Fowler et al., 1998). It is clear that the seasonal distributions of NH₃ vary depending on the dominant source type and remain a very important element in understanding local emission sources and changes in environmental conditions (Tang et al., 2018b).

Soil emissions of NOₓ over north equatorial Africa (2.2 Tg N yr⁻¹) account for almost 70% of African soil emissions because of the vast areas covered by dry ecosystems (Jaeglé et al., 2004). In the Sahel region, NH₃ emissions can represent an important N flux in natural ecosystems, cropland, grazed soils (Hickman et al., 2018), and bacterial decomposition of urea in animal excreta (Adon et al., 2010). Indeed, many organisms in soils involved in the decomposition of organic matter excrete NH₃ directly or N compounds that readily hydrolyze to NHₓ (Bouwman et al., 1997). A minimum level of soil moisture is required for the microbial activities, such as urea hydrolysis, which generates NH₃

(Warner et al., 2017). Atmospheric NH₃ has been reported to be influenced by meteorological and physical parameters such as the presence of plants. Due to high temperatures, low soil moisture, and bare soil surfaces conditions, the process of volatilization from soils remains the dominant NH₃ loss in the West African Sahel region (Delon et al., 2010) and Africa contributes 14% of the global source of NH₃ (Bouwman et al., 1997). Likewise, NH₃ volatilization potential from soil and vegetation systems nearly doubles with every 5°C increase in air temperature (Sutton et al., 2013; Pinder et al., 2012). However, the capture of NH₃ at the external surface of the leaf and transport into the leaf interior can be an important sink of atmospheric NH₃ (Van Hove et al., 1987).

According to Giglio et al. (2010), ~250 Mha of land area was burned in the Northern Hemisphere and Southern Hemisphere of Africa for the time period 1997 through 2008. This value represents on average 70% of the global area burned each year. Biomass burning emits large amounts of aerosols and trace gases which significantly affect the biosphere–atmosphere interface, atmospheric chemistry, cloud properties, Earth's radiation budget, global carbon cycle, ecosystems and biodiversity, air quality, and atmospheric circulation (Crutzen and Andreae, 1990; Andreae and Merlet, 2001; Stocker et al., 2013). Recently, Bray et al. (2021) estimated average NH₃ emissions from biomass burning at a global scale over the period 2001–2015 at 4.53 ± 0.51 Tg yr⁻¹. Many scientific papers have shown that biomass burning represents the major source of NH₃ occurring in African savanna and forest ecosystems (Shi et al., 2015; van der Werf et al., 2017). The amount of NH₃ emitted from biomass burning in Africa represents roughly 60% to 70% of global NH₃ emissions from biomass fires (Whitburn et al., 2015). Biomass burning emissions tend to drive seasonal variation in NH₃ total column densities in western Africa, with the largest emissions occurring late in the dry season and early rainy season. Relationships between biomass burning and NH₃ may be observed when evaluating national-scale statistics: countries with the highest rates of increasing vertical column densities (VCDs) of NH₃ also had high rates of growth in CO VCDs; burned area displayed a similar pattern, though not significantly (Hickman et al., 2021).

Satellite measurements of NH₃ provide a means to monitor atmospheric composition globally (Clarisse et al., 2009; Warner et al., 2017) and are a powerful tool for understanding atmospheric composition particularly for regions like Africa, where other types of measurements are scarce (Hickman et al., 2018). The Infrared Atmospheric Sounding Interferom-
index, and rainfall combined with biomass burning, agricultural, and residential activities are the key drivers of atmospheric NH₃ in the INDAAF stations. The results also show that the drivers of trends are (1) agriculture in the dry savanna of Katibougou; (2) air temperature and agriculture in the wet savanna of Djougou and Lamto; and (3) leaf area index, air temperature, residential, and agriculture in the forest of Bomassa.

# 1 Introduction

Atmospheric nitrogen (N) compounds play an important role in all compartments of the critical zone (biosphere–atmosphere–hydrosphere) at the global scale. Since 2002, Bouwman et al. (2002a) have claimed that in the future, both acidification and eutrophication risks due to excess of N could significantly increase in Asia, Africa and South America but decrease in North America and western Europe. Reactive nitrogen (Nr) in the atmosphere, either reduced ($NH_x = NH_3$ and $NH_4^+$) or oxidized ($NO_x$) forms, has a very different role. Ammonia ($NH_3$), the inorganic form of Nr typically produced through the deprotonation of $NH_4^+$, is the most abundant alkaline component in the atmosphere (Behera et al., 2013). In the atmosphere, $NH_3$ influences the abundance and chemical composition of sulfate particles, primarily from dimethyl sulfide (DMS) emissions arising from planktonic algae (Bouwman and Van Der Hoek, 1997). In the lower troposphere, $NH_3$ neutralizes a great portion of the acids produced by oxides of sulfur and nitrogen (Adon et al., 2010) and forms fine particulate matter ($PM_{2.5}$) (Malm et al., 2004). Through wet or dry deposition to the surface, $NH_3$ can be detrimental over time due to an increased toxicity toward sensitive species of plants (Behera et al., 2013; Galoway et al., 2004), ecosystems (Erisman et al., 2013), and soils (Stevens et al., 2018). Different sources contribute to $NH_3$ emissions on the African continent, which in turn influence the seasonality of atmospheric concentrations and deposition of $NH_3$. Due to its high reactivity, a significant fraction of the $NH_3$ emitted is rapidly deposited within a 1 km radius of the source (Fowler et al., 1998). It is clear that the seasonal distributions of $NH_3$ vary depending on the dominant source type and remain a very important element in understanding local emission sources and changes in environmental conditions (Tang et al., 2018b).

Soil emissions of $NO_x$ over north equatorial Africa ($2.2 \text{ Tg N yr}^{-1}$) account for almost 70% of African soil emissions because of the vast areas covered by dry ecosystems (Jaeglé et al., 2004). In the Sahel region, $NH_3$ emissions can represent an important N flux in natural ecosystems, cropland, grazed soils (Hickman et al., 2018), and bacterial decomposition of urea in animal excreta (Adon et al., 2010). Indeed, many organisms in soils involved in the decomposition of organic matter excrete $NH_3$ directly or N compounds that readily hydrolyze to $NH_x$ (Bouwman et al., 1997). A minimum level of soil moisture is required for the microbial activities, such as urea hydrolysis, which generates $NH_3$

(Warner et al., 2017). Atmospheric $NH_3$ has been reported to be influenced by meteorological and physical parameters such as the presence of plants. Due to high temperatures, low soil moisture, and bare soil surfaces conditions, the process of volatilization from soils remains the dominant $NH_3$ loss in the West African Sahel region (Delon et al., 2010) and Africa contributes 14% of the global source of $NH_3$ (Bouwman et al., 1997). Likewise, $NH_3$ volatilization potential from soil and vegetation systems nearly doubles with every 5°C increase in air temperature (Sutton et al., 2013; Pinder et al., 2012). However, the capture of $NH_3$ at the external surface of the leaf and transport into the leaf interior can be an important sink of atmospheric $NH_3$ (Van Hove et al., 1987).

According to Giglio et al. (2010), ~250 Mha of land area was burned in the Northern Hemisphere and Southern Hemisphere of Africa for the time period 1997 through 2008. This value represents on average 70% of the global area burned each year. Biomass burning emits large amounts of aerosols and trace gases which significantly affect the biosphere–atmosphere interface, atmospheric chemistry, cloud properties, Earth's radiation budget, global carbon cycle, ecosystems and biodiversity, air quality, and atmospheric circulation (Crutzen and Andreae, 1990; Andreae and Merlet, 2001; Stocker et al., 2013). Recently, Bray et al. (2021) estimated average $NH_3$ emissions from biomass burning at a global scale over the period 2001–2015 at $4.53 \pm 0.51 \text{ Tg yr}^{-1}$. Many scientific papers have shown that biomass burning represents the major source of $NH_3$ occurring in African savanna and forest ecosystems (Shi et al., 2015; van der Werf et al., 2017). The amount of $NH_3$ emitted from biomass burning in Africa represents roughly 60% to 70% of global $NH_3$ emissions from biomass fires (Whitburn et al., 2015). Biomass burning emissions tend to drive seasonal variation in $NH_3$ total column densities in western Africa, with the largest emissions occurring late in the dry season and early rainy season. Relationships between biomass burning and $NH_3$ may be observed when evaluating national-scale statistics: countries with the highest rates of increasing vertical column densities (VCDs) of $NH_3$ also had high rates of growth in CO VCDs; burned area displayed a similar pattern, though not significantly (Hickman et al., 2021).

Satellite measurements of $NH_3$ provide a means to monitor atmospheric composition globally (Clarisse et al., 2009; Warner et al., 2017) and are a powerful tool for understanding atmospheric composition particularly for regions like Africa, where other types of measurements are scarce (Hickman et al., 2018). The Infrared Atmospheric Sounding Interferom-
eter (IASI) datasets have been validated based on aircraft and ground-based measurements. The IASI version 3 NH₃ measurements are accurate at the scale of an individual pixel size of 12 km in diameter (Guo et al., 2021). Previous validation work comparing older versions of the IASI product with ground-based Fourier transform infrared (FTIR) observations of NH₃ total columns has also shown robust correlations at sites with high NH₃ concentrations but lower at sites where atmospheric concentrations approach IASI's detection limits (Dammers et al., 2017). Although FTIR observations are absent from Africa, earlier works have shown fair agreement between previous versions of IASI total column densities and International Network to study Deposition and Atmospheric chemistry in Africa (INDAAF) NH₃ surface observations in western Africa (Van Damme et al., 2015) and seasonal patterns (Hickman et al., 2018; Ossohou et al., 2019). During the year 2008, Hickman et al. (2018) found elevated total columns of NH₃ from the IASI in the Sahel during March-April mainly due to the Birch effect. Through recent improvements in retrieval algorithms, Van Damme et al. (2021) used version 3 of the IASI NH₃ total column datasets to characterize the evolution of atmospheric NH₃ at global, national, and regional scales over an 11-year period (2008–2018). Using a statistical trend method based on least squares regression and bootstrap resampling, Van Damme et al. (2021) found large increases in NH₃ in several subcontinental regions over the last decade, especially in western and central Africa (29.0 ± 2.3 % per decade).

Based on a 10-year period of ground-based measurements within the framework of the INDAAF programme, Adon et al. (2010) documented surface concentrations and seasonal cycles according to the atmospheric sources of NH₃ in western and central Africa. INDAAF has been a long-term monitoring measurement network since 1995 to document atmospheric chemistry and deposition fluxes in Africa. This programme is part of the European Aerosol, Clouds and Trace Gases Research Infrastructure – France (ACTRIS-FR) and of the International Global Atmospheric Chemistry’s Deposition of Biogeochemically Important Trace Species (IGAC/DEBITS) activities. In addition, it is a labelled contributing network to the Global Atmospheric Watch programme of the World Meteorological Organization (GAW/WMO). INDAAF measurements are of great interest to remove some uncertainties in order to understand the seasonality of several trace gases including NH₃ in western and central Africa. Some uncertainties are caused by the scarcity of data on the spatial and temporal distribution of application of synthetic fertilizers and animal manure by crop, as well as the prevailing management conditions (Beusen et al., 2008).

Here we provide updated analyses of these long-term records, complemented with satellite retrievals, to better understand 21st century NH₃ dynamics in Africa. Specifically, in the framework of the INDAAF programme, this study aims to improve the long-term NH₃ assessment in the western and central African regions. We first compare the

monthly and seasonal patterns in ground-based NH₃ concentrations (1998/2005–2018) and IASI NH₃ total columns (2008–2018) measured at three major African ecosystems: dry savannas, wet savannas, and forests. Monthly and seasonal evolutions allow us to highlight the main sources and factors influencing atmospheric NH₃ levels in the tropical African ecosystems. Secondly, we use non-parametric statistically robust tests to assess long-term trends in NH₃ from surface and satellite measurements over the ecosystemic transect and discuss results according to the analysis of seasonality and meteorological data trends.

# 2 Material and methods

## 2.1 Presentation of sampling sites

Figure 1 shows the location of the eight labelled INDAAF monitoring stations situated in western and central Africa. Each site represents an African regional ecosystem with its own characteristics in terms of emission sources and its sensitivity to climatic, ecological, and anthropogenic changes. Thus, the sites are distributed by pairs according to latitudinal bands with significantly different rainfall patterns to represent dry savanna (Banizoumbou in Niger, Katibougou in Mali), wet savanna (Djougou in Benin, Lamto in Côte d'Ivoire), and equatorial forest (Bomassa in the Republic of the Congo and Zoétélé in Cameroon) ecosystems. Additional details on the monitoring sites can be found in the literature (Abbadie, 2006; Adon et al., 2010; Akpo et al., 2015; Delmas et al., 1995; Diawara et al., 2014; Le Roux et al., 2006; Ossohou et al., 2019; Ouafo-Leumbe et al., 2018; Yoboué et al., 2005). To date, measurements of atmospheric and meteorological physico-chemical parameters are continuing at all the INDAAF sites. These measurements are referenced in the INDAAF database (https://indaafobs-mip.fr, last access: 17 August 2023) and in the WMO OSCAR database (https://oscar.wmo.int/surface/#, last access: 17 August 2023).

The geographical characteristics, soil, vegetation, climate types, and the months representative of the wet and dry seasons of the western and central African sites of interest are described in Table 1. It is important to keep in mind that dry savannas are characterized by a short wet season from June to September, whereas the wet season is longer in wet savanna and forest ecosystems, extending from April to October and March to November, respectively.

## 2.2 NH₃ sampling and chemical analysis

Monitoring of NH₃ in the framework of the INDAAF programme began in 1998 (2005 for Djougou). Sampling was carried out using the INDAAF passive sampler technique inspired by the work of Ferm (1991). The passive samplers were mounted and analysed at the Laboratoire d'Aérologie (LAERO) in Toulouse (France) for all INDAAF sites.
eter (IASI) datasets have been validated based on aircraft and ground-based measurements. The IASI version 3 NH₃ measurements are accurate at the scale of an individual pixel size of 12 km in diameter (Guo et al., 2021). Previous validation work comparing older versions of the IASI product with ground-based Fourier transform infrared (FTIR) observations of NH₃ total columns has also shown robust correlations at sites with high NH₃ concentrations but lower at sites where atmospheric concentrations approach IASI's detection limits (Dammers et al., 2017). Although FTIR observations are absent from Africa, earlier works have shown fair agreement between previous versions of IASI total column densities and International Network to study Deposition and Atmospheric chemistry in Africa (INDAAF) NH₃ surface observations in western Africa (Van Damme et al., 2015) and seasonal patterns (Hickman et al., 2018; Ossohou et al., 2019). During the year 2008, Hickman et al. (2018) found elevated total columns of NH₃ from the IASI in the Sahel during March–April mainly due to the Birch effect. Through recent improvements in retrieval algorithms, Van Damme et al. (2021) used version 3 of the IASI NH₃ total column datasets to characterize the evolution of atmospheric NH₃ at global, national, and regional scales over an 11-year period (2008–2018). Using a statistical trend method based on least squares regression and bootstrap resampling, Van Damme et al. (2021) found large increases in NH₃ in several subcontinental regions over the last decade, especially in western and central Africa (29.0 ± 2.3 % per decade).

Based on a 10-year period of ground-based measurements within the framework of the INDAAF programme, Adon et al. (2010) documented surface concentrations and seasonal cycles according to the atmospheric sources of NH₃ in western and central Africa. INDAAF has been a long-term monitoring measurement network since 1995 to document atmospheric chemistry and deposition fluxes in Africa. This programme is part of the European Aerosol, Clouds and Trace Gases Research Infrastructure – France (ACTRIS-FR) and of the International Global Atmospheric Chemistry’s Deposition of Biogeochemically Important Trace Species (IGAC/DEBITS) activities. In addition, it is a labelled contributing network to the Global Atmospheric Watch programme of the World Meteorological Organization (GAW/WMO). INDAAF measurements are of great interest to remove some uncertainties in order to understand the seasonality of several trace gases including NH₃ in western and central Africa. Some uncertainties are caused by the scarcity of data on the spatial and temporal distribution of application of synthetic fertilizers and animal manure by crop, as well as the prevailing management conditions (Beusen et al., 2008).

Here we provide updated analyses of these long-term records, complemented with satellite retrievals, to better understand 21st century NH₃ dynamics in Africa. Specifically, in the framework of the INDAAF programme, this study aims to improve the long-term NH₃ assessment in the western and central African regions. We first compare the

monthly and seasonal patterns in ground-based NH₃ concentrations (1998/2005–2018) and IASI NH₃ total columns (2008–2018) measured at three major African ecosystems: dry savannas, wet savannas, and forests. Monthly and seasonal evolutions allow us to highlight the main sources and factors influencing atmospheric NH₃ levels in the tropical African ecosystems. Secondly, we use non-parametric statistically robust tests to assess long-term trends in NH₃ from surface and satellite measurements over the ecosystemic transect and discuss results according to the analysis of seasonality and meteorological data trends.

## 2 Material and methods

### 2.1 Presentation of sampling sites

Figure 1 shows the location of the eight labelled INDAAF monitoring stations situated in western and central Africa. Each site represents an African regional ecosystem with its own characteristics in terms of emission sources and its sensitivity to climatic, ecological, and anthropogenic changes. Thus, the sites are distributed by pairs according to latitudinal bands with significantly different rainfall patterns to represent dry savanna (Banizoumbou in Niger, Katibougou in Mali), wet savanna (Djougou in Benin, Lamto in Côte d'Ivoire), and equatorial forest (Bomassa in the Republic of the Congo and Zoétélé in Cameroon) ecosystems. Additional details on the monitoring sites can be found in the literature (Abbadie, 2006; Adon et al., 2010; Akpo et al., 2015; Delmas et al., 1995; Diawara et al., 2014; Le Roux et al., 2006; Ossohou et al., 2019; Ouafo-Leumbe et al., 2018; Yoboué et al., 2005). To date, measurements of atmospheric and meteorological physico-chemical parameters are continuing at all the INDAAF sites. These measurements are referenced in the INDAAF database (https://indaafobs-mip.fr, last access: 17 August 2023) and in the WMO OSCAR database (https://oscar.wmo.int/surface/#/, last access: 17 August 2023).

The geographical characteristics, soil, vegetation, climate types, and the months representative of the wet and dry seasons of the western and central African sites of interest are described in Table 1. It is important to keep in mind that dry savannas are characterized by a short wet season from June to September, whereas the wet season is longer in wet savanna and forest ecosystems, extending from April to October and March to November, respectively.

### 2.2 NH₃ sampling and chemical analysis

Monitoring of NH₃ in the framework of the INDAAF programme began in 1998 (2005 for Djougou). Sampling was carried out using the INDAAF passive sampler technique inspired by the work of Ferm (1991). The passive samplers were mounted and analysed at the Laboratoire d'Aérologie (LAERO) in Toulouse (France) for all INDAAF sites.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** INDAAF measurement network composed of 10 stations across Africa. Presentation of the stations of (1) Banizoumbou (Niger), (3) Katibougou (Mali), (5) Lamto (Côte d'Ivoire), (6) Djougou (Benin), (7) Zoétélé (Cameroon), and (8) Bomassa (Republic of the Congo) stations (adapted from Mayaux et al., 2004; Ossohou et al., 2019).</figcaption></figure>

<table><caption>**Table 1.** Site coordinates and location information (WS: wet season; DS: dry season). Dry savannas (WS: June–September; DS: October–May), wet savannas (WS: April–October; DS: November–March), and forests (WS: March–November; DS: December–February).</caption><thead><tr><th>Ecosystems</th><th>Station</th><th>Latitude, longitude</th><th>Type of soil and/or vegetation</th><th>Climate</th><th>Country</th></tr></thead><tbody><tr><td rowspan="2">Dry savannas</td><td>Banizoumbou (Adon et al., 2013; Delon et al., 2012; de Rouw and Rajot, 2004)</td><td>13°31′ N, 02°38′ E</td><td>91.2 % sandy soils, tiger bush – fallow bush</td><td>Sahelian</td><td>Niger</td></tr><tr><td>Katibougou (Adon et al., 2013; Delon et al., 2012)</td><td>12°56′ N, 07°32′ W</td><td>Sandy soils, deciduous shrubs</td><td>Sudano- Sahelian</td><td>Mali</td></tr><tr><td rowspan="2">Wet savannas</td><td>Djougou (Akpo et al., 2015; Ouafo-Leumbe et al., 2018)</td><td>09°39′ N, 01°44′ E</td><td>Ferralitic and ferruginous soil, mosaic of dry forests and savanna</td><td>Sudano- Guinean</td><td>Benin</td></tr><tr><td>Lamto (Abbadie, 2006; Yoboué et al., 2005)</td><td>06°13′ N, 05°02′ W</td><td>Ferrugineous soils, grass, shrub, and tree stratum</td><td>Guinean</td><td>Côte d'Ivoire</td></tr><tr><td rowspan="2">Forests</td><td>Bomassa (Mitani et al., 1993)</td><td>02°12′ N, 16°20′ E</td><td>Dense evergreen forest</td><td>Equatorial</td><td>Republic of the Congo</td></tr><tr><td>Zoétélé (Sigha-Nkamdjou et al., 2003)</td><td>03°15′ N, 11°53′ E</td><td>Dense evergreen forest</td><td>Equatorial</td><td>Cameroon</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** INDAAF measurement network composed of 10 stations across Africa. Presentation of the stations of (1) Banizoumbou (Niger), (3) Katibougou (Mali), (5) Lamto (Côte d'Ivoire), (6) Djougou (Benin), (7) Zoétélé (Cameroon), and (8) Bomassa (Republic of the Congo) stations (adapted from Mayaux et al., 2004; Ossohou et al., 2019).</figcaption></figure>

<table><caption>**Table 1.** Site coordinates and location information (WS: wet season; DS: dry season). Dry savannas (WS: June–September; DS: October–May), wet savannas (WS: April–October; DS: November–March), and forests (WS: March–November; DS: December–February).</caption><thead><tr><th>Ecosystems</th><th>Station</th><th>Latitude, longitude</th><th>Type of soil and/or vegetation</th><th>Climate</th><th>Country</th></tr></thead><tbody><tr><td rowspan="2">Dry savannas</td><td>Banizoumbou (Adon et al., 2013; Delon et al., 2012; de Rouw and Rajot, 2004)</td><td>13°31′ N, 02°38′ E</td><td>91.2 % sandy soils, tiger bush – fallow bush</td><td>Sahelian</td><td>Niger</td></tr><tr><td>Katibougou (Adon et al., 2013; Delon et al., 2012)</td><td>12°56′ N, 07°32′ W</td><td>Sandy soils, deciduous shrubs</td><td>Sudano-Sahelian</td><td>Mali</td></tr><tr><td rowspan="2">Wet savannas</td><td>Djougou (Akpo et al., 2015; Ouafo-Leumbe et al., 2018)</td><td>09°39′ N, 01°44′ E</td><td>Ferralitic and ferruginous soil, mosaic of dry forests and savanna</td><td>Sudano-Guinean</td><td>Benin</td></tr><tr><td>Lamto (Abbadie, 2006; Yoboué et al., 2005)</td><td>06°13′ N, 05°02′ W</td><td>Ferrugineous soils, grass, shrub, and tree stratum</td><td>Guinean</td><td>Côte d'Ivoire</td></tr><tr><td rowspan="2">Forests</td><td>Bomassa (Mitani et al., 1993)</td><td>02°12′ N, 16°20′ E</td><td>Dense evergreen forest</td><td>Equatorial</td><td>Republic of the Congo</td></tr><tr><td>Zoétélé (Sigha-Nkamdjou et al., 2003)</td><td>03°15′ N, 11°53′ E</td><td>Dense evergreen forest</td><td>Equatorial</td><td>Cameroon</td></tr></tbody></table>
Adon et al. (2010) give a complete overview of the sampling and analytical procedures for the INDAAF passive sampler technique. For each INDAAF site, the passive samplers were made of impregnated filter paper with a species-specific solution for the adsorption of gases. Samplers were exposed for 1 month in duplicates to ensure reproducibility, and monthly concentrations are calculated from the arithmetic mean of the duplicates. Desorbed filters were analysed using ion chromatography (IC). The Laboratoire d'Aérologie participates twice a year in WMO's quality assurance intercomparison programme. Results have always shown that the analytical accuracy of the IC realized at the LAERO is greater than 95%. The intercomparison results of the LAERO are available under the reference 700106 on the WMO website (http://qasac-americas.org/, last access: 24 October 2022). The sampling technique using the INDAAF passive sampler method has been validated on tropical, subtropical, rural, and urban sites in Africa (Adon et al., 2010; Bahino et al., 2018; Ossohou et al., 2020). INDAAF passive samplers have proven to be accurate, cheaper, easy to use, and useful for air quality monitoring.

The precision of the measurements of passive samplers, evaluated through covariance with duplicates, was estimated at 14.3% for NH₃ (Adon et al., 2010). The detection limit for NH₃ was calculated from field blanks and is equal to 0.7 ± 0.2 ppb. Values below the detection limit, as well as non-valid reproducibility values, were removed from the database. Thus, the percentages of valid data in the final database for the studied period 1998/2005–2018 are 97% for Banizoumbou, 93% for Katibougou, 90% for Djougou, 94% for Lamto, 73% for Bomassa, and 93% for Zoétélé.

## 2.3 Biomass burning and anthropogenic emissions of NH₃

The fourth version of the Global Fire Emissions Database (GFED4) provides monthly biomass burning emissions at 0.25° resolution from 1997 onwards from all biomass burning sources, i.e. many sectors (agricultural waste burning, boreal forest fires, peat fires, savanna fires, grassland fires, shrubland fires, temperate forest fires, and tropical deforestation and degradation). Emissions of NH₃ from biomass burning sources were downloaded for the 1° × 1° grid cell containing each INDAAF site. The GFED4 emissions are based on the combination of satellite information on fire activity and vegetation productivity to estimate gridded monthly burned area and fire emissions, as well as scalars that can be used to calculate higher-temporal-resolution emissions (Giglio et al., 2013; van der Werf et al., 2017). The Global Fire Emissions Database – currently by far the most widely used global fire emissions inventory – has been widely cited in the literature, and GFED4 data can be downloaded from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.sedoo.fr/#/metadata/433, last access: 31 May 2023).

The Community Emissions Data System (CEDS) produces consistent estimates of global air emissions species from anthropogenic sources (Smith et al., 2015; O'Rourke et al., 2021). The goal of the CEDS system is to combine existing emissions estimates with driver data to be able to produce consistent estimates of emissions over time at 0.1° × 0.1°. Here, we use CEDS anthropogenic emissions of NH₃ by all the sectors (energy, transportation, ships, residential, industry process, solvents, agriculture, and waste) at 1° × 1° grid cell containing each INDAAF site to estimate the anthropogenic NH₃ emissions. Several studies have described NH₃ emissions data from CEDS (Hoesly et al., 2018; Feng et al., 2020; Beale et al., 2022).

## 2.4 IASI NH₃ total columns and Tropical Rainfall Measuring Mission measurements

IASI-A, launched on board the European Space Agency's Metop-A satellite in 2006, provides measurements of atmospheric NH₃ twice a day (09:30 in the morning and evening, local solar time at the Equator) (Clarisse et al., 2009). Here we use morning observations, which is when the thermal contrast is more favourable for infrared retrievals in the lowest layers of the atmosphere (Clarisse et al., 2010; Van Damme et al., 2014). The NH₃ retrieval product used (artificial neural network for IASI retrieval of NH₃: ANNI-NH₃-v3R) follows a neural network retrieval approach. We refer to Whitburn et al. (2016) and Van Damme et al. (2017, 2021) for a detailed description of the algorithm. Only observations with cloud cover below 10% were used. Given the absence of hourly or even daily observations of NH₃ concentrations in sub-Saharan Africa, the detection limit of the IASI is difficult to determine with certainty. We used the IASI-A Level-2 NH₃ product observations within 100 km around each site for the years 2008 – the first full year of data available – to the end of 2018. It is important to note that monthly IASI NH₃ total columns can be negative. The negative total columns are related to measurement noise, inherent to any type of measurement. In the ANNI product, noise is translated both to positive and negative columns, unlike some other measurement products that translate noise always to positive columns, resulting in positive biases (Whitburn et al., 2016). On average, such noise averages out over long time periods, resulting in a column close to zero over remote regions. The few negative monthly values that are observed here are close to zero, and they occur during months with low ammonia and low measurement sensitivity. Note that these few negative values do not drive the observed trends.

We also used the Tropical Rainfall Measuring Mission (TRMM) daily precipitation product (3B42), which is based on a combination of TRMM observations, geo-synchronous infrared observations, and rain gauge observations (Huffman et al., 2007). Independent rain gauge observations from western Africa have been used to validate this precipitation product, with no indication of bias (Nicholson et al., 2003).
Adon et al. (2010) give a complete overview of the sampling and analytical procedures for the INDAAF passive sampler technique. For each INDAAF site, the passive samplers were made of impregnated filter paper with a species-specific solution for the adsorption of gases. Samplers were exposed for 1 month in duplicates to ensure reproducibility, and monthly concentrations are calculated from the arithmetic mean of the duplicates. Desorbed filters were analysed using ion chromatography (IC). The Laboratoire d'Aérologie participates twice a year in WMO's quality assurance intercomparison programme. Results have always shown that the analytical accuracy of the IC realized at the LAERO is greater than 95%. The intercomparison results of the LAERO are available under the reference 700106 on the WMO website (http://qasac-americas.org/, last access: 24 October 2022). The sampling technique using the INDAAF passive sampler method has been validated on tropical, subtropical, rural, and urban sites in Africa (Adon et al., 2010; Bahino et al., 2018; Ossohou et al., 2020). INDAAF passive samplers have proven to be accurate, cheaper, easy to use, and useful for air quality monitoring.

The precision of the measurements of passive samplers, evaluated through covariance with duplicates, was estimated at 14.3% for NH₃ (Adon et al., 2010). The detection limit for NH₃ was calculated from field blanks and is equal to 0.7 ± 0.2 ppb. Values below the detection limit, as well as non-valid reproducibility values, were removed from the database. Thus, the percentages of valid data in the final database for the studied period 1998/2005–2018 are 97% for Banizoumbou, 93% for Katibougou, 90% for Djougou, 94% for Lamto, 73% for Bomassa, and 93% for Zoétélé.

## 2.3 Biomass burning and anthropogenic emissions of NH₃

The fourth version of the Global Fire Emissions Database (GFED4) provides monthly biomass burning emissions at 0.25° resolution from 1997 onwards from all biomass burning sources, i.e. many sectors (agricultural waste burning, boreal forest fires, peat fires, savanna fires, grassland fires, shrubland fires, temperate forest fires, and tropical deforestation and degradation). Emissions of NH₃ from biomass burning sources were downloaded for the 1° × 1° grid cell containing each INDAAF site. The GFED4 emissions are based on the combination of satellite information on fire activity and vegetation productivity to estimate gridded monthly burned area and fire emissions, as well as scalars that can be used to calculate higher-temporal-resolution emissions (Giglio et al., 2013; van der Werf et al., 2017). The Global Fire Emissions Database – currently by far the most widely used global fire emissions inventory – has been widely cited in the literature, and GFED4 data can be downloaded from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.sedoo.fr/#/metadata/433, last access: 31 May 2023).

The Community Emissions Data System (CEDS) produces consistent estimates of global air emissions species from anthropogenic sources (Smith et al., 2015; O'Rourke et al., 2021). The goal of the CEDS system is to combine existing emissions estimates with driver data to be able to produce consistent estimates of emissions over time at 0.1° × 0.1°. Here, we use CEDS anthropogenic emissions of NH₃ by all the sectors (energy, transportation, ships, residential, industry process, solvents, agriculture, and waste) at 1° × 1° grid cell containing each INDAAF site to estimate the anthropogenic NH₃ emissions. Several studies have described NH₃ emissions data from CEDS (Hoesly et al., 2018; Feng et al., 2020; Beale et al., 2022).

## 2.4 IASI NH₃ total columns and Tropical Rainfall Measuring Mission measurements

IASI-A, launched on board the European Space Agency's Metop-A satellite in 2006, provides measurements of atmospheric NH₃ twice a day (09:30 in the morning and evening, local solar time at the Equator) (Clarisse et al., 2009). Here we use morning observations, which is when the thermal contrast is more favourable for infrared retrievals in the lowest layers of the atmosphere (Clarisse et al., 2010; Van Damme et al., 2014). The NH₃ retrieval product used (artificial neural network for IASI retrieval of NH₃: ANNI-NH₃-v3R) follows a neural network retrieval approach. We refer to Whitburn et al. (2016) and Van Damme et al. (2017, 2021) for a detailed description of the algorithm. Only observations with cloud cover below 10% were used. Given the absence of hourly or even daily observations of NH₃ concentrations in sub-Saharan Africa, the detection limit of the IASI is difficult to determine with certainty. We used the IASI-A Level-2 NH₃ product observations within 100 km around each site for the years 2008 – the first full year of data available – to the end of 2018. It is important to note that monthly IASI NH₃ total columns can be negative. The negative total columns are related to measurement noise, inherent to any type of measurement. In the ANNI product, noise is translated both to positive and negative columns, unlike some other measurement products that translate noise always to positive columns, resulting in positive biases (Whitburn et al., 2016). On average, such noise averages out over long time periods, resulting in a column close to zero over remote regions. The few negative monthly values that are observed here are close to zero, and they occur during months with low ammonia and low measurement sensitivity. Note that these few negative values do not drive the observed trends.

We also used the Tropical Rainfall Measuring Mission (TRMM) daily precipitation product (3B42), which is based on a combination of TRMM observations, geo-synchronous infrared observations, and rain gauge observations (Huffman et al., 2007). Independent rain gauge observations from western Africa have been used to validate this precipitation product, with no indication of bias (Nicholson et al., 2003).
For analyses of seasonal and interannual variability in each product, we used the mean monthly value of NH₃ or the monthly sum (for precipitation) for a 1° × 1° grid cell containing each INDAAF site. Note that monthly means are excluded from the IASI NH₃ analysis for months when there are fewer than 20 valid observations.

## 2.5 Trend analysis

Trend analyses were carried out by using Mann–Kendall (MK) (Kendall, 1975; Mann, 1945) and seasonal Kendall (SK) (Hirsch et al., 1982) tests, which are statistical non-parametric tests used to determine the increasing or decreasing trends in a random variable over some period of time. The MK and SK tests were suitable for cases with monotonous trends. The MK test allows working with no seasonal or other cycles in the data such as average annual data. The SK test follows the same principle as the MK test and is significantly robust to seasonality and was therefore applied for monthly time series. The SK test takes into account a 12-month seasonality in the time series data by computing the MK test on each dataset of “months” over the period 1998/2005–2018 separately and then combining the results (Tang et al., 2018a). MK and SK tests allow working with non-normal data, in situations with many missing values, and are resistant to outliers (Kumar et al., 2018).

We coupled MK and SK tests, respectively, to Sen's slope (SS) (Sen, 1968) and seasonal Kendall slope (SKS) to estimate the magnitude of the trend. These statistical tests have been widely applied and described in the literature to estimate trends in environmental parameters (Shadmani et al., 2012; Yue et al., 2002; Yue and Wang, 2004), but the application over African rural sites is limited (Ossohou et al., 2019, 2020). Two-tailed tests are conducted with the statistical software R version 4.0.4 (R Core Team, 2021) and Addinsoft (2022) for this study.

# 3 Results and discussion

## 3.1 Variations in ground-based NH₃ and IASI NH₃

In the first part of this subsection, we will present the NH₃ concentration variations in the dry savanna ecosystem. In the second part, we will present the same variations for wet savanna and forest sites. Each part will show the monthly and annual evolutions and descriptive statistics of NH₃ ground-based and satellite measurements at each site. At the end of each of these two parts, we will discuss the results obtained according to the sources and the major processes that influence the atmospheric NH₃ levels.

### 3.1.1 Dry savanna

Figure 2 presents monthly variations in ground-based NH₃ concentrations and IASI NH₃ at the INDAAF dry savanna

sites of Banizoumbou (Fig. 2a) and Katibougou (Fig. 2b) over 1998–2018 and 2008–2018, respectively. The monthly 21-year surface concentrations of NH₃ are in the same range at Banizoumbou and Katibougou (Table 2) with coefficients of variation of ~50%. Nevertheless, the monthly coefficient of variation in IASI NH₃ total columns appears to be larger at Banizoumbou (52%) compared to Katibougou (43%) over the 11-year period. From 2008 to 2018, we obtain a significant Pearson's correlation between monthly ground-based NH₃ concentrations and IASI NH₃ total columns at Banizoumbou ($r = 0.30, p < 0.01$) but not at Katibougou ($r = 0.06, p = 0.51$).

Table 2 shows that mean ground-based concentrations of NH₃ for each dry savanna site are significantly higher in the wet season compared to the dry season according to the *t* test ($p < 0.01$). Furthermore, we observe a decrease of 8.1% (in Banizoumbou) and 23.0% (in Katibougou) in average annual ground-based concentrations of NH₃ from the period 1998–2007 to 2008–2018 (Table 3). In contrast, Table 3 shows that average wet season ground-based NH₃ concentrations increase between 1998–2007 and 2008–2018 in Banizoumbou (+5.9%) but decrease in Katibougou (–22.0%). Mean dry season NH₃ ground-based concentrations decrease in Banizoumbou (–18.9) and Katibougou (–22.0) from 1998–2007 to 2008–2018 (Table 3).

Figure 3 compiles the mean monthly 21-year average ground-based concentrations (gray line) and 11-year average total column densities (dark line) in dry savanna ecosystems to obtain the mean annual cycle evolutions of NH₃ at the stations of Banizoumbou (Fig. 3a) and Katibougou (Fig. 3b). In both sites of the sub-Saharan dry ecosystems, we observe a marked seasonal cycle with two peaks in ground-based concentrations and total columns of NH₃ appearing at the beginning (May–June) and the end (October) of the wet season (Fig. 3). The lowest values of NH₃ (concentrations and densities) are generally observed during December–January, but the highest values are obtained in May–June. Indeed, mean monthly measurements vary at Banizoumbou from 2.8 ± 1.1 ppb (January) to 8.3 ± 2.6 ppb (June) for ground-based concentrations and from 4.0 ± 1.3 × 10¹⁵ molec. cm⁻² (January) to 19.5 ± 3.7 × 10¹⁵ molec. cm⁻² (May) for IASI NH₃ total columns (Fig. 3a). At Katibougou, mean monthly ground-based concentrations ranged from 3.3 ± 1.3 ppb (January) to 7.6 ± 2.1 ppb (June) and from 4.8 ± 2.1 × 10¹⁵ molec. cm⁻² (December) to 16.2 ± 2.2 × 10¹⁵ molec. cm⁻² (May) for IASI NH₃ total columns (Fig. 3b). The mean annual variation coefficients are 34% and 27% for ground-based concentrations and 40% and 34% for IASI NH₃ total column measurements at Banizoumbou and Katibougou, respectively.

In the Sahelian region, major sources of atmospheric NH₃ include bacterial decomposition of urea in livestock excreta and emissions from natural or fertilized soils (Bouwman and Van Der Hoek, 1997). In addition, it has been shown in the literature that African dry savanna ecosystems, characterized
For analyses of seasonal and interannual variability in each product, we used the mean monthly value of NH₃ or the monthly sum (for precipitation) for a 1° × 1° grid cell containing each INDAAF site. Note that monthly means are excluded from the IASI NH₃ analysis for months when there are fewer than 20 valid observations.

## 2.5 Trend analysis

Trend analyses were carried out by using Mann–Kendall (MK) (Kendall, 1975; Mann, 1945) and seasonal Kendall (SK) (Hirsch et al., 1982) tests, which are statistical non-parametric tests used to determine the increasing or decreasing trends in a random variable over some period of time. The MK and SK tests were suitable for cases with monotonous trends. The MK test allows working with no seasonal or other cycles in the data such as average annual data. The SK test follows the same principle as the MK test and is significantly robust to seasonality and was therefore applied for monthly time series. The SK test takes into account a 12-month seasonality in the time series data by computing the MK test on each dataset of “months” over the period 1998/2005–2018 separately and then combining the results (Tang et al., 2018a). MK and SK tests allow working with non-normal data, in situations with many missing values, and are resistant to outliers (Kumar et al., 2018).

We coupled MK and SK tests, respectively, to Sen's slope (SS) (Sen, 1968) and seasonal Kendall slope (SKS) to estimate the magnitude of the trend. These statistical tests have been widely applied and described in the literature to estimate trends in environmental parameters (Shadmani et al., 2012; Yue et al., 2002; Yue and Wang, 2004), but the application over African rural sites is limited (Ossohou et al., 2019, 2020). Two-tailed tests are conducted with the statistical software R version 4.0.4 (R Core Team, 2021) and Addinsoft (2022) for this study.

# 3 Results and discussion

## 3.1 Variations in ground-based NH₃ and IASI NH₃

In the first part of this subsection, we will present the NH₃ concentration variations in the dry savanna ecosystem. In the second part, we will present the same variations for wet savanna and forest sites. Each part will show the monthly and annual evolutions and descriptive statistics of NH₃ ground-based and satellite measurements at each site. At the end of each of these two parts, we will discuss the results obtained according to the sources and the major processes that influence the atmospheric NH₃ levels.

### 3.1.1 Dry savanna

Figure 2 presents monthly variations in ground-based NH₃ concentrations and IASI NH₃ at the INDAAF dry savanna

sites of Banizoumbou (Fig. 2a) and Katibougou (Fig. 2b) over 1998–2018 and 2008–2018, respectively. The monthly 21-year surface concentrations of NH₃ are in the same range at Banizoumbou and Katibougou (Table 2) with coefficients of variation of ~50%. Nevertheless, the monthly coefficient of variation in IASI NH₃ total columns appears to be larger at Banizoumbou (52%) compared to Katibougou (43%) over the 11-year period. From 2008 to 2018, we obtain a significant Pearson's correlation between monthly ground-based NH₃ concentrations and IASI NH₃ total columns at Banizoumbou ($r = 0.30, p < 0.01$) but not at Katibougou ($r = 0.06, p = 0.51$).

Table 2 shows that mean ground-based concentrations of NH₃ for each dry savanna site are significantly higher in the wet season compared to the dry season according to the *t* test ($p < 0.01$). Furthermore, we observe a decrease of 8.1% (in Banizoumbou) and 23.0% (in Katibougou) in average annual ground-based concentrations of NH₃ from the period 1998–2007 to 2008–2018 (Table 3). In contrast, Table 3 shows that average wet season ground-based NH₃ concentrations increase between 1998–2007 and 2008–2018 in Banizoumbou (+5.9%) but decrease in Katibougou (–22.0%). Mean dry season NH₃ ground-based concentrations decrease in Banizoumbou (–18.9) and Katibougou (–22.0) from 1998–2007 to 2008–2018 (Table 3).

Figure 3 compiles the mean monthly 21-year average ground-based concentrations (gray line) and 11-year average total column densities (dark line) in dry savanna ecosystems to obtain the mean annual cycle evolutions of NH₃ at the stations of Banizoumbou (Fig. 3a) and Katibougou (Fig. 3b). In both sites of the sub-Saharan dry ecosystems, we observe a marked seasonal cycle with two peaks in ground-based concentrations and total columns of NH₃ appearing at the beginning (May–June) and the end (October) of the wet season (Fig. 3). The lowest values of NH₃ (concentrations and densities) are generally observed during December–January, but the highest values are obtained in May–June. Indeed, mean monthly measurements vary at Banizoumbou from 2.8 ± 1.1 ppb (January) to 8.3 ± 2.6 ppb (June) for ground-based concentrations and from 4.0 ± 1.3 × 10¹⁵ molec. cm⁻² (January) to 19.5 ± 3.7 × 10¹⁵ molec. cm⁻² (May) for IASI NH₃ total columns (Fig. 3a). At Katibougou, mean monthly ground-based concentrations ranged from 3.3 ± 1.3 ppb (January) to 7.6 ± 2.1 ppb (June) and from 4.8 ± 2.1 × 10¹⁵ molec. cm⁻² (December) to 16.2 ± 2.2 × 10¹⁵ molec. cm⁻² (May) for IASI NH₃ total columns (Fig. 3b). The mean annual variation coefficients are 34% and 27% for ground-based concentrations and 40% and 34% for IASI NH₃ total column measurements at Banizoumbou and Katibougou, respectively.

In the Sahelian region, major sources of atmospheric NH₃ include bacterial decomposition of urea in livestock excreta and emissions from natural or fertilized soils (Bouwman and Van Der Hoek, 1997). In addition, it has been shown in the literature that African dry savanna ecosystems, characterized
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Monthly time series of ground-based NH₃ concentrations over the period 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Banizoumbou, Niger, and (b) Katibougou, Mali. The dashed lines represent the linear regression lines.</figcaption></figure>

<table><caption>**Table 2.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) at Banizoumbou, Niger, and Katibougou, Mali.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub><br>(ppb)</th><th colspan="2">IASI NH<sub>3</sub><br>(10<sup>15</sup> molec. cm<sup>-2</sup>)</th></tr><tr><th>Banizoumbou</th><th>Katibougou</th><th>Banizoumbou</th><th>Katibougou</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>0.3</td><td>1.4</td></tr><tr><td>Max</td><td>13.3</td><td>13.1</td><td>30.8</td><td>22.1</td></tr><tr><td>Annual</td><td>Avg</td><td>5.8 ± 1.2</td><td>5.7 ± 1.1</td><td>10.7 ± 1.1</td><td>10.0 ± 0.9</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>2.4</td><td>8.0</td><td>6.8</td></tr><tr><td>Max</td><td>10.4</td><td>9.3</td><td>13.5</td><td>12.4</td></tr><tr><td>Avg</td><td>6.9 ± 1.6</td><td>6.7 ± 1.5</td><td>11.3 ± 1.3</td><td>9.1 ± 1.6</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.5</td><td>1.8</td><td>7.4</td><td>9.0</td></tr><tr><td>Max</td><td>8.1</td><td>8.0</td><td>12.9</td><td>12.2</td></tr><tr><td>Avg</td><td>5.2 ± 1.1</td><td>5.2 ± 1.0</td><td>10.3 ± 1.5</td><td>10.3 ± 0.8</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Monthly time series of ground-based NH₃ concentrations over the period 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Banizoumbou, Niger, and (b) Katibougou, Mali. The dashed lines represent the linear regression lines.</figcaption></figure>

<table><caption>**Table 2.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) at Banizoumbou, Niger, and Katibougou, Mali.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub><br>(ppb)</th><th colspan="2">IASI NH<sub>3</sub><br>(10<sup>15</sup> molec. cm<sup>-2</sup>)</th></tr><tr><th>Banizoumbou</th><th>Katibougou</th><th>Banizoumbou</th><th>Katibougou</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>0.3</td><td>1.4</td></tr><tr><td>Max</td><td>13.3</td><td>13.1</td><td>30.8</td><td>22.1</td></tr><tr><td>Annual</td><td>Avg</td><td>5.8 ± 1.2</td><td>5.7 ± 1.1</td><td>10.7 ± 1.1</td><td>10.0 ± 0.9</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>2.4</td><td>8.0</td><td>6.8</td></tr><tr><td>Max</td><td>10.4</td><td>9.3</td><td>13.5</td><td>12.4</td></tr><tr><td>Avg</td><td>6.9 ± 1.6</td><td>6.7 ± 1.5</td><td>11.3 ± 1.3</td><td>9.1 ± 1.6</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.5</td><td>1.8</td><td>7.4</td><td>9.0</td></tr><tr><td>Max</td><td>8.1</td><td>8.0</td><td>12.9</td><td>12.2</td></tr><tr><td>Avg</td><td>5.2 ± 1.1</td><td>5.2 ± 1.0</td><td>10.3 ± 1.5</td><td>10.3 ± 0.8</td></tr></tbody></table>
<table><caption>**Table 3.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 1998–2007 and 2008–2018) at Banizoumbou, Niger, and Katibougou, Mali.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">1998–2007</th><th colspan="2">2008–2018</th></tr><tr><th>Banizoumbou</th><th>Katibougou</th><th>Banizoumbou</th><th>Katibougou</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.8</td><td>1.8</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>13.3</td><td>13.1</td><td>13.3</td><td>13.1</td></tr><tr><td>Annual</td><td>Avg</td><td>6.1 ± 1.3</td><td>6.5 ± 0.8</td><td>5.6 ± 1.1</td><td>5.0 ± 1.1</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>5.0</td><td>3.6</td><td>2.4</td></tr><tr><td>Max</td><td>9.8</td><td>9.3</td><td>10.4</td><td>9.0</td></tr><tr><td>Avg</td><td>6.7 ± 1.5</td><td>7.7 ± 1.3</td><td>7.1 ± 1.8</td><td>5.9 ± 1.5</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.5</td><td>4.8</td><td>3.2</td><td>1.8</td></tr><tr><td>Max</td><td>8.1</td><td>8.0</td><td>6.0</td><td>6.4</td></tr><tr><td>Avg</td><td>5.8 ± 1.2</td><td>5.9 ± 0.7</td><td>4.7 ± 0.8</td><td>4.6 ± 1.0</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Mean monthly ground-based NH₃ concentrations (1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures (1998–2018) measured by ground-based instruments (1998–2018) and TRMM (2005–2018) at (a) Banizoumbou, Niger, and (b) Katibougou, Mali. Error bars represent the mean absolute deviation.</figcaption></figure>

by sandy soils, tiger bush, fallow bush, and deciduous shrubs (Ossohou et al., 2019), tend to have alkaline soils, creating favourable conditions for NH₃ volatilization (Clarisse et al., 2019; Delon et al., 2017; Hickman et al., 2018; Vågen et al., 2016). Among other factors, air temperature and rainfall pattern and amount influence NH₃ emissions considerably in drylands like Banizoumbou and Katibougou. We find positive correlation coefficients statistically significant at 99% between ground-based NH₃ concentrations and air temper-

ature in the dry savanna ecosystem of Banizoumbou (0.32) and Katibougou (0.26), showing that NH₃ volatilization increases with temperature (Van Damme et al., 2021). Note that the correlation coefficient between monthly IASI NH₃ and emission fluxes of NH₃ from the agricultural sector from the CEDS emission inventory at Katibougou is equal to 0.22 ($p < 0.05$). In the dry savannas, soils are often characterized by large pulses of NH₃ related to successive dryings and wettings of dry soils (McCalley and Sparks, 2008; Soper et
<table><caption>**Table 3.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 1998–2007 and 2008–2018) at Banizoumbou, Niger, and Katibougou, Mali.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">1998–2007</th><th colspan="2">2008–2018</th></tr><tr><th>Banizoumbou</th><th>Katibougou</th><th>Banizoumbou</th><th>Katibougou</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.8</td><td>1.8</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>13.3</td><td>13.1</td><td>13.3</td><td>13.1</td></tr><tr><td>Annual</td><td>Avg</td><td>6.1 ± 1.3</td><td>6.5 ± 0.8</td><td>5.6 ± 1.1</td><td>5.0 ± 1.1</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>5.0</td><td>3.6</td><td>2.4</td></tr><tr><td>Max</td><td>9.8</td><td>9.3</td><td>10.4</td><td>9.0</td></tr><tr><td>Avg</td><td>6.7 ± 1.5</td><td>7.7 ± 1.3</td><td>7.1 ± 1.8</td><td>5.9 ± 1.5</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.5</td><td>4.8</td><td>3.2</td><td>1.8</td></tr><tr><td>Max</td><td>8.1</td><td>8.0</td><td>6.0</td><td>6.4</td></tr><tr><td>Avg</td><td>5.8 ± 1.2</td><td>5.9 ± 0.7</td><td>4.7 ± 0.8</td><td>4.6 ± 1.0</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Mean monthly ground-based NH₃ concentrations (1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures (1998–2018) measured by ground-based instruments (1998–2018) and TRMM (2005–2018) at (a) Banizoumbou, Niger, and (b) Katibougou, Mali. Error bars represent the mean absolute deviation.</figcaption></figure>

by sandy soils, tiger bush, fallow bush, and deciduous shrubs (Ossohou et al., 2019), tend to have alkaline soils, creating favourable conditions for NH₃ volatilization (Clarisse et al., 2019; Delon et al., 2017; Hickman et al., 2018; Vågen et al., 2016). Among other factors, air temperature and rainfall pattern and amount influence NH₃ emissions considerably in drylands like Banizoumbou and Katibougou. We find positive correlation coefficients statistically significant at 99% between ground-based NH₃ concentrations and air temper-

ature in the dry savanna ecosystem of Banizoumbou (0.32) and Katibougou (0.26), showing that NH₃ volatilization increases with temperature (Van Damme et al., 2021). Note that the correlation coefficient between monthly IASI NH₃ and emission fluxes of NH₃ from the agricultural sector from the CEDS emission inventory at Katibougou is equal to 0.22 (*p* < 0.05). In the dry savannas, soils are often characterized by large pulses of NH₃ related to successive dryings and wettings of dry soils (McCalley and Sparks, 2008; Soper et
al., 2016). As we can see in Fig. 3, the first peak observed in May–June (beginning of the wet season) could be related to the optimal soil moisture to initiate bacterial activity and a flush of newly mineralized N. Our results support the conclusions of an earlier study that used satellite retrievals and in situ measurements for the year 2008 over Africa to argue that the onset of the rainy season causes pulsed emissions of NH₃ over the Sahel (Hickman et al., 2018). Our study based on ground-based and satellite measurements ranging from 1 to 2 decades clearly shows a correspondence between early rainy season precipitation and NH₃ concentrations over the two dry savanna sites. Moreover, the results based on our analysis of a long-term database clearly indicate that this process is reproducible every year.

The temporal evolution of NH₃ can be associated with the two most important phenomena: (1) possible Birch effect emissions in the early and possibly late rainy season and (2) the overall seasonal cycle of NH₃ and the reasons for this broad seasonality (separate from the Birch effect). Indeed, during the wet season (June–September), the months are wetter and cooler and give the soils more moisture than the dry season months. As a result, wet season soils are less susceptible to intense drying events than during the dry season. This consequently results in more limited NH₃ volatilization from soil drying, leading to low NH₃ levels in the wet season. However, at the end of the wet season, rainfall became erratic and led to drying soils for a few days. This erratic rainfall combined with an increase in air temperature may explain the second observed NH₃ peak, occurring at the end of the wet season. A similar late-season pulse of nitric oxide from the re-wetted soils was observed at the regional scales in the Sahel (Jaeglé et al., 2004), suggesting that there may be some similar potential for NH₃ pulsing from re-wetted dry soils. This late-season peak appears to be of less importance than the early wet season peak, presumably because over the growing season, growing vegetation and microbial communities immobilized and reduced soil nitrogen pools and may continue to do so. One of the arguments for why Birch effect emissions happen at the beginning of the growing season is that there has been an accumulation of labile N in soils in the dry season. During the wet season, NH₃ is found directly in the rainwater in the form of NH₄⁺, thus promoting wet deposition on the growing vegetation. NH₃ also reacts with some acid gases such as H₂SO₄, HNO₃ and HCl to form aerosols of atmospheric ammonium salts, such as ammonium sulfate ([NH₄]₂SO₄), ammonium bisulfate (NH₄HSO₄), ammonium nitrate (NH₄NO₃), and ammonium chloride (NH₄Cl). The conversion of gases to particles in the atmosphere can occur through condensation and/or direct nucleation processes (Baek et al., 2004). Condensation adds mass to pre-existing aerosols, while direct nucleation allows the formation of atmospheric aerosols from gaseous precursors. These reactions could therefore lead to a decrease in atmospheric NH₃ concentrations in the Sahelian region (Koziel et al., 2006). As shown by positive correlations between IASI NH₃ and emis-

sions of NH₃ by agricultural activities, we suggest that in the dry savanna area of Katibougou, NH₃ concentration levels could also be influenced by agriculture activities. Since the months of September through March correspond to the fire period in the wet savanna and forest sites, we suggest that even though the two dry savanna sites experience few fires, NH₃ columns from the IASI are certainly affected by NH₃ present in the transported fire plumes. It is also important to highlight the pastoralism in the Sahel region, mainly nomadic in nature. Indeed, Sahelian agro-pastoralism appears to be very important, representing 25 % to 30 % of the gross production product (GDP), and contributes 10 % to 15 % of the GDP of Mali and Niger for example (Adon et al., 2010).

### 3.1.2 Wet savanna and forest

In the wet savanna ecosystem, we present the monthly evolutions of ground-based NH₃ concentrations (2005–2018: Djougou; 1998–2018: Lamto) and IASI NH₃ total column densities (2008–2018 for both sites) at Djougou (Fig. 4a) and Lamto (Fig. 4b). Monthly ground-based NH₃ concentrations range from 0.7 to 12.1 ppb at Djougou and from 0.7 to 14.2 ppb at Lamto. IASI NH₃ total column densities vary from $-2.8 \times 10^{15}$ to $36.6 \times 10^{15}$ molec. cm$^{-2}$ at Djougou and from $-1.3 \times 10^{15}$ to $58.0 \times 10^{15}$ molec. cm$^{-2}$ at Lamto. The results show that the maxima of IASI NH₃ total column densities are highest at Lamto (Fig. 4). The coefficients of variation are globally high, equal to 57 % and 62 % for ground-based measurements and 54 % and 69 % for IASI NH₃ total columns at Djougou and Lamto, respectively. For the entire period of measurements, the Pearson correlation test applied to monthly ground-based NH₃ concentrations and IASI NH₃ total columns reveals no significant correlation at Djougou ($r = 0.03, p = 0.72$) but a strong linear correlation at Lamto ($r = 0.55, p < 0.01$).

Table 4 presents a synthesis of monthly, seasonal, and annual minimum, maximum, and average ground-based NH₃ concentrations and IASI NH₃ total columns at Djougou and Lamto stations. The results show that mean annual, wet season, and dry season ground-based NH₃ concentrations in Djougou are significantly higher than those in Lamto ($t$ test, $p < 0.05$). In contrast, mean annual and dry season IASI NH₃ total columns are significantly higher ($t$ test, $p < 0.01$) at Lamto compared to Djougou (Table 4). The data recorded in Table 5 show that for the period 2006–2007, the average ground-based NH₃ concentrations at Djougou and Lamto are of the same order of magnitude. However, the average concentration obtained in the dry season of the period 2006–2007 in Lamto is 20 % higher than in Djougou. In contrast to the period 2006–2007, annual, dry season, and wet season averages of ground-based concentrations of NH₃ over the period 2008–2018 are the highest at Djougou, with differences ranging from 1.6 to 1.8 ppb compared to the Lamto averages (Table 5).
al., 2016). As we can see in Fig. 3, the first peak observed in May–June (beginning of the wet season) could be related to the optimal soil moisture to initiate bacterial activity and a flush of newly mineralized N. Our results support the conclusions of an earlier study that used satellite retrievals and in situ measurements for the year 2008 over Africa to argue that the onset of the rainy season causes pulsed emissions of NH₃ over the Sahel (Hickman et al., 2018). Our study based on ground-based and satellite measurements ranging from 1 to 2 decades clearly shows a correspondence between early rainy season precipitation and NH₃ concentrations over the two dry savanna sites. Moreover, the results based on our analysis of a long-term database clearly indicate that this process is reproducible every year.

The temporal evolution of NH₃ can be associated with the two most important phenomena: (1) possible Birch effect emissions in the early and possibly late rainy season and (2) the overall seasonal cycle of NH₃ and the reasons for this broad seasonality (separate from the Birch effect). Indeed, during the wet season (June–September), the months are wetter and cooler and give the soils more moisture than the dry season months. As a result, wet season soils are less susceptible to intense drying events than during the dry season. This consequently results in more limited NH₃ volatilization from soil drying, leading to low NH₃ levels in the wet season. However, at the end of the wet season, rainfall became erratic and led to drying soils for a few days. This erratic rainfall combined with an increase in air temperature may explain the second observed NH₃ peak, occurring at the end of the wet season. A similar late-season pulse of nitric oxide from the re-wetted soils was observed at the regional scales in the Sahel (Jaeglé et al., 2004), suggesting that there may be some similar potential for NH₃ pulsing from re-wetted dry soils. This late-season peak appears to be of less importance than the early wet season peak, presumably because over the growing season, growing vegetation and microbial communities immobilized and reduced soil nitrogen pools and may continue to do so. One of the arguments for why Birch effect emissions happen at the beginning of the growing season is that there has been an accumulation of labile N in soils in the dry season. During the wet season, NH₃ is found directly in the rainwater in the form of NH₄⁺, thus promoting wet deposition on the growing vegetation. NH₃ also reacts with some acid gases such as H₂SO₄, HNO₃ and HCl to form aerosols of atmospheric ammonium salts, such as ammonium sulfate ([NH₄]₂SO₄), ammonium bisulfate (NH₄HSO₄), ammonium nitrate (NH₄NO₃), and ammonium chloride (NH₄Cl). The conversion of gases to particles in the atmosphere can occur through condensation and/or direct nucleation processes (Baek et al., 2004). Condensation adds mass to pre-existing aerosols, while direct nucleation allows the formation of atmospheric aerosols from gaseous precursors. These reactions could therefore lead to a decrease in atmospheric NH₃ concentrations in the Sahelian region (Koziel et al., 2006). As shown by positive correlations between IASI NH₃ and emis-

sions of NH₃ by agricultural activities, we suggest that in the dry savanna area of Katibougou, NH₃ concentration levels could also be influenced by agriculture activities. Since the months of September through March correspond to the fire period in the wet savanna and forest sites, we suggest that even though the two dry savanna sites experience few fires, NH₃ columns from the IASI are certainly affected by NH₃ present in the transported fire plumes. It is also important to highlight the pastoralism in the Sahel region, mainly nomadic in nature. Indeed, Sahelian agro-pastoralism appears to be very important, representing 25 % to 30 % of the gross production product (GDP), and contributes 10 % to 15 % of the GDP of Mali and Niger for example (Adon et al., 2010).

### 3.1.2 Wet savanna and forest

In the wet savanna ecosystem, we present the monthly evolutions of ground-based NH₃ concentrations (2005–2018: Djougou; 1998–2018: Lamto) and IASI NH₃ total column densities (2008–2018 for both sites) at Djougou (Fig. 4a) and Lamto (Fig. 4b). Monthly ground-based NH₃ concentrations range from 0.7 to 12.1 ppb at Djougou and from 0.7 to 14.2 ppb at Lamto. IASI NH₃ total column densities vary from $-2.8 \times 10^{15}$ to $36.6 \times 10^{15}$ molec. cm$^{-2}$ at Djougou and from $-1.3 \times 10^{15}$ to $58.0 \times 10^{15}$ molec. cm$^{-2}$ at Lamto. The results show that the maxima of IASI NH₃ total column densities are highest at Lamto (Fig. 4). The coefficients of variation are globally high, equal to 57 % and 62 % for ground-based measurements and 54 % and 69 % for IASI NH₃ total columns at Djougou and Lamto, respectively. For the entire period of measurements, the Pearson correlation test applied to monthly ground-based NH₃ concentrations and IASI NH₃ total columns reveals no significant correlation at Djougou ($r = 0.03, p = 0.72$) but a strong linear correlation at Lamto ($r = 0.55, p < 0.01$).

Table 4 presents a synthesis of monthly, seasonal, and annual minimum, maximum, and average ground-based NH₃ concentrations and IASI NH₃ total columns at Djougou and Lamto stations. The results show that mean annual, wet season, and dry season ground-based NH₃ concentrations in Djougou are significantly higher than those in Lamto ($t$ test, $p < 0.05$). In contrast, mean annual and dry season IASI NH₃ total columns are significantly higher ($t$ test, $p < 0.01$) at Lamto compared to Djougou (Table 4). The data recorded in Table 5 show that for the period 2006–2007, the average ground-based NH₃ concentrations at Djougou and Lamto are of the same order of magnitude. However, the average concentration obtained in the dry season of the period 2006–2007 in Lamto is 20 % higher than in Djougou. In contrast to the period 2006–2007, annual, dry season, and wet season averages of ground-based concentrations of NH₃ over the period 2008–2018 are the highest at Djougou, with differences ranging from 1.6 to 1.8 ppb compared to the Lamto averages (Table 5).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Monthly time series of ground-based NH₃ concentrations over the periods 2005–2018 and 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Djougou, Benin, and (b) Lamto, Côte d’Ivoire. The dashed lines represent the linear regression lines.</figcaption></figure>

<table><caption>**Table 4.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (Djougou: 2005–2018; Lamto: 1998–2018) and IASI NH₃ total column densities (2008–2018) at Djougou, Benin, and Lamto, Côte d’Ivoire.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub> (ppb)</th><th colspan="2">IASI NH<sub>3</sub> (10<sup>15</sup> molec.cm<sup>-2</sup>)</th></tr><tr><th>Djougou</th><th>Lamto</th><th>Djougou</th><th>Lamto</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>-2.8</td><td>-1.3</td></tr><tr><td>Max</td><td>12.1</td><td>14.2</td><td>36.6</td><td>58.0</td></tr><tr><td>Annual</td><td>Avg</td><td>4.7 ± 1.3</td><td>3.5 ± 0.8</td><td>16.0 ± 1.3</td><td>20.9 ± 1.6</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>1.5</td><td>1.5</td><td>10.6</td><td>8.4</td></tr><tr><td>Max</td><td>7.6</td><td>4.5</td><td>15.0</td><td>15.9</td></tr><tr><td>Avg</td><td>4.1 ± 1.5</td><td>2.8 ± 0.9</td><td>13.4 ± 0.9</td><td>12.0 ± 1.7</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>3.5</td><td>2.7</td><td>14.9</td><td>27.3</td></tr><tr><td>Max</td><td>8.8</td><td>7.8</td><td>23.1</td><td>37.1</td></tr><tr><td>Avg</td><td>5.5 ± 1.3</td><td>4.6 ± 1.1</td><td>19.6 ± 2.1</td><td>30.7 ± 2.7</td></tr></tbody></table>

<table><caption>**Table 5.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 2006–2007 and 2008–2018) at Djougou, Benin, and Lamto, Côte d’Ivoire.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">2006–2007*</th><th colspan="2">2008–2018</th></tr><tr><th>Djougou</th><th>Lamto</th><th>Djougou</th><th>Lamto</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>1.4</td><td>0.7</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>9.8</td><td>14.2</td><td>12.1</td><td>9.5</td></tr><tr><td>Annual</td><td>Avg</td><td>4.4 ± 1.1</td><td>4.7 ± 0.3</td><td>4.9 ± 1.3</td><td>3.2 ± 0.6</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>3.5</td><td>1.5</td><td>1.5</td></tr><tr><td>Max</td><td>4.6</td><td>3.9</td><td>7.6</td><td>4.5</td></tr><tr><td>Avg</td><td>3.6 ± 0.9</td><td>3.2 ± 0.7</td><td>4.4 ± 1.5</td><td>2.6 ± 0.7</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>3.9</td><td>6.4</td><td>3.5</td><td>2.7</td></tr><tr><td>Max</td><td>6.8</td><td>6.7</td><td>8.8</td><td>5.6</td></tr><tr><td>Avg</td><td>5.4 ± 1.5</td><td>6.5 ± 0.2</td><td>5.6 ± 1.4</td><td>4.0 ± 0.7</td></tr></tbody></table>

<footnote>* Full year of data in Djougou begins in 2006.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Monthly time series of ground-based NH₃ concentrations over the periods 2005–2018 and 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Djougou, Benin, and (b) Lamto, Côte d’Ivoire. The dashed lines represent the linear regression lines.</figcaption></figure>

<table><caption>**Table 4.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (Djougou: 2005–2018; Lamto: 1998–2018) and IASI NH₃ total column densities (2008–2018) at Djougou, Benin, and Lamto, Côte d’Ivoire.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub> (ppb)</th><th colspan="2">IASI NH<sub>3</sub> (10<sup>15</sup> molec.cm<sup>-2</sup>)</th></tr><tr><th>Djougou</th><th>Lamto</th><th>Djougou</th><th>Lamto</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>-2.8</td><td>-1.3</td></tr><tr><td>Max</td><td>12.1</td><td>14.2</td><td>36.6</td><td>58.0</td></tr><tr><td>Annual</td><td>Avg</td><td>4.7 ± 1.3</td><td>3.5 ± 0.8</td><td>16.0 ± 1.3</td><td>20.9 ± 1.6</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>1.5</td><td>1.5</td><td>10.6</td><td>8.4</td></tr><tr><td>Max</td><td>7.6</td><td>4.5</td><td>15.0</td><td>15.9</td></tr><tr><td>Avg</td><td>4.1 ± 1.5</td><td>2.8 ± 0.9</td><td>13.4 ± 0.9</td><td>12.0 ± 1.7</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>3.5</td><td>2.7</td><td>14.9</td><td>27.3</td></tr><tr><td>Max</td><td>8.8</td><td>7.8</td><td>23.1</td><td>37.1</td></tr><tr><td>Avg</td><td>5.5 ± 1.3</td><td>4.6 ± 1.1</td><td>19.6 ± 2.1</td><td>30.7 ± 2.7</td></tr></tbody></table>

<table><caption>**Table 5.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 2006–2007 and 2008–2018) at Djougou, Benin, and Lamto, Côte d’Ivoire.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">2006–2007*</th><th colspan="2">2008–2018</th></tr><tr><th>Djougou</th><th>Lamto</th><th>Djougou</th><th>Lamto</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>1.4</td><td>0.7</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>9.8</td><td>14.2</td><td>12.1</td><td>9.5</td></tr><tr><td>Annual</td><td>Avg</td><td>4.4 ± 1.1</td><td>4.7 ± 0.3</td><td>4.9 ± 1.3</td><td>3.2 ± 0.6</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.7</td><td>3.5</td><td>1.5</td><td>1.5</td></tr><tr><td>Max</td><td>4.6</td><td>3.9</td><td>7.6</td><td>4.5</td></tr><tr><td>Avg</td><td>3.6 ± 0.9</td><td>3.2 ± 0.7</td><td>4.4 ± 1.5</td><td>2.6 ± 0.7</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>3.9</td><td>6.4</td><td>3.5</td><td>2.7</td></tr><tr><td>Max</td><td>6.8</td><td>6.7</td><td>8.8</td><td>5.6</td></tr><tr><td>Avg</td><td>5.4 ± 1.5</td><td>6.5 ± 0.2</td><td>5.6 ± 1.4</td><td>4.0 ± 0.7</td></tr></tbody></table>

<footnote>* Full year of data in Djougou begins in 2006.</footnote>
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Mean monthly ground-based NH₃ concentrations (Djougou: 2005–2018; Lamto: 1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures measured by ground-based instruments (1998–2018) and TRMM (2005–2018) (Djougou: 2005–2018; Lamto: 1998–2018) at (a) Djougou, Benin, and (b) Lamto, Côte d'Ivoire. Error bars represent the mean absolute deviation.</figcaption></figure>

Figure 5 presents the annual mean cycle of monthly ground-based concentrations and IASI NH₃ total column densities at Djougou (Fig. 5a) and Lamto (Fig. 5b) located in the wet savanna ecosystem. The results show that the annual mean ground-based and IASI NH₃ profiles have a poor covariation at Djougou (Fig. 5a), while IASI NH₃ shows a good agreement with ground-based NH₃ at the Lamto site (Fig. 5b). Ground-based NH₃ concentrations and IASI NH₃ total columns exhibit a seasonality at Lamto and Djougou stations with higher values occurring in the dry season (January to March) and lower values in the wet season (May through November). Mean annual cycles of IASI NH₃ total column density seasonality are less marked at Djougou compared to Lamto station. Monthly mean concentrations and total column densities of NH₃ range from 3.2 ± 1.4 (June) to 6.7 ± 1.8 ppb (February) and from 4.7 ± 1.1 × 10¹⁵ (August) to 27.3 ± 3.9 × 10¹⁵ molec. cm⁻² (February) at Djougou (Fig. 5a) and from 2.2 ± 1.0 (June) to 6.1 ± 1.8 ppb (February) and from 2.7 ± 1.6 × 10¹⁵ (July) to 45.3 ± 5.3 × 10¹⁵ molec. cm⁻² (February) at Lamto (Fig. 5b), respectively. The mean annual variation coefficients are 23% and 41% for ground-based concentrations and 51% and 76% for IASI NH₃ total column measurements at Djougou and Lamto, respectively.

The monthly variations in ground-based NH₃ concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) over the two forested monitoring sites are presented in Fig. 6. The results show that the peak values of ground-based concentrations and IASI NH₃ total column densities are generally higher at Bomassa (Fig. 6a) compared to Zoétélé (Fig. 6b). The monthly 21-year coefficients of variation in NH₃ are of the same order of magnitude at Bomassa (55%) and Zoétélé (56%). Nevertheless, the monthly coefficients of variation in IASI NH₃ total column densities are significantly higher in the forested ecosystem compared to dry and wet savannas, i.e. more than 80% at Bomassa and Zoétélé over the 11-year period. Significant Pearson's correlations are found between monthly ground-based NH₃ and IASI NH₃ total column densities at Bomassa ($r = 0.18, p = 0.07$) and Zoétélé ($r = 0.34, p < 0.01$).

The monthly, seasonal, and annual measurement results of ground-based NH₃ concentrations and IASI NH₃ total columns at Bomassa and Zoétélé are summarized in Table 6. According to the *t* test, ground-based NH₃ average concentrations are significantly higher (*p* < 0.001) at Bomassa compared to Zoétélé, but IASI NH₃ total column average densities are of the same order of magnitude for these sites. For each forested ecosystem station, the results show that the mean ground-based NH₃ concentrations are of the same order of magnitude.
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Mean monthly ground-based NH₃ concentrations (Djougou: 2005–2018; Lamto: 1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures measured by ground-based instruments (1998–2018) and TRMM (2005–2018) (Djougou: 2005–2018; Lamto: 1998–2018) at (a) Djougou, Benin, and (b) Lamto, Côte d'Ivoire. Error bars represent the mean absolute deviation.</figcaption></figure>

Figure 5 presents the annual mean cycle of monthly ground-based concentrations and IASI NH₃ total column densities at Djougou (Fig. 5a) and Lamto (Fig. 5b) located in the wet savanna ecosystem. The results show that the annual mean ground-based and IASI NH₃ profiles have a poor covariation at Djougou (Fig. 5a), while IASI NH₃ shows a good agreement with ground-based NH₃ at the Lamto site (Fig. 5b). Ground-based NH₃ concentrations and IASI NH₃ total columns exhibit a seasonality at Lamto and Djougou stations with higher values occurring in the dry season (January to March) and lower values in the wet season (May through November). Mean annual cycles of IASI NH₃ total column density seasonality are less marked at Djougou compared to Lamto station. Monthly mean concentrations and total column densities of NH₃ range from 3.2 ± 1.4 (June) to 6.7 ± 1.8 ppb (February) and from 4.7 ± 1.1 × 10¹⁵ (August) to 27.3 ± 3.9 × 10¹⁵ molec. cm⁻² (February) at Djougou (Fig. 5a) and from 2.2 ± 1.0 (June) to 6.1 ± 1.8 ppb (February) and from 2.7 ± 1.6 × 10¹⁵ (July) to 45.3 ± 5.3 × 10¹⁵ molec. cm⁻² (February) at Lamto (Fig. 5b), respectively. The mean annual variation coefficients are 23% and 41% for ground-based concentrations and 51% and 76% for IASI NH₃ total column measurements at Djougou and Lamto, respectively.

The monthly variations in ground-based NH₃ concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) over the two forested monitoring sites are presented in Fig. 6. The results show that the peak values of ground-based concentrations and IASI NH₃ total column densities are generally higher at Bomassa (Fig. 6a) compared to Zoétélé (Fig. 6b). The monthly 21-year coefficients of variation in NH₃ are of the same order of magnitude at Bomassa (55%) and Zoétélé (56%). Nevertheless, the monthly coefficients of variation in IASI NH₃ total column densities are significantly higher in the forested ecosystem compared to dry and wet savannas, i.e. more than 80% at Bomassa and Zoétélé over the 11-year period. Significant Pearson's correlations are found between monthly ground-based NH₃ and IASI NH₃ total column densities at Bomassa ($r = 0.18, p = 0.07$) and Zoétélé ($r = 0.34, p < 0.01$).

The monthly, seasonal, and annual measurement results of ground-based NH₃ concentrations and IASI NH₃ total columns at Bomassa and Zoétélé are summarized in Table 6. According to the *t* test, ground-based NH₃ average concentrations are significantly higher (*p* < 0.001) at Bomassa compared to Zoétélé, but IASI NH₃ total column average densities are of the same order of magnitude for these sites. For each forested ecosystem station, the results show that the mean ground-based NH₃ concentrations are of the same order of magnitude.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Monthly time series of ground-based NH₃ concentrations over the period 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Bomassa, Republic of the Congo, and (b) Zoétélé, Cameroon. The dashed lines represent the linear regression lines.</figcaption></figure>

der of magnitude between wet and dry seasons. However, IASI total column densities are significantly higher (t test, *p* < 0.001) in the dry season at Bomassa and Zoétélé compared to the wet season (Table 6). We summarized the descriptive statistics of ground-based NH₃ concentrations in Table 7, separately for the periods 1998–2007 and 2008–2018. In general, we note that mean concentrations have increased for the Bomassa site but decreased at Zoétélé between 1998–2007 and 2008–2018. Indeed, mean dry season, annual, and wet season ground-based NH₃ concentrations in Bomassa have increased by 33%, 41%, and 44%, respectively, from 1998–2007 to 2008–2018 (Table 7). At Zoétélé, we observe a decrease of about 26% in the mean annual, wet season, and dry season ground-based concentrations of NH₃ between 1998–2007 and 2008–2018 (Table 7).

We present the mean annual ground-based NH₃ concentrations and IASI NH₃ evolutions based on monthly data measured in the forested ecosystems of Bomassa (Fig. 7a) and Zoétélé (Fig. 7b). Ground-based concentrations show no clear seasonality at Bomassa and Zoétélé. In contrast, IASI NH₃ total columns show a well-marked seasonality,

<table><caption>**Table 6.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) at Bomassa, Republic of the Congo, and Zoétélé, Cameroon.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub><br>(ppb)</th><th colspan="2">IASI NH<sub>3</sub><br>(10<sup>15</sup> molec. cm<sup>-2</sup>)</th></tr><tr><th>Bomassa</th><th>Zoétélé</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>-3.1</td><td>-0.1</td></tr><tr><td>Max</td><td>14.4</td><td>8.4</td><td>97</td><td>61.5</td></tr><tr><td>Annual</td><td>Avg</td><td>5.6 ± 1.4</td><td>3.4 ± 0.9</td><td>12.4 ± 2.1</td><td>13.8 ± 2.0</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.4</td><td>1.4</td><td>5.0</td><td>7.8</td></tr><tr><td>Max</td><td>8.6</td><td>5.6</td><td>10.9</td><td>12.4</td></tr><tr><td>Avg</td><td>5.6 ± 1.5</td><td>3.3 ± 0.9</td><td>7.9 ± 1.2</td><td>9.9 ± 1.4</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.3</td><td>1.2</td><td>20.4</td><td>14.8</td></tr><tr><td>Max</td><td>9.1</td><td>7.4</td><td>44.1</td><td>32.5</td></tr><tr><td>Avg</td><td>5.6 ± 1.7</td><td>3.8 ± 1.3</td><td>26.8 ± 4.8</td><td>22.0 ± 4.8</td></tr></tbody></table>

with high densities in the dry season (December to February) and low densities in the wet season (April to November).
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Monthly time series of ground-based NH₃ concentrations over the period 1998–2018 and IASI NH₃ total column densities from 2008 to 2018 at (a) Bomassa, Republic of the Congo, and (b) Zoétélé, Cameroon. The dashed lines represent the linear regression lines.</figcaption></figure>

der of magnitude between wet and dry seasons. However, IASI total column densities are significantly higher (t test, *p* < 0.001) in the dry season at Bomassa and Zoétélé compared to the wet season (Table 6). We summarized the descriptive statistics of ground-based NH₃ concentrations in Table 7, separately for the periods 1998–2007 and 2008–2018. In general, we note that mean concentrations have increased for the Bomassa site but decreased at Zoétélé between 1998–2007 and 2008–2018. Indeed, mean dry season, annual, and wet season ground-based NH₃ concentrations in Bomassa have increased by 33%, 41%, and 44%, respectively, from 1998–2007 to 2008–2018 (Table 7). At Zoétélé, we observe a decrease of about 26% in the mean annual, wet season, and dry season ground-based concentrations of NH₃ between 1998–2007 and 2008–2018 (Table 7).

We present the mean annual ground-based NH₃ concentrations and IASI NH₃ evolutions based on monthly data measured in the forested ecosystems of Bomassa (Fig. 7a) and Zoétélé (Fig. 7b). Ground-based concentrations show no clear seasonality at Bomassa and Zoétélé. In contrast, IASI NH₃ total columns show a well-marked seasonality,

<table><caption>**Table 6.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal INDAAF NH₃ ground-based concentrations (1998–2018) and IASI NH₃ total column densities (2008–2018) at Bomassa, Republic of the Congo, and Zoétélé, Cameroon.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Ground-based NH<sub>3</sub> (ppb)</th><th colspan="2">IASI NH<sub>3</sub> (10<sup>15</sup> molec.cm<sup>-2</sup>)</th></tr><tr><th>Bomassa</th><th>Zoétélé</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>0.7</td><td>0.7</td><td>-3.1</td><td>-0.1</td></tr><tr><td>Max</td><td>14.4</td><td>8.4</td><td>97</td><td>61.5</td></tr><tr><td>Annual</td><td>Avg</td><td>5.6 ± 1.4</td><td>3.4 ± 0.9</td><td>12.4 ± 2.1</td><td>13.8 ± 2.0</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.4</td><td>1.4</td><td>5.0</td><td>7.8</td></tr><tr><td>Max</td><td>8.6</td><td>5.6</td><td>10.9</td><td>12.4</td></tr><tr><td>Avg</td><td>5.6 ± 1.5</td><td>3.3 ± 0.9</td><td>7.9 ± 1.2</td><td>9.9 ± 1.4</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.3</td><td>1.2</td><td>20.4</td><td>14.8</td></tr><tr><td>Max</td><td>9.1</td><td>7.4</td><td>44.1</td><td>32.5</td></tr><tr><td>Avg</td><td>5.6 ± 1.7</td><td>3.8 ± 1.3</td><td>26.8 ± 4.8</td><td>22.0 ± 4.8</td></tr></tbody></table>

with high densities in the dry season (December to February) and low densities in the wet season (April to November).
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Mean monthly ground-based NH₃ concentrations (1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures (1998–2018) measured by ground-based instruments (1998–2018) and TRMM (2005–2018) at (a) Bomassa, Republic of the Congo, and (b) Zoétélé, Cameroon. Error bars represent the mean absolute deviation.</figcaption></figure>

<table><caption>**Table 7.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 1998–2007 and 2008–2018) at Bomassa, Republic of the Congo, and Zoétélé, Cameroon.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">1998–2007</th><th colspan="2">2008–2018</th></tr><tr><th>Bomassa</th><th>Zoétélé</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>1.3</td><td>0.7</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>14.4</td><td>8.0</td><td>13.6</td><td>8.4</td></tr><tr><td>Annual</td><td>Avg</td><td>4.6 ± 1.4</td><td>4.0 ± 0.7</td><td>6.5 ± 0.9</td><td>2.9 ± 0.7</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.4</td><td>1.5</td><td>4.4</td><td>1.4</td></tr><tr><td>Max</td><td>6.8</td><td>5.6</td><td>6.8</td><td>4.5</td></tr><tr><td>Avg</td><td>4.5 ± 1.4</td><td>3.8 ± 0.8</td><td>6.5 ± 0.9</td><td>2.8 ± 0.8</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.3</td><td>1.3</td><td>3.5</td><td>1.2</td></tr><tr><td>Max</td><td>8.4</td><td>5.8</td><td>9.1</td><td>7.4</td></tr><tr><td>Avg</td><td>4.8 ± 1.7</td><td>4.4 ± 1.0</td><td>6.4 ± 1.4</td><td>3.3 ± 0.9</td></tr></tbody></table>

ber) for the two sites. Mean monthly ground-based NH₃ concentrations slightly vary from a minimum of 4.1 ± 1.1 ppb (September) and a maximum of 7.1 ± 3.0 ppb (March) at Bomassa (Fig. 7a) and from a minimum and maximum of 2.4 ± 0.9 ppb (November) and 4.2 ± 1.6 ppb (March) at Zoétélé, respectively (Fig. 7b). NH₃ total column densities show a peak representing the annual maximum in

February (45.7 ± 13.6 × 10¹⁵ molec. cm⁻² for Bomassa and 37.1 ± 9.9 × 10¹⁵ molec. cm⁻² for Zoétélé) and the lowest values in July (2.2 ± 1.7 × 10¹⁵ molec. cm⁻² for Bomassa and 2.1 ± 1.0 × 10¹⁵ molec. cm⁻² for Zoétélé). Mean annual coefficients of variation over the 21-year period are 16% and 19% for NH₃ concentrations and more than 80% for IASI NH₃ over the 11-year period at Bomassa and Zoétélé, respectively.

Biomass burning is recognized as a significant source of atmospheric NH₃, especially in tropical regions but also at higher latitudes (Coheur et al., 2009; Lutsch et al., 2019; Whitburn et al., 2015). It represents the second largest terrestrial source of NH₃ after agriculture (Whitburn et al., 2017) and contributes about 13% of total NH₃ emissions (Galloway et al., 2004) at the global scale. Other major sources of NH₃ in African wet savanna and forest ecosystems include decomposition of urea from animal excreta, fertilized soils (Bouwman et al., 2002b), and domestic fuelwood burning (Adon et al., 2010; Lobert et al., 1990). In wet savannas and forests in Africa, the NH₃ concentrations represent a combination of all natural sources with the largest contribution from biomass burning sources (Adon et al., 2010).

Our study demonstrates that the highest NH₃ concentrations in wet savanna and forest ecosystems are recorded during the period when fires predominate (December–February), while the lowest are obtained when rainfall is
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Mean monthly ground-based NH₃ concentrations (1998–2018), IASI NH₃ total column densities (2008–2018), rainfall amounts, and air temperatures (1998–2018) measured by ground-based instruments (1998–2018) and TRMM (2005–2018) at (a) Bomassa, Republic of the Congo, and (b) Zoétélé, Cameroon. Error bars represent the mean absolute deviation.</figcaption></figure>

<table><caption>**Table 7.** Minimum (Min), maximum (Max), and average (Avg) monthly, annual, and seasonal ground-based NH₃ concentrations (in ppb; 1998–2007 and 2008–2018) at Bomassa, Republic of the Congo, and Zoétélé, Cameroon.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">1998–2007</th><th colspan="2">2008–2018</th></tr><tr><th>Bomassa</th><th>Zoétélé</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td rowspan="2">Monthly</td><td>Min</td><td>1.3</td><td>0.7</td><td>0.7</td><td>0.7</td></tr><tr><td>Max</td><td>14.4</td><td>8.0</td><td>13.6</td><td>8.4</td></tr><tr><td>Annual</td><td>Avg</td><td>4.6 ± 1.4</td><td>4.0 ± 0.7</td><td>6.5 ± 0.9</td><td>2.9 ± 0.7</td></tr><tr><td rowspan="3">Wet season</td><td>Min</td><td>2.4</td><td>1.5</td><td>4.4</td><td>1.4</td></tr><tr><td>Max</td><td>6.8</td><td>5.6</td><td>6.8</td><td>4.5</td></tr><tr><td>Avg</td><td>4.5 ± 1.4</td><td>3.8 ± 0.8</td><td>6.5 ± 0.9</td><td>2.8 ± 0.8</td></tr><tr><td rowspan="3">Dry season</td><td>Min</td><td>2.3</td><td>1.3</td><td>3.5</td><td>1.2</td></tr><tr><td>Max</td><td>8.4</td><td>5.8</td><td>9.1</td><td>7.4</td></tr><tr><td>Avg</td><td>4.8 ± 1.7</td><td>4.4 ± 1.0</td><td>6.4 ± 1.4</td><td>3.3 ± 0.9</td></tr></tbody></table>

ber) for the two sites. Mean monthly ground-based NH₃ concentrations slightly vary from a minimum of 4.1 ± 1.1 ppb (September) and a maximum of 7.1 ± 3.0 ppb (March) at Bomassa (Fig. 7a) and from a minimum and maximum of 2.4 ± 0.9 ppb (November) and 4.2 ± 1.6 ppb (March) at Zoétélé, respectively (Fig. 7b). NH₃ total column densities show a peak representing the annual maximum in

February (45.7 ± 13.6 × 10¹⁵ molec. cm⁻² for Bomassa and 37.1 ± 9.9 × 10¹⁵ molec. cm⁻² for Zoétélé) and the lowest values in July (2.2 ± 1.7 × 10¹⁵ molec. cm⁻² for Bomassa and 2.1 ± 1.0 × 10¹⁵ molec. cm⁻² for Zoétélé). Mean annual coefficients of variation over the 21-year period are 16% and 19% for NH₃ concentrations and more than 80% for IASI NH₃ over the 11-year period at Bomassa and Zoétélé, respectively.

Biomass burning is recognized as a significant source of atmospheric NH₃, especially in tropical regions but also at higher latitudes (Coheur et al., 2009; Lutsch et al., 2019; Whitburn et al., 2015). It represents the second largest terrestrial source of NH₃ after agriculture (Whitburn et al., 2017) and contributes about 13% of total NH₃ emissions (Galloway et al., 2004) at the global scale. Other major sources of NH₃ in African wet savanna and forest ecosystems include decomposition of urea from animal excreta, fertilized soils (Bouwman et al., 2002b), and domestic fuelwood burning (Adon et al., 2010; Lobert et al., 1990). In wet savannas and forests in Africa, the NH₃ concentrations represent a combination of all natural sources with the largest contribution from biomass burning sources (Adon et al., 2010).

Our study demonstrates that the highest NH₃ concentrations in wet savanna and forest ecosystems are recorded during the period when fires predominate (December–February), while the lowest are obtained when rainfall is
high. Indeed, during the dry season, farmers take advantage of the absence of rainfall to clear land, weed, and burn agricultural residues. This slash-and-burn agriculture contributes significantly to nitrogen ($NO_x$ and $NH_3$) and carbon (CO and $CO_2$) emissions (Tiemoko et al., 2021) into the atmosphere during the dry season. Fires related to agriculture and hunting become more important in the dry season and represent, respectively, 64% and 6% of the economic activities of the villagers in certain areas such as Lamto (Suzanne, 2016).

In order to show the influence of the combustion and anthropogenic sources on the atmospheric $NH_3$ concentrations and total column densities, we have conducted linear correlation study between monthly ground-based $NH_3$ concentrations and IASI $NH_3$ total column densities on the one hand and the GFED4 (1998–2018) and CEDS (1998–2018) emission data of $NH_3$ on the other hand. For the station of Djougou, correlations are calculated over the periods 2005–2018 and 2008–2018 for ground-based $NH_3$ and IASI $NH_3$, respectively. The Pearson correlation results are summarized in Table 8.

These results show that $NH_3$ emissions from biomass burning influence ground-based concentrations of $NH_3$ in Lamto and total column densities of $NH_3$ in the Lamto, Bomassa, and Zoétélé areas (Table 8). Similarly, there is an evident relationship between anthropogenic emissions by residential and agricultural sectors in the wet savanna and forest sites (Table 8). These results are consistent with the study of Whitburn et al. (2015) carried out in four regions, including “Africa north of Equator (ANE)” accounting for a major part of the total affected by fires. Indeed, they found a significant correlation ($r = 0.57$) between time series of monthly $NH_3$ columns retrieved from IASI measurements and MODIS fire radiative power (FRP) over the period 2008–2013 (Whitburn et al., 2015). The most likely explanation of these significant correlations between $NH_3$ (simultaneously for ground-based concentrations and total columns) and emission data (GFED4 and CEDS) is that $NH_3$ concentrations observed at Lamto are mainly influenced by biomass burning and agricultural sources (Adon et al., 2010, 2013; Whitburn et al., 2015). However, atmospheric $NH_3$ concentrations and columns in Djougou, Bomassa, and Zoétélé are also affected by human activities in these areas.

For the wet savanna and forested ecosystems where $NH_3$ seasonality is driven by biomass burning emissions, it looks like there is still an overall pattern of increasing $NH_3$ in the dry season and decreasing $NH_3$ in the rainy season that would be expected, which is not the case at Djougou. This modest increase in ground-based $NH_3$ concentrations in the wet season at Djougou could be due to the leaf area index (LAI) which is much lower there than in Lamto during the wet season with annual averages of about $1.2 \text{ m}^2 \text{ m}^{-2}$ in Djougou and $3.6 \text{ m}^2 \text{ m}^{-2}$ in Lamto (Ossohou et al., 2019). Indeed, $NH_3$ emissions during the wet season at Djougou are therefore less intercepted by the canopy via the dry deposition process. Note that canopy heights at Djougou and

Lamto look very different in Fig. 1. Dry deposition can be affected by both LAI and the vertical distance between canopy and instrument. The canopy looks much shorter at Djougou, with lots of vegetation being lower than the instrument. This could be another reason why $NH_3$ emissions may be less intercepted by the canopy at Djougou. In a general way, we assume that canopy interception and bi-directional exchange could play a role in reducing the seasonal variability at the surface (Adon et al., 2013; Delon et al., 2019) but not for the total column densities while keeping in mind that the satellite observations are for 100 km around each site, so they are influenced by a lot of non-local dynamics. It is also important to highlight the influence of air temperature, which significantly enhances $NH_3$ volatilization at Djougou, Lamto, and Bomassa with correlation coefficients ($p < 0.05$) that will be given in Sect. 3.2 to justify the significant $NH_3$ trends obtained for these sites.

## 3.2 Trends in ground-based $NH_3$ and IASI $NH_3$

We conduct the long-term trend computations by using the Mann–Kendall (MK) test coupled to Sen slope (SS) for annual, wet season, and dry season means for each year of ground-based concentrations (14- and 21-year periods) and total column densities within a diameter of 100 km centred around each site (11-year period). Additional trend analyses are carried out using the seasonal Kendall (SK) coupled to seasonal Kendall slope (SKS) only for monthly data over the entire period. We adopt significance thresholds of 90% ($p < 0.1$) for all trend analyses, and the percent increase or decrease is based on the mean concentrations or total column densities over each period.

In Sect. 3.2.1, we present and discuss trend results for annual, wet season, and dry season means of ground-based concentrations and IASI $NH_3$ total column densities in the three main ecosystems using the MK test coupled to SS. Section 3.2.2 focuses on long-term trends based on monthly data of $NH_3$ ground-based concentrations and total column densities at the six stations by using the SK test coupled to SKS. In these sections, we present only the results of significant trends. In the paragraph preceding the conclusion of the paper, we present a general comment on the trends obtained for each ecosystem and explain the differences obtained between ground-based concentration and total column density measurement trends.

Reported ground-based $NH_3$ concentration and IASI $NH_3$ trends are analysed in light of $NH_3$ emissions from biomass burning and anthropogenic sources (described in Sect. 2.3) and meteorological (air temperature and rainfall) and physical (LAI) parameters when available, which influence the atmospheric level of $NH_3$.
high. Indeed, during the dry season, farmers take advantage of the absence of rainfall to clear land, weed, and burn agricultural residues. This slash-and-burn agriculture contributes significantly to nitrogen ($NO_x$ and $NH_3$) and carbon (CO and $CO_2$) emissions (Tiemoko et al., 2021) into the atmosphere during the dry season. Fires related to agriculture and hunting become more important in the dry season and represent, respectively, 64% and 6% of the economic activities of the villagers in certain areas such as Lamto (Suzanne, 2016).

In order to show the influence of the combustion and anthropogenic sources on the atmospheric $NH_3$ concentrations and total column densities, we have conducted linear correlation study between monthly ground-based $NH_3$ concentrations and IASI $NH_3$ total column densities on the one hand and the GFED4 (1998–2018) and CEDS (1998–2018) emission data of $NH_3$ on the other hand. For the station of Djougou, correlations are calculated over the periods 2005–2018 and 2008–2018 for ground-based $NH_3$ and IASI $NH_3$, respectively. The Pearson correlation results are summarized in Table 8.

These results show that $NH_3$ emissions from biomass burning influence ground-based concentrations of $NH_3$ in Lamto and total column densities of $NH_3$ in the Lamto, Bomassa, and Zoétélé areas (Table 8). Similarly, there is an evident relationship between anthropogenic emissions by residential and agricultural sectors in the wet savanna and forest sites (Table 8). These results are consistent with the study of Whitburn et al. (2015) carried out in four regions, including “Africa north of Equator (ANE)” accounting for a major part of the total affected by fires. Indeed, they found a significant correlation ($r = 0.57$) between time series of monthly $NH_3$ columns retrieved from IASI measurements and MODIS fire radiative power (FRP) over the period 2008–2013 (Whitburn et al., 2015). The most likely explanation of these significant correlations between $NH_3$ (simultaneously for ground-based concentrations and total columns) and emission data (GFED4 and CEDS) is that $NH_3$ concentrations observed at Lamto are mainly influenced by biomass burning and agricultural sources (Adon et al., 2010, 2013; Whitburn et al., 2015). However, atmospheric $NH_3$ concentrations and columns in Djougou, Bomassa, and Zoétélé are also affected by human activities in these areas.

For the wet savanna and forested ecosystems where $NH_3$ seasonality is driven by biomass burning emissions, it looks like there is still an overall pattern of increasing $NH_3$ in the dry season and decreasing $NH_3$ in the rainy season that would be expected, which is not the case at Djougou. This modest increase in ground-based $NH_3$ concentrations in the wet season at Djougou could be due to the leaf area index (LAI) which is much lower there than in Lamto during the wet season with annual averages of about $1.2 m^2 m^{-2}$ in Djougou and $3.6 m^2 m^{-2}$ in Lamto (Ossohou et al., 2019). Indeed, $NH_3$ emissions during the wet season at Djougou are therefore less intercepted by the canopy via the dry deposition process. Note that canopy heights at Djougou and

Lamto look very different in Fig. 1. Dry deposition can be affected by both LAI and the vertical distance between canopy and instrument. The canopy looks much shorter at Djougou, with lots of vegetation being lower than the instrument. This could be another reason why $NH_3$ emissions may be less intercepted by the canopy at Djougou. In a general way, we assume that canopy interception and bi-directional exchange could play a role in reducing the seasonal variability at the surface (Adon et al., 2013; Delon et al., 2019) but not for the total column densities while keeping in mind that the satellite observations are for 100 km around each site, so they are influenced by a lot of non-local dynamics. It is also important to highlight the influence of air temperature, which significantly enhances $NH_3$ volatilization at Djougou, Lamto, and Bomassa with correlation coefficients ($p < 0.05$) that will be given in Sect. 3.2 to justify the significant $NH_3$ trends obtained for these sites.

## 3.2 Trends in ground-based $NH_3$ and IASI $NH_3$

We conduct the long-term trend computations by using the Mann–Kendall (MK) test coupled to Sen slope (SS) for annual, wet season, and dry season means for each year of ground-based concentrations (14- and 21-year periods) and total column densities within a diameter of 100 km centred around each site (11-year period). Additional trend analyses are carried out using the seasonal Kendall (SK) coupled to seasonal Kendall slope (SKS) only for monthly data over the entire period. We adopt significance thresholds of 90% ($p < 0.1$) for all trend analyses, and the percent increase or decrease is based on the mean concentrations or total column densities over each period.

In Sect. 3.2.1, we present and discuss trend results for annual, wet season, and dry season means of ground-based concentrations and IASI $NH_3$ total column densities in the three main ecosystems using the MK test coupled to SS. Section 3.2.2 focuses on long-term trends based on monthly data of $NH_3$ ground-based concentrations and total column densities at the six stations by using the SK test coupled to SKS. In these sections, we present only the results of significant trends. In the paragraph preceding the conclusion of the paper, we present a general comment on the trends obtained for each ecosystem and explain the differences obtained between ground-based concentration and total column density measurement trends.

Reported ground-based $NH_3$ concentration and IASI $NH_3$ trends are analysed in light of $NH_3$ emissions from biomass burning and anthropogenic sources (described in Sect. 2.3) and meteorological (air temperature and rainfall) and physical (LAI) parameters when available, which influence the atmospheric level of $NH_3$.
<table><caption>**Table 8.** Pearson correlation coefficients between NH₃ measurements (ground-based and IASI) and emission data (GFED4 and CEDS) at INDAAF wet savanna (Djougou, Benin, and Lamto, Côte d'Ivoire) and forest (Bomassa, Republic of the Congo, and Zoétélé, Cameroon) sites over the period 1998–2018 (Djougou: 2005–2018). Coefficients have been calculated from monthly data. Values in brackets represent significance levels (p values). Values in bold represent significant correlation coefficients.</caption><thead><tr><th></th><th>Djougou</th><th>Lamto</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td>Ground-based and GFED4</td><td>0.04 (0.63)</td><td><strong>0.34</strong> (&lt; 0.01)</td><td>-0.06 (0.39)</td><td>0.12 (0.07)</td></tr><tr><td>Ground-based and CEDS</td><td><strong>0.19</strong> (0.02)</td><td>-0.04 (0.5)</td><td><strong>0.22</strong> (&lt; 0.01)</td><td><strong>-0.27</strong> (&lt; 0.01)</td></tr><tr><td>IASI and GFED4</td><td>-0.06 (0.5)</td><td><strong>0.33</strong> (&lt; 0.01)</td><td><strong>0.18</strong> (0.04)</td><td><strong>0.39</strong> (&lt; 0.01)</td></tr><tr><td>IASI and CEDS</td><td><strong>0.27</strong> (&lt; 0.01)</td><td><strong>0.37</strong> (&lt; 0.01)</td><td><strong>0.24</strong> (&lt; 0.01)</td><td><strong>0.27</strong> (&lt; 0.01)</td></tr></tbody></table>

### 3.2.1 Annual trends

Globally, results indicate decreasing annual, wet season, and dry season trends in ground-based NH₃ concentrations for the three ecosystems except at Bomassa but increasing trends in IASI NH₃ total column densities. At the annual scale, results show there is no simultaneous trend for ground-based concentrations and total column densities of NH₃ at the same site.

Results indicate significant increases in IASI NH₃ total column densities at the dry savanna site of Katibougou of +0.34 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.46 % yr⁻¹), at the wet savanna site of Djougou of +0.42 × 10¹⁵ molec. cm⁻² yr⁻¹ (+2.65 % yr⁻¹), and at the forest site of Zoétélé of +0.47 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.42 % yr⁻¹) over the 11-year period. Surprisingly, for the forested ecosystem, annual ground-based NH₃ concentrations register an increasing trend at Bomassa of +0.14 ppb yr⁻¹ (+2.56 % yr⁻¹) but a decreasing trend at Zoétélé of -0.10 ppb yr⁻¹ (-2.95 % yr⁻¹) over the 21-year period.

We also investigate potential trends by applying the non-parametric MK test coupled to SS to the annual average of wet and dry seasons (separately) at the six stations representing the great ecosystems in sub-Saharan Africa. We observe in the wet season that NH₃ concentrations decrease at Katibougou in Malian dry savanna by -0.22 ppb yr⁻¹ (-3.25 % yr⁻¹) and at Zoétélé in Cameroon's forest ecosystem by -0.11 ppb yr⁻¹ (-3.24 % yr⁻¹) but increase at the other forested site of Bomassa in the Republic of the Congo by +0.13 ppb yr⁻¹ (+2.29 % yr⁻¹). Ground-based NH₃ concentrations in the dry season reveal decreasing trends in both dry savanna (-0.13 ppb yr⁻¹ or -2.41 % yr⁻¹ for Banizoumbou and -0.12 ppb yr⁻¹ or -2.26 % yr⁻¹ for Katibougou) and wet savanna (-0.08 ppb yr⁻¹ or -1.70 % yr⁻¹ for Lamto) sites. From satellite measurements, the significant increasing trends are obtained from mean wet season to mean wet season for IASI NH₃ total column densities at Banizoumbou (+0.36 × 10¹⁵ molec. cm⁻² yr⁻¹ or 3.20 % yr⁻¹), Katibougou (+0.55 × 10¹⁵ molec. cm⁻² yr⁻¹ or 6.01 % yr⁻¹), and Djougou (+0.24 × 10¹⁵ molec. cm⁻² yr⁻¹ or 1.77 % yr⁻¹). In the dry season, we obtain increasing trends at Katibougou (+0.24 × 10¹⁵ molec. cm⁻² yr⁻¹ or

2.33 % yr⁻¹), Djougou (+0.69 × 10¹⁵ molec. cm⁻² yr⁻¹ or 3.54 % yr⁻¹), and Zoétélé (+1.37 × 10¹⁵ molec. cm⁻² yr⁻¹ or 6.24 % yr⁻¹).

To investigate the potential causes of the observed trends in NH₃ concentrations at Zoétélé, we have applied MK trend and Pearson's correlation tests to meteorological and NH₃ emission data from GFED4 databases. The results show the decreasing trend in ground-based NH₃ concentrations in the wet season at Zoétélé could be attributed to wet season to wet season increases in the LAI (+0.69 % yr⁻¹), with a 99 % significant anti-correlation of -0.57 between these two variables. We do not yet know the cause of the increase in LAI from one wet season to the next in the Zoétélé forest ecosystem. However, more vegetation results in greater dry deposition rate, which would significantly reduce the observed wet season to wet season ground-based atmospheric NH₃ concentrations at Zoétélé. During the wet season, air humidity and soil moisture increase, leading to large NH₃ deposition on vegetation during wet months (Delon et al., 2019).

The increasing trends in IASI NH₃ total column densities at Katibougou (dry and wet seasons) and Djougou (annual, dry season, and wet season) are linked to emissions from the agricultural sector. Indeed, our results show significant correlations at 95 % between satellite data and NH₃ emissions from agriculture at Katibougou ($τ = 0.75$ and 0.96 in dry and wet seasons, respectively) and Djougou ($τ = 0.72$, 0.63, and 0.68 for annual, dry season, and wet season, respectively). In addition, the MK test coupled to SS applied to agricultural NH₃ emissions provided by the CEDS database shows increasing trends for annual means (+2.51 % yr⁻¹ at Djougou), annual average of wet season (+2.36 % yr⁻¹ at Katibougou and +2.56 % yr⁻¹ at Djougou), and annual average of dry season (+2.37 % yr⁻¹ at Katibougou and +2.47 % yr⁻¹ at Djougou) over the 2008–2018 period.

### 3.2.2 Trends accounting for seasonality

Long time series of atmospheric NH₃ could usually be affected by seasonality, which is the cyclical changes in concentrations or densities over the course of the year. The SK test is significantly robust in revealing trends in seasonal time series. In this section, we perform trend computations using SK coupled to SKS of monthly mean ground-based
<table><caption>**Table 8.** Pearson correlation coefficients between NH₃ measurements (ground-based and IASI) and emission data (GFED4 and CEDS) at INDAAF wet savanna (Djougou, Benin, and Lamto, Côte d'Ivoire) and forest (Bomassa, Republic of the Congo, and Zoétélé, Cameroon) sites over the period 1998–2018 (Djougou: 2005–2018). Coefficients have been calculated from monthly data. Values in brackets represent significance levels (p values). Values in bold represent significant correlation coefficients.</caption><thead><tr><th></th><th>Djougou</th><th>Lamto</th><th>Bomassa</th><th>Zoétélé</th></tr></thead><tbody><tr><td>Ground-based and GFED4</td><td>0.04 (0.63)</td><td><strong>0.34</strong> (&lt; 0.01)</td><td>-0.06 (0.39)</td><td>0.12 (0.07)</td></tr><tr><td>Ground-based and CEDS</td><td><strong>0.19</strong> (0.02)</td><td>-0.04 (0.5)</td><td><strong>0.22</strong> (&lt; 0.01)</td><td><strong>-0.27</strong> (&lt; 0.01)</td></tr><tr><td>IASI and GFED4</td><td>-0.06 (0.5)</td><td><strong>0.33</strong> (&lt; 0.01)</td><td><strong>0.18</strong> (0.04)</td><td><strong>0.39</strong> (&lt; 0.01)</td></tr><tr><td>IASI and CEDS</td><td><strong>0.27</strong> (&lt; 0.01)</td><td><strong>0.37</strong> (&lt; 0.01)</td><td><strong>0.24</strong> (&lt; 0.01)</td><td><strong>0.27</strong> (&lt; 0.01)</td></tr></tbody></table>

### 3.2.1 Annual trends

Globally, results indicate decreasing annual, wet season, and dry season trends in ground-based NH₃ concentrations for the three ecosystems except at Bomassa but increasing trends in IASI NH₃ total column densities. At the annual scale, results show there is no simultaneous trend for ground-based concentrations and total column densities of NH₃ at the same site.

Results indicate significant increases in IASI NH₃ total column densities at the dry savanna site of Katibougou of +0.34 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.46 % yr⁻¹), at the wet savanna site of Djougou of +0.42 × 10¹⁵ molec. cm⁻² yr⁻¹ (+2.65 % yr⁻¹), and at the forest site of Zoétélé of +0.47 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.42 % yr⁻¹) over the 11-year period. Surprisingly, for the forested ecosystem, annual ground-based NH₃ concentrations register an increasing trend at Bomassa of +0.14 ppb yr⁻¹ (+2.56 % yr⁻¹) but a decreasing trend at Zoétélé of -0.10 ppb yr⁻¹ (-2.95 % yr⁻¹) over the 21-year period.

We also investigate potential trends by applying the non-parametric MK test coupled to SS to the annual average of wet and dry seasons (separately) at the six stations representing the great ecosystems in sub-Saharan Africa. We observe in the wet season that NH₃ concentrations decrease at Katibougou in Malian dry savanna by -0.22 ppb yr⁻¹ (-3.25 % yr⁻¹) and at Zoétélé in Cameroon's forest ecosystem by -0.11 ppb yr⁻¹ (-3.24 % yr⁻¹) but increase at the other forested site of Bomassa in the Republic of the Congo by +0.13 ppb yr⁻¹ (+2.29 % yr⁻¹). Ground-based NH₃ concentrations in the dry season reveal decreasing trends in both dry savanna (-0.13 ppb yr⁻¹ or -2.41 % yr⁻¹ for Banizoumbou and -0.12 ppb yr⁻¹ or -2.26 % yr⁻¹ for Katibougou) and wet savanna (-0.08 ppb yr⁻¹ or -1.70 % yr⁻¹ for Lamto) sites. From satellite measurements, the significant increasing trends are obtained from mean wet season to mean wet season for IASI NH₃ total column densities at Banizoumbou (+0.36 × 10¹⁵ molec. cm⁻² yr⁻¹ or 3.20 % yr⁻¹), Katibougou (+0.55 × 10¹⁵ molec. cm⁻² yr⁻¹ or 6.01 % yr⁻¹), and Djougou (+0.24 × 10¹⁵ molec. cm⁻² yr⁻¹ or 1.77 % yr⁻¹). In the dry season, we obtain increasing trends at Katibougou (+0.24 × 10¹⁵ molec. cm⁻² yr⁻¹ or

2.33 % yr⁻¹), Djougou (+0.69 × 10¹⁵ molec. cm⁻² yr⁻¹ or 3.54 % yr⁻¹), and Zoétélé (+1.37 × 10¹⁵ molec. cm⁻² yr⁻¹ or 6.24 % yr⁻¹).

To investigate the potential causes of the observed trends in NH₃ concentrations at Zoétélé, we have applied MK trend and Pearson's correlation tests to meteorological and NH₃ emission data from GFED4 databases. The results show the decreasing trend in ground-based NH₃ concentrations in the wet season at Zoétélé could be attributed to wet season to wet season increases in the LAI (+0.69 % yr⁻¹), with a 99 % significant anti-correlation of -0.57 between these two variables. We do not yet know the cause of the increase in LAI from one wet season to the next in the Zoétélé forest ecosystem. However, more vegetation results in greater dry deposition rate, which would significantly reduce the observed wet season to wet season ground-based atmospheric NH₃ concentrations at Zoétélé. During the wet season, air humidity and soil moisture increase, leading to large NH₃ deposition on vegetation during wet months (Delon et al., 2019).

The increasing trends in IASI NH₃ total column densities at Katibougou (dry and wet seasons) and Djougou (annual, dry season, and wet season) are linked to emissions from the agricultural sector. Indeed, our results show significant correlations at 95 % between satellite data and NH₃ emissions from agriculture at Katibougou ($τ = 0.75$ and 0.96 in dry and wet seasons, respectively) and Djougou ($τ = 0.72$, 0.63, and 0.68 for annual, dry season, and wet season, respectively). In addition, the MK test coupled to SS applied to agricultural NH₃ emissions provided by the CEDS database shows increasing trends for annual means (+2.51 % yr⁻¹ at Djougou), annual average of wet season (+2.36 % yr⁻¹ at Katibougou and +2.56 % yr⁻¹ at Djougou), and annual average of dry season (+2.37 % yr⁻¹ at Katibougou and +2.47 % yr⁻¹ at Djougou) over the 2008–2018 period.

### 3.2.2 Trends accounting for seasonality

Long time series of atmospheric NH₃ could usually be affected by seasonality, which is the cyclical changes in concentrations or densities over the course of the year. The SK test is significantly robust in revealing trends in seasonal time series. In this section, we perform trend computations using SK coupled to SKS of monthly mean ground-based
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Estimated percentage changes in ground-based NH₃ concentrations (in black) and IASI NH₃ total column densities (in gray) for INDAAF network stations where monthly trends are significant at 90%. These trends are obtained using the monthly data of the overall database.</figcaption></figure>

NH₃ concentrations and IASI NH₃ total column densities for the entire dataset. Results for only significant monthly trends (*p* < 0.1) from all INDAAF sites are shown in Fig. 8. In general, the statistical tests reveal significant decreasing trends for ground-based NH₃ concentrations (except at Bomassa) but increasing trends for IASI NH₃ total column densities (Fig. 8).

Ground-based NH₃ concentrations decrease in the dry savannas of Banizoumbou by -0.03 ppb yr⁻¹ (-0.55 % yr⁻¹) and Katibougou by -0.16 ppb yr⁻¹ (-2.76 % yr⁻¹), but IASI NH₃ total column densities increase at the same sites by +0.14 × 10¹⁵ molec. cm⁻² yr⁻¹ (+1.29 % yr⁻¹) and +0.33 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.31 % yr⁻¹), respectively. A significant decreasing trend is also found for ground-based NH₃ concentrations in the wet savanna of Lamto (-0.06 ppb yr⁻¹ or -1.62 % yr⁻¹), but significant increasing trends are obtained for IASI NH₃ total column densities at both the Djougou (+0.45 × 10¹⁵ molec. cm⁻² yr⁻¹ or +2.83 % yr⁻¹) and Lamto (+0.54 × 10¹⁵ molec. cm⁻² yr⁻¹ or +2.58 % yr⁻¹) sites. The SK test applied to monthly ground-based NH₃ concentrations in the forested ecosystem sites shows a significant increasing trend by +0.12 ppb yr⁻¹ (+2.12 % yr⁻¹) at Bomassa but decreasing trend by -0.09 ppb yr⁻¹ (-2.55 % yr⁻¹) at Zoétélé. On the contrary, we obtained no significant trends for IASI NH₃ total column densities in the forested zone. The SK test applied to monthly data from January 1998 to December 2018 shows that relative humidity decreases by -0.15 % yr⁻¹ at Lamto. We calculate Pearson's correlation between ground-based NH₃ concentrations and relative humidity and find a coefficient of -0.50 significant at 99%. This statistical test demonstrates that the decreasing monthly trend in ground-based NH₃ concentrations cannot be explained by the monthly relative humidity trend.

In the dry savanna ecosystem, we believe that the simplest explanation for the increasing trend in IASI NH₃ total column densities observed at Katibougou is the significant increasing trend in NH₃ emissions by agricultural activities (+2.33 % yr⁻¹) for the period 2008–2018. In the wet

savanna ecosystems, we suggest that the increase in agricultural emissions of NH₃ (+2.02 % yr⁻¹) and air temperature (+0.06 °C yr⁻¹) for the period 2008–2018 at Djougou could be responsible for the increasing trend in IASI NH₃ total column densities at this site, with 99 % significant correlation coefficients of 0.36 and 0.65, respectively. At Lamto, the increase in total column densities of NH₃ could be attributed to increases in air temperature (+0.14 °C yr⁻¹) and emissions of NH₃ by agricultural activities (+2.3 % yr⁻¹) over the period 2008–2018. Indeed, there are significant linear correlations between IASI NH₃ and air temperature (τ = 0.72, *p* < 0.01) and between IASI NH₃ and agricultural emissions of NH₃ (τ = 0.50, *p* < 0.01). At the forested ecosystem of Bomassa, our results show that monthly air temperature and NH₃ emissions by anthropogenic sources (residential and agriculture) contribute to the increases in ground-based concentrations and total column densities of NH₃. Indeed, during ground-based concentrations and IASI total column density observations, we find increasing trends in air temperature (1998–2018: +0.03 °C yr⁻¹; 2008–2018: +0.02 °C yr⁻¹) and NH₃ emissions from residential (1998–2018: +5.78 % yr⁻¹; 2008–2018: +4.74 % yr⁻¹) and agricultural (1998–2018: +4.46 % yr⁻¹; 2008–2018: +1.52 % yr⁻¹) sectors at Bomassa. In addition, our study shows that monthly air temperature and these anthropogenic sources of NH₃ are significantly correlated with ground-based and IASI NH₃ at Bomassa over these two periods of data. We assume that increasing trends in atmospheric NH₃ at Bomassa are due to air temperature and residential and agricultural NH₃ emissions.

Trend studies of NH₃ concentrations and densities obtained, respectively, with the INDAAF passive samplers and the IASI instrument have shown significant trends depending on each biome. Overall, we obtained decreasing trends for ground-based measurements (except at the Bomassa forest site) but increasing trends for IASI total column densities of NH₃ (except in forest ecosystems). The long-term statistical trend results for NH₃ emissions from GFED4 databases are not significant, so biomass burning could not explain the trends obtained for the ground-based and satellite data. However, meteorological and anthropogenic emission data from CEDS clearly show that the drivers of atmospheric NH₃ trends are (1) agriculture in the dry savanna of Katibougou; (2) air temperature and agriculture in the wet savanna of Djougou and Lamto; and (3) air temperature, residential, and agriculture in the forest of Bomassa.

In our study, satellite observations integrated across 100 km centred around each site can be expected due to the very different nature of the observations. The IASI provides a total column value, which we have averaged over an area of roughly 7854 km² for each station comparison. The surface stations provide a point measurement at the surface. So any differences (a) between surface concentrations and concentrations at any other altitude in the atmosphere or (b) between composition at the station and at any other point in
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Estimated percentage changes in ground-based NH₃ concentrations (in black) and IASI NH₃ total column densities (in gray) for INDAAF network stations where monthly trends are significant at 90%. These trends are obtained using the monthly data of the overall database.</figcaption></figure>

NH₃ concentrations and IASI NH₃ total column densities for the entire dataset. Results for only significant monthly trends (*p* < 0.1) from all INDAAF sites are shown in Fig. 8. In general, the statistical tests reveal significant decreasing trends for ground-based NH₃ concentrations (except at Bomassa) but increasing trends for IASI NH₃ total column densities (Fig. 8).

Ground-based NH₃ concentrations decrease in the dry savannas of Banizoumbou by -0.03 ppb yr⁻¹ (-0.55 % yr⁻¹) and Katibougou by -0.16 ppb yr⁻¹ (-2.76 % yr⁻¹), but IASI NH₃ total column densities increase at the same sites by +0.14 × 10¹⁵ molec. cm⁻² yr⁻¹ (+1.29 % yr⁻¹) and +0.33 × 10¹⁵ molec. cm⁻² yr⁻¹ (+3.31 % yr⁻¹), respectively. A significant decreasing trend is also found for ground-based NH₃ concentrations in the wet savanna of Lamto (-0.06 ppb yr⁻¹ or -1.62 % yr⁻¹), but significant increasing trends are obtained for IASI NH₃ total column densities at both the Djougou (+0.45 × 10¹⁵ molec. cm⁻² yr⁻¹ or +2.83 % yr⁻¹) and Lamto (+0.54 × 10¹⁵ molec. cm⁻² yr⁻¹ or +2.58 % yr⁻¹) sites. The SK test applied to monthly ground-based NH₃ concentrations in the forested ecosystem sites shows a significant increasing trend by +0.12 ppb yr⁻¹ (+2.12 % yr⁻¹) at Bomassa but decreasing trend by -0.09 ppb yr⁻¹ (-2.55 % yr⁻¹) at Zoétélé. On the contrary, we obtained no significant trends for IASI NH₃ total column densities in the forested zone. The SK test applied to monthly data from January 1998 to December 2018 shows that relative humidity decreases by -0.15 % yr⁻¹ at Lamto. We calculate Pearson's correlation between ground-based NH₃ concentrations and relative humidity and find a coefficient of -0.50 significant at 99%. This statistical test demonstrates that the decreasing monthly trend in ground-based NH₃ concentrations cannot be explained by the monthly relative humidity trend.

In the dry savanna ecosystem, we believe that the simplest explanation for the increasing trend in IASI NH₃ total column densities observed at Katibougou is the significant increasing trend in NH₃ emissions by agricultural activities (+2.33 % yr⁻¹) for the period 2008–2018. In the wet

savanna ecosystems, we suggest that the increase in agricultural emissions of NH₃ (+2.02 % yr⁻¹) and air temperature (+0.06 °C yr⁻¹) for the period 2008–2018 at Djougou could be responsible for the increasing trend in IASI NH₃ total column densities at this site, with 99 % significant correlation coefficients of 0.36 and 0.65, respectively. At Lamto, the increase in total column densities of NH₃ could be attributed to increases in air temperature (+0.14 °C yr⁻¹) and emissions of NH₃ by agricultural activities (+2.3 % yr⁻¹) over the period 2008–2018. Indeed, there are significant linear correlations between IASI NH₃ and air temperature (τ = 0.72, *p* < 0.01) and between IASI NH₃ and agricultural emissions of NH₃ (τ = 0.50, *p* < 0.01). At the forested ecosystem of Bomassa, our results show that monthly air temperature and NH₃ emissions by anthropogenic sources (residential and agriculture) contribute to the increases in ground-based concentrations and total column densities of NH₃. Indeed, during ground-based concentrations and IASI total column density observations, we find increasing trends in air temperature (1998–2018: +0.03 °C yr⁻¹; 2008–2018: +0.02 °C yr⁻¹) and NH₃ emissions from residential (1998–2018: +5.78 % yr⁻¹; 2008–2018: +4.74 % yr⁻¹) and agricultural (1998–2018: +4.46 % yr⁻¹; 2008–2018: +1.52 % yr⁻¹) sectors at Bomassa. In addition, our study shows that monthly air temperature and these anthropogenic sources of NH₃ are significantly correlated with ground-based and IASI NH₃ at Bomassa over these two periods of data. We assume that increasing trends in atmospheric NH₃ at Bomassa are due to air temperature and residential and agricultural NH₃ emissions.

Trend studies of NH₃ concentrations and densities obtained, respectively, with the INDAAF passive samplers and the IASI instrument have shown significant trends depending on each biome. Overall, we obtained decreasing trends for ground-based measurements (except at the Bomassa forest site) but increasing trends for IASI total column densities of NH₃ (except in forest ecosystems). The long-term statistical trend results for NH₃ emissions from GFED4 databases are not significant, so biomass burning could not explain the trends obtained for the ground-based and satellite data. However, meteorological and anthropogenic emission data from CEDS clearly show that the drivers of atmospheric NH₃ trends are (1) agriculture in the dry savanna of Katibougou; (2) air temperature and agriculture in the wet savanna of Djougou and Lamto; and (3) air temperature, residential, and agriculture in the forest of Bomassa.

In our study, satellite observations integrated across 100 km centred around each site can be expected due to the very different nature of the observations. The IASI provides a total column value, which we have averaged over an area of roughly 7854 km² for each station comparison. The surface stations provide a point measurement at the surface. So any differences (a) between surface concentrations and concentrations at any other altitude in the atmosphere or (b) between composition at the station and at any other point in
the 7854 km² area can produce a mismatch between the sta-
tion observations and the IASI retrieval. In addition, NH₃
plumes from the combustion of biomass from distant sources
are likely less well captured from INDAAF passive samplers,
whereas they are very well measured by the IASI (Zheng et
al., 2021). It is likely that IR sounders have a higher sensitiv-
ity to fire plumes, which are located higher in the atmosphere
(and so the ground-based measurements will show less sen-
sitivity to them).

4 Conclusion

Using a 21-year period of INDAAF passive samplers and an 11-year period of the IASI product, we have characterized coevolutions and trends in atmospheric NH₃ at six stations of the INDAAF network in the African dry savanna (Banizoumbou, Niger, and Katibougou, Mali), wet savanna (Djougou, Benin, and Lamto, Côte d'Ivoire), and forest (Bomassa, Republic of the Congo, and Zoétélé, Cameroon). The remote sensing data are intended as a complement to the surface data to provide some insight into local dynamics near each surface station. The results showed that ground-based concentrations of NH₃ and IASI NH₃ total column densities are significantly higher in the dry savanna and wet savanna ecosystems, respectively. Indeed, mean annual ground-based concentrations of NH₃ over the 1998/2005–2018 period are 5.7–5.8 ppb in dry savanna, 3.5–4.7 ppb in wet savanna, and 3.4–5.6 ppb in forest ecosystems. The overall mean annual IASI NH₃ total column densities for a circle with a diameter of 100 km centred on each site over 2008–2018 are 10.1–11.0 × 10¹⁵ molec. cm⁻² in the dry savanna, 16.5–21.4 × 10¹⁵ molec. cm⁻² in the wet savanna, and 14.3–15.1 × 10¹⁵ molec. cm⁻² in the forest ecosystems. If we consider only ground-based measurements, the results show that NH₃ emissions from Sahelian soils, livestock, and agriculture (only at Katibougou) in the dry savanna ecosystem lead to average concentrations in the dry season that are equal to those obtained in the wet savanna ecosystem, globally dominated by biomass burning and agriculture.

We have recorded a 95 % significant Pearson correlation
between monthly ground-based concentrations and IASI to-
tal column densities of NH₃ at Banizoumbou (r = 0.30),
Lamto (r = 0.55), Bomassa (r = 0.18), and Zoétélé (r =
0.34), showing that NH₃ abundances at the wet savanna of
Lamto show the best agreement between ground-based and
satellite remote sensing. In the dry savanna sites of Bani-
zoumbou and Katibougou, the seasonal ground-based con-
centrations of NH₃ are highest both at the beginning and
the end of the wet season. Conversely, ground-based con-
centrations of NH₃ are highest in the dry season at the wet
savanna sites of Djougou and Lamto, but no marked season-
ality between wet and dry season was observed for ground-
based NH₃ concentrations in the forest sites of Bomassa and
Zoétélé. IASI NH₃ total column densities follow the same

seasonality as ground-based NH₃ concentrations in the dry
and wet savannas, while the seasonality is more marked in
the forested ecosystem.

The non-parametric Mann–Kendall statistical trend test shows a 90 % significant mean annual increasing trend for IASI NH₃ total column densities, which is the most important in the dry savanna of Katibougou (+3.98 % yr⁻¹). Ground-based NH₃ concentrations in the forested ecosystem increase at Bomassa (+2.56 % yr⁻¹) but decrease at Zoétélé (−2.95 % yr⁻¹). In both dry and wet seasons, ground-based NH₃ concentrations decrease from −3.25 % yr⁻¹ (Katibougou) to −1.70 % yr⁻¹ (Lamto) but increase in the wet season at Bomassa (+2.29 % yr⁻¹). IASI NH₃ total column densities increase in the wet season (+6.66 % yr⁻¹) and dry season (+2.55 % yr⁻¹) only at Katibougou. The seasonal Kendall test applied to monthly data over the entire period also shows decreasing trends at all the sites, except at Bomassa (+2.12 % yr⁻¹) for ground-based NH₃ concentrations. In contrast to trends calculated using ground-based observations, monthly IASI NH₃ total column densities increase for all ecosystems, ranging from +1.21 % yr⁻¹ (Bomassa) to +4.00 % yr⁻¹ (Katibougou). The increasing trends observed in dry seasons of wet savanna and forest ecosystems could be attributed to a longer residence time of NH₃ from biomass burning and agricultural-waste-burning sources in the atmosphere, which are the main sources of atmospheric NH₃ in this season. The decreasing trend in ground-based NH₃ concentrations in the wet season at Zoétélé could be related to wet season to wet season increases in the LAI (+0.69 % yr⁻¹), with a 99 % significant anti-correlation of −0.57 between these two variables. Emission inventories have inherent uncertainties that may come from activity data and emission factors or even from missing emission sources. In terms of measurement data, the monthly averaged data mask considerable diurnal variability in NH₃ concentrations. Drivers contributing to this variability include the influence of physical and meteorological parameters and the influence of local emission sources and interactions with others atmospheric compounds at INDAAF sites.

Results reported in this paper represent the unique long-
term regional characterization of ground-based NH₃ concen-
trations in Africa. Our study allows a better understanding
of the main drivers of the atmospheric NH₃ level of concen-
trations and trends. More field campaigns and experiments to
highlight others sources (soils, livestock, fertilizer quantities,
etc.) is still necessary before obtaining a definitive answer
to decreasing trends in ground-based concentrations of NH₃
at the INDAAF sites. The NitroAfrica project (2023–2026)
will help fill some of these gaps with valuable data on nitro-
gen in rural Africa. The overall objective of the NitroAfrica
project is to study – coupling field experiments and differ-
ent modelling approaches – the relationships and retroactions
between atmospheric N deposition, N cycling in the soil–
vegetation system, emissions of reactive N forms from the
the 7854 km² area can produce a mismatch between the sta-
tion observations and the IASI retrieval. In addition, NH₃
plumes from the combustion of biomass from distant sources
are likely less well captured from INDAAF passive samplers,
whereas they are very well measured by the IASI (Zheng et
al., 2021). It is likely that IR sounders have a higher sensitiv-
ity to fire plumes, which are located higher in the atmosphere
(and so the ground-based measurements will show less sen-
sitivity to them).

4 Conclusion

Using a 21-year period of INDAAF passive samplers and an 11-year period of the IASI product, we have characterized coevolutions and trends in atmospheric NH₃ at six stations of the INDAAF network in the African dry savanna (Banizoumbou, Niger, and Katibougou, Mali), wet savanna (Djougou, Benin, and Lamto, Côte d'Ivoire), and forest (Bomassa, Republic of the Congo, and Zoétélé, Cameroon). The remote sensing data are intended as a complement to the surface data to provide some insight into local dynamics near each surface station. The results showed that ground-based concentrations of NH₃ and IASI NH₃ total column densities are significantly higher in the dry savanna and wet savanna ecosystems, respectively. Indeed, mean annual ground-based concentrations of NH₃ over the 1998/2005–2018 period are 5.7–5.8 ppb in dry savanna, 3.5–4.7 ppb in wet savanna, and 3.4–5.6 ppb in forest ecosystems. The overall mean annual IASI NH₃ total column densities for a circle with a diameter of 100 km centred on each site over 2008–2018 are 10.1–11.0 × 10¹⁵ molec. cm⁻² in the dry savanna, 16.5–21.4 × 10¹⁵ molec. cm⁻² in the wet savanna, and 14.3–15.1 × 10¹⁵ molec. cm⁻² in the forest ecosystems. If we consider only ground-based measurements, the results show that NH₃ emissions from Sahelian soils, livestock, and agriculture (only at Katibougou) in the dry savanna ecosystem lead to average concentrations in the dry season that are equal to those obtained in the wet savanna ecosystem, globally dominated by biomass burning and agriculture.

We have recorded a 95 % significant Pearson correlation
between monthly ground-based concentrations and IASI to-
tal column densities of NH3 at Banizoumbou (r = 0.30),
Lamto (r = 0.55), Bomassa (r = 0.18), and Zoétélé (r =
0.34), showing that NH3 abundances at the wet savanna of
Lamto show the best agreement between ground-based and
satellite remote sensing. In the dry savanna sites of Bani-
zoumbou and Katibougou, the seasonal ground-based con-
centrations of NH3 are highest both at the beginning and
the end of the wet season. Conversely, ground-based con-
centrations of NH3 are highest in the dry season at the wet
savanna sites of Djougou and Lamto, but no marked season-
ality between wet and dry season was observed for ground-
based NH3 concentrations in the forest sites of Bomassa and
Zoétélé. IASI NH3 total column densities follow the same

seasonality as ground-based NH₃ concentrations in the dry
and wet savannas, while the seasonality is more marked in
the forested ecosystem.

The non-parametric Mann–Kendall statistical trend test shows a 90 % significant mean annual increasing trend for IASI NH₃ total column densities, which is the most important in the dry savanna of Katibougou (+3.98 % yr⁻¹). Ground-based NH₃ concentrations in the forested ecosystem increase at Bomassa (+2.56 % yr⁻¹) but decrease at Zoétélé (−2.95 % yr⁻¹). In both dry and wet seasons, ground-based NH₃ concentrations decrease from −3.25 % yr⁻¹ (Katibougou) to −1.70 % yr⁻¹ (Lamto) but increase in the wet season at Bomassa (+2.29 % yr⁻¹). IASI NH₃ total column densities increase in the wet season (+6.66 % yr⁻¹) and dry season (+2.55 % yr⁻¹) only at Katibougou. The seasonal Kendall test applied to monthly data over the entire period also shows decreasing trends at all the sites, except at Bomassa (+2.12 % yr⁻¹) for ground-based NH₃ concentrations. In contrast to trends calculated using ground-based observations, monthly IASI NH₃ total column densities increase for all ecosystems, ranging from +1.21 % yr⁻¹ (Bomassa) to +4.00 % yr⁻¹ (Katibougou). The increasing trends observed in dry seasons of wet savanna and forest ecosystems could be attributed to a longer residence time of NH₃ from biomass burning and agricultural-waste-burning sources in the atmosphere, which are the main sources of atmospheric NH₃ in this season. The decreasing trend in ground-based NH₃ concentrations in the wet season at Zoétélé could be related to wet season to wet season increases in the LAI (+0.69 % yr⁻¹), with a 99 % significant anti-correlation of −0.57 between these two variables. Emission inventories have inherent uncertainties that may come from activity data and emission factors or even from missing emission sources. In terms of measurement data, the monthly averaged data mask considerable diurnal variability in NH₃ concentrations. Drivers contributing to this variability include the influence of physical and meteorological parameters and the influence of local emission sources and interactions with others atmospheric compounds at INDAAF sites.

Results reported in this paper represent the unique long-
term regional characterization of ground-based NH₃ concen-
trations in Africa. Our study allows a better understanding
of the main drivers of the atmospheric NH₃ level of concen-
trations and trends. More field campaigns and experiments to
highlight others sources (soils, livestock, fertilizer quantities,
etc.) is still necessary before obtaining a definitive answer
to decreasing trends in ground-based concentrations of NH₃
at the INDAAF sites. The NitroAfrica project (2023–2026)
will help fill some of these gaps with valuable data on nitro-
gen in rural Africa. The overall objective of the NitroAfrica
project is to study – coupling field experiments and differ-
ent modelling approaches – the relationships and retroactions
between atmospheric N deposition, N cycling in the soil–
vegetation system, emissions of reactive N forms from the
surface to the atmosphere, atmospheric chemistry, and regional climate.

**Data availability.** The INDAAF NH₃ observations are available at https://indaaf.obs-mip.fr and at https://oscar.wmo.int/surface/#/ upon registration. GFED4 NH₃ data are available at https://eccad.sedoo.fr/#/metadata/433 upon registration. TRMM 3B42 precipitation data are available from https://pmm.nasa.gov/data-access/downloads/trmmT (last access: March 2020; https://doi.org/10.5067/TRMM/TMPA/MONTH/7, Tropical Rainfall Measuring Mission, 2011). The IASI NH₃ data are available from the IASI (https://iasi.aeris-data.fr, last access: 2 July 2023; https://doi.org/10.25326/12, Clarisse et al., 2018a; https://doi.org/10.25326/13, Clarisse et al., 2018b).

**Author contributions.** MO designed the study, conducted the statistical analysis, and wrote the paper. JEH and CGL contributed to study design and edited the paper. LC, PFC, and MVD developed the original IASI trace gas retrievals and edited the paper. MA and VY edited the paper. EG and MDA analysed the samples.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Acknowledgements.** This paper is part of the INDAAF (International Network to study Deposition and Atmospheric chemistry in Africa) long-term project supported by the CNRS/INSU (Centre National de la Recherche Scientifique/Institut National des Sciences de l'Univers), by the ACTRIS-FR research infrastructure, and by the IRD (Institut de Recherche pour le Développement). We are particularly grateful to all INDAAF local field technicians for their work. This work was supported by the CNES. 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 research in Belgium was funded by the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS) and the Air Liquide Foundation (TAPIR project). This work is also partly supported by the FED-tWIN project ARENBERG funded via the Belgian Science Policy Office (BELSPO). Lieven Clarisse is a research associate supported by the Belgian F.R.S.-FNRS.

**Financial support.** This study has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 871944.

**Review statement.** This paper was edited by Eleanor Browne and reviewed by two anonymous referees.

## References

Abbadie, L. (Ed.): Lamto: structure, functioning, and dynamics of a savanna ecosystem, Springer Science+Business Media, New York, 415 pp., ISBN 9780387948447, 2006.

Addinsoft: XLSTAT statistical and data analysis solution, Addinsoft, Paris, France, https://www.xlstat.com/fr (last access: 2 June 2023), 2022.

Adon, M., Galy-Lacaux, C., Yoboué, V., Delon, C., Lacaux, J. P., Castera, P., Gardrat, E., Pienaar, J., Al Ourabi, H., Laouali, D., Diop, B., Sigha-Nkamdjou, L., Akpo, A., Tathy, J. P., Lavenu, F., and Mougin, E.: Long term measurements of sulfur dioxide, nitrogen dioxide, ammonia, nitric acid and ozone in Africa using passive samplers, Atmos. Chem. Phys., 10, 7467–7487, https://doi.org/10.5194/acp-10-7467-2010, 2010.

Adon, M., Galy-Lacaux, C., Delon, C., Yoboue, V., Solmon, F., and Kaptue Tchuente, A. T.: Dry deposition of nitrogen compounds (NO₂, HNO₃, NH₃), sulfur dioxide and ozone in west and central African ecosystems using the inferential method, Atmos. Chem. Phys., 13, 11351–11374, https://doi.org/10.5194/acp-13-11351-2013, 2013.

Akpo, A. B., Galy-Lacaux, C., Laouali, D., Delon, C., Liousse, C., Adon, M., Gardrat, E., Mariscal, A., and Darakpa, C.: Precipitation chemistry and wet deposition in a remote wet savanna site in West Africa: Djougou (Benin), Atmos. Environ., 115, 110–123, https://doi.org/10.1016/j.atmosenv.2015.04.064, 2015.

Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.

Baek, B. H., Aneja, V. P., and Tong, Q.: Chemical coupling between ammonia, acid gases, and fine particles, Environ. Pollut., 129, 89–98, 2004.

Bahino, J., Yoboué, V., Galy-Lacaux, C., Adon, M., Akpo, A., Keita, S., Liousse, C., Gardrat, E., Chiron, C., Ossohou, M., Gnamien, S., and Djossou, J.: A pilot study of gaseous pollutants' measurement (NO₂, SO₂, NH₃, HNO₃ and O₃) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities, Atmos. Chem. Phys., 18, 5173–5198, https://doi.org/10.5194/acp-18-5173-2018, 2018.

Beale, C. A., Paulot, F., Randles, C. A., Wang, R., Guo, X., Clarisse, L., Van Damme, M., Coheur, P.-F., Clerbaux, C., Shephard, M. W., Dammers, E., Cady-Pereira, K., and Zondlo, M. A.: Large sub-regional differences of ammonia seasonal patterns over India reveal inventory discrepancies, Environ. Res. Lett., 17, 104006, https://doi.org/10.1088/1748-9326/ac881f, 2022.

Behera, S. N., Sharma, M., Aneja, V. P., and Balasubramanian, R.: Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies, Environ. Sci. Pollut. Res., 20, 8092–8131, https://doi.org/10.1007/s11356-013-2051-9, 2013.

Beusen, A. H. W., Bouwman, A. F., Heuberger, P. S. C., Van Drecht, G., and Van Der Hoek, K. W.: Bottom-up uncertainty estimates of global ammonia emissions from global agricultural production systems, Atmos. Environ., 42, 6067–6077, https://doi.org/10.1016/j.atmosenv.2008.03.044, 2008.
surface to the atmosphere, atmospheric chemistry, and regional climate.

**Data availability.** The INDAAF NH₃ observations are available at https://indaafobs-mip.fr and at https://oscar.wmo.int/surface/#/ upon registration. GFED4 NH₃ data are available at https://eccad.sedoo.fr/#/metadata/433 upon registration. TRMM 3B42 precipitation data are available from https://pmm.nasa.gov/data-access/downloads/trmmT (last access: March 2020; https://doi.org/10.5067/TRMM/TMPA/MONTH/7, Tropical Rainfall Measuring Mission, 2011). The IASI NH₃ data are available from the IASI (https://iasi.aeris-data.fr, last access: 2 July 2023; https://doi.org/10.25326/12, Clarisse et al., 2018a; https://doi.org/10.25326/13, Clarisse et al., 2018b).

**Author contributions.** MO designed the study, conducted the statistical analysis, and wrote the paper. JEH and CGL contributed to study design and edited the paper. LC, PFC, and MVD developed the original IASI trace gas retrievals and edited the paper. MA and VY edited the paper. EG and MDA analysed the samples.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Acknowledgements.** This paper is part of the INDAAF (International Network to study Deposition and Atmospheric chemistry in Africa) long-term project supported by the CNRS/INSU (Centre National de la Recherche Scientifique/Institut National des Sciences de l'Univers), by the ACTRIS-FR research infrastructure, and by the IRD (Institut de Recherche pour le Développement). We are particularly grateful to all INDAAF local field technicians for their work. This work was supported by the CNES. 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 research in Belgium was funded by the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS) and the Air Liquide Foundation (TAPIR project). This work is also partly supported by the FED-tWIN project ARENBERG funded via the Belgian Science Policy Office (BELSPO). Lieven Clarisse is a research associate supported by the Belgian F.R.S.-FNRS.

**Financial support.** This study has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 871944.

**Review statement.** This paper was edited by Eleanor Browne and reviewed by two anonymous referees.

## References

Abbadie, L. (Ed.): Lamto: structure, functioning, and dynamics of a savanna ecosystem, Springer Science+Business Media, New York, 415 pp., ISBN 9780387948447, 2006.

Addinsoft: XLSTAT statistical and data analysis solution, Addinsoft, Paris, France, https://www.xlstat.com/fr (last access: 2 June 2023), 2022.

Adon, M., Galy-Lacaux, C., Yoboué, V., Delon, C., Lacaux, J. P., Castera, P., Gardrat, E., Pienaar, J., Al Ourabi, H., Laouali, D., Diop, B., Sigha-Nkamdjou, L., Akpo, A., Tathy, J. P., Lavenu, F., and Mougin, E.: Long term measurements of sulfur dioxide, nitrogen dioxide, ammonia, nitric acid and ozone in Africa using passive samplers, Atmos. Chem. Phys., 10, 7467–7487, https://doi.org/10.5194/acp-10-7467-2010, 2010.

Adon, M., Galy-Lacaux, C., Delon, C., Yoboue, V., Solmon, F., and Kaptue Tchuente, A. T.: Dry deposition of nitrogen compounds (NO₂, HNO₃, NH₃), sulfur dioxide and ozone in west and central African ecosystems using the inferential method, Atmos. Chem. Phys., 13, 11351–11374, https://doi.org/10.5194/acp-13-11351-2013, 2013.

Akpo, A. B., Galy-Lacaux, C., Laouali, D., Delon, C., Liousse, C., Adon, M., Gardrat, E., Mariscal, A., and Darakpa, C.: Precipitation chemistry and wet deposition in a remote wet savanna site in West Africa: Djougou (Benin), Atmos. Environ., 115, 110–123, https://doi.org/10.1016/j.atmosenv.2015.04.064, 2015.

Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.

Baek, B. H., Aneja, V. P., and Tong, Q.: Chemical coupling between ammonia, acid gases, and fine particles, Environ. Pollut., 129, 89–98, 2004.

Bahino, J., Yoboué, V., Galy-Lacaux, C., Adon, M., Akpo, A., Keita, S., Liousse, C., Gardrat, E., Chiron, C., Ossohou, M., Gnamien, S., and Djossou, J.: A pilot study of gaseous pollutants' measurement (NO₂, SO₂, NH₃, HNO₃ and O₃) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities, Atmos. Chem. Phys., 18, 5173–5198, https://doi.org/10.5194/acp-18-5173-2018, 2018.

Beale, C. A., Paulot, F., Randles, C. A., Wang, R., Guo, X., Clarisse, L., Van Damme, M., Coheur, P.-F., Clerbaux, C., Shephard, M. W., Dammers, E., Cady-Pereira, K., and Zondlo, M. A.: Large sub-regional differences of ammonia seasonal patterns over India reveal inventory discrepancies, Environ. Res. Lett., 17, 104006, https://doi.org/10.1088/1748-9326/ac881f, 2022.

Behera, S. N., Sharma, M., Aneja, V. P., and Balasubramanian, R.: Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies, Environ. Sci. Pollut. Res., 20, 8092–8131, https://doi.org/10.1007/s11356-013-2051-9, 2013.

Beusen, A. H. W., Bouwman, A. F., Heuberger, P. S. C., Van Drecht, G., and Van Der Hoek, K. W.: Bottom-up uncertainty estimates of global ammonia emissions from global agricultural production systems, Atmos. Environ., 42, 6067–6077, https://doi.org/10.1016/j.atmosenv.2008.03.044, 2008.
Bouwman, A. F. and Van Der Hoek, K. W.: Scenarios of animal waste production and fertilizer use and associated ammonia emission for the developing countries, Atmos. Environ., 31, 4095–4102, https://doi.org/10.1016/S1352-2310(97)00288-4, 1997.

Bouwman, A. F., Lee, D. S., Asman, W. A. H., Dentener, F. J., Van Der Hoek, K. W., and Olivier, J. G. J.: A global high-resolution emission inventory for ammonia, Global Biogeochem. Cy., 11, 561–587, https://doi.org/10.1029/97GB02266, 1997.

Bouwman, A. F., Van Vuuren, D. P., Derwent, R. G., and Posch, M.: A Global Analysis of Acidification and Eutrophication of Terrestrial Ecosystems, Water Air Soil Poll., 141, 349–382, https://doi.org/10.1023/A:1021398008726, 2002a.

Bouwman, A. F., Boumans, L. J. M., and Batjes, N. H.: Estimation of global NH₃ volatilization loss from synthetic fertilizers and animal manure applied to arable lands and grasslands: Ammonia Emission From Fertilizers, Global Biogeochem. Cy., 16, 81–814, https://doi.org/10.1029/2000GB001389, 2002b.

Bray, C. D., Battye, W. H., Aneja, V. P., and Schlesinger, W. H.: Global emissions of NH₃, NOₓ and N₂O from biomass burning and the impact of climate change, J. Air Waste Manage., 71, 102–114, https://doi.org/10.1080/10962247.2020.1842822, 2021.

Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D., and Coheur, P.-F.: Global ammonia distribution derived from infrared satellite observations, Nat. Geosci., 2, 479–483, https://doi.org/10.1038/ngeo551, 2009.

Clarisse, L., Shephard, M. W., Dentener, F., Hurtmans, D., Cady-Pereira, K., Karagulian, F., Van Damme, M., Clerbaux, C., and Coheur, P.-F.: Satellite monitoring of ammonia: A case study of the San Joaquin Valley, J. Geophys. Res., 115, D13302, https://doi.org/10.1029/2009JD013291, 2010.

Clarisse, L., Van Damme, M., and Coheur, P.-F.: Reanalyzed daily IASI/Metop-A ULB-LATMOS ammonia (NH3) L2 product (total column), Aeris [data set], https://doi.org/10.25326/12, 2018a.

Clarisse, L., Van Damme, M., and Coheur, P.-F.: Reanalyzed daily IASI/Metop-B ULB-LATMOS ammonia (NH3) L2 product (total column), Aeris [data set], https://doi.org/10.25326/13, 2018b.

Clarisse, L., Van Damme, M., Gardner, W., Coheur, P.-F., Clerbaux, C., Whitburn, S., Hadji-Lazaro, J., and Hurtmans, D.: Atmospheric ammonia (NH₃) emanations from Lake Natron's saline mudflats, Sci. Rep., 9, 4441, https://doi.org/10.1038/s41598-019-39935-3, 2019.

Coheur, P.-F., Clarisse, L., Turquety, S., Hurtmans, D., and Clerbaux, C.: IASI measurements of reactive trace species in biomass burning plumes, Atmos. Chem. Phys., 9, 5655–5667, https://doi.org/10.5194/acp-9-5655-2009, 2009.

Crutzen, P. J. and Andreae, M. O.: Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles, Science, 250, 1669–1678, https://doi.org/10.1126/science.250.4988.1669, 1990.

Dammers, E., Shephard, M. W., Palm, M., Cady-Pereira, K., Capps, S., Lutsch, E., Strong, K., Hannigan, J. W., Ortega, I., Toon, G. C., Stremme, W., Grutter, M., Jones, N., Smale, D., Siemons, J., Hrpcek, K., Tremblay, D., Schaap, M., Notholt, J., and Erisman, J. W.: Validation of the CrIS fast physical NH₃ retrieval with ground-based FTIR, Atmos. Meas. Tech., 10, 2645–2667, https://doi.org/10.5194/amt-10-2645-2017, 2017.

Delmas, R., Lacaux, J. P., Menaut, J. C., Abbadie, L., Le Roux, X., Helas, G., and Lobert, J.: Nitrogen compound emission

from biomass burning in tropical African savanna FOS/DECAFE 1991 experiment (Lamto, Ivory Coast), J. Atmos. Chem., 22, 175–193, https://doi.org/10.1007/BF00708188, 1995.

Delon, C., Galy-Lacaux, C., Boone, A., Liousse, C., Serça, D., Adon, M., Diop, B., Akpo, A., Lavenu, F., Mougin, E., and Timouk, F.: Atmospheric nitrogen budget in Sahelian dry savannas, Atmos. Chem. Phys., 10, 2691–2708, https://doi.org/10.5194/acp-10-2691-2010, 2010.

Delon, C., Galy-Lacaux, C., Adon, M., Liousse, C., Serça, D., Diop, B., and Akpo, A.: Nitrogen compounds emission and deposition in West African ecosystems: comparison between wet and dry savanna, Biogeosciences, 9, 385–402, https://doi.org/10.5194/bg-9-385-2012, 2012.

Delon, C., Galy-Lacaux, C., Serça, D., Loubet, B., Camara, N., Gardrat, E., Saneh, I., Fensholt, R., Tagesson, T., Le Dantec, V., Sambou, B., Diop, C., and Mougin, E.: Soil and vegetation-atmosphere exchange of NO, NH₃, and N₂O from field measurements in a semi arid grazed ecosystem in Senegal, Atmos. Environ., 156, 36–51, https://doi.org/10.1016/j.atmosenv.2017.02.024, 2017.

Delon, C., Galy-Lacaux, C., Serça, D., Personne, E., Mougin, E., Adon, M., Le Dantec, V., Loubet, B., Fensholt, R., and Tagesson, T.: Modelling land-atmosphere daily exchanges of NO, NH₃, and CO₂ in a semi-arid grazed ecosystem in Senegal, Biogeosciences, 16, 2049–2077, https://doi.org/10.5194/bg-16-2049-2019, 2019.

de Rouw, A. and Rajot, J.-L.: Soil organic matter, surface crust-ing and erosion in Sahelian farming systems based on man-nuring or fallowing, Agr. Ecosyst. Environ., 104, 263–276, https://doi.org/10.1016/j.agee.2003.12.020, 2004.

Diawara, A., Yoroba, F., Kouadio, K. Y., Kouassi, K. B., Assamoi, E. M., Diedhiou, A., and Assamoi, P.: Climate Variability in the Sudano-Guinean Transition Area and Its Impact on Vegetation: The Case of the Lamto Region in Côte D'Ivoire, Adv. Meteorol., 2014, 1–11, https://doi.org/10.1155/2014/831414, 2014.

Erisman, J. W., Galloway, J. N., Seitzinger, S., Bleeker, A., Dise, N. B., Petrescu, A. M. R., Leach, A. M., and de Vries, W.: Consequences of human modification of the global nitrogen cycle, Phil. Trans. R. Soc. B, 368, 20130116, https://doi.org/10.1098/rstb.2013.0116, 2013.

Feng, L., Smith, S. J., Braun, C., Crippa, M., Gidden, M. J., Hoesly, R., Klimont, Z., van Marle, M., van den Berg, M., and van der Werf, G. R.: The generation of gridded emissions data for CMIP6, Geosci. Model Dev., 13, 461–482, https://doi.org/10.5194/gmd-13-461-2020, 2020.

Ferm, M.: A sensitive Diffusional Sampler, IVL publication B – 1020, 1–12, ISSN 0283-877X, 1991.

Fowler, D., Sutton, M. A., Smith, R. I., Pitcairn, C. E. R., Coyle, M., Campbell, G., and Stedman, J.: Regional mass budgets of oxidized and reduced nitrogen and their relative contribution to the nitrogen inputs of sensitive ecosystems, Environ. Pollut., 102, 337–342, https://doi.org/10.1016/S0269-7491(98)80052-3, 1998.

Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W., Howarth, R. W., Seitzinger, S. P., Asner, G. P., Cleveland, C. C., Green, P. A., Holland, E. A., Karl, D. M., Michaels, A. F., Porter, J. H., Townsend, A. R., and Vöosmarty, C. J.: Nitrogen Cycles: Past, Present, and Future, Biogeochemistry, 70, 153–226, https://doi.org/10.1007/s10533-004-0370-0, 2004.
Bouwman, A. F. and Van Der Hoek, K. W.: Scenarios of animal waste production and fertilizer use and associated ammonia emission for the developing countries, Atmos. Environ., 31, 4095–4102, https://doi.org/10.1016/S1352-2310(97)00288-4, 1997.

Bouwman, A. F., Lee, D. S., Asman, W. A. H., Dentener, F. J., Van Der Hoek, K. W., and Olivier, J. G. J.: A global high-resolution emission inventory for ammonia, Global Biogeochem. Cy., 11, 561–587, https://doi.org/10.1029/97GB02266, 1997.

Bouwman, A. F., Van Vuuren, D. P., Derwent, R. G., and Posch, M.: A Global Analysis of Acidification and Eutrophication of Terrestrial Ecosystems, Water Air Soil Poll., 141, 349–382, https://doi.org/10.1023/A:1021398008726, 2002a.

Bouwman, A. F., Boumans, L. J. M., and Batjes, N. H.: Estimation of global NH₃ volatilization loss from synthetic fertilizers and animal manure applied to arable lands and grasslands: Ammonia Emission From Fertilizers, Global Biogeochem. Cy., 16, 81–814, https://doi.org/10.1029/2000GB001389, 2002b.

Bray, C. D., Battye, W. H., Aneja, V. P., and Schlesinger, W. H.: Global emissions of NH₃, NOₓ and N₂O from biomass burning and the impact of climate change, J. Air Waste Manage., 71, 102–114, https://doi.org/10.1080/10962247.2020.1842822, 2021.

Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D., and Coheur, P.-F.: Global ammonia distribution derived from infrared satellite observations, Nat. Geosci., 2, 479–483, https://doi.org/10.1038/ngeo551, 2009.

Clarisse, L., Shephard, M. W., Dentener, F., Hurtmans, D., Cady-Pereira, K., Karagulian, F., Van Damme, M., Clerbaux, C., and Coheur, P.-F.: Satellite monitoring of ammonia: A case study of the San Joaquin Valley, J. Geophys. Res., 115, D13302, https://doi.org/10.1029/2009JD013291, 2010.

Clarisse, L., Van Damme, M., and Coheur, P.-F.: Reanalyzed daily IASI/Metop-A ULB-LATMOS ammonia (NH3) L2 product (total column), Aeris [data set], https://doi.org/10.25326/12, 2018a.

Clarisse, L., Van Damme, M., and Coheur, P.-F.: Reanalyzed daily IASI/Metop-B ULB-LATMOS ammonia (NH3) L2 product (total column), Aeris [data set], https://doi.org/10.25326/13, 2018b.

Clarisse, L., Van Damme, M., Gardner, W., Coheur, P.-F., Clerbaux, C., Whitburn, S., Hadji-Lazaro, J., and Hurtmans, D.: Atmospheric ammonia (NH₃) emanations from Lake Natron's saline mudflats, Sci. Rep., 9, 4441, https://doi.org/10.1038/s41598-019-39935-3, 2019.

Coheur, P.-F., Clarisse, L., Turquety, S., Hurtmans, D., and Clerbaux, C.: IASI measurements of reactive trace species in biomass burning plumes, Atmos. Chem. Phys., 9, 5655–5667, https://doi.org/10.5194/acp-9-5655-2009, 2009.

Crutzen, P. J. and Andreae, M. O.: Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles, Science, 250, 1669–1678, https://doi.org/10.1126/science.250.4988.1669, 1990.

Dammers, E., Shephard, M. W., Palm, M., Cady-Pereira, K., Capps, S., Lutsch, E., Strong, K., Hannigan, J. W., Ortega, I., Toon, G. C., Stremme, W., Grutter, M., Jones, N., Smale, D., Siemons, J., Hrpcek, K., Tremblay, D., Schaap, M., Notholt, J., and Erisman, J. W.: Validation of the CrIS fast physical NH₃ retrieval with ground-based FTIR, Atmos. Meas. Tech., 10, 2645–2667, https://doi.org/10.5194/amt-10-2645-2017, 2017.

Delmas, R., Lacaux, J. P., Menaut, J. C., Abbadie, L., Le Roux, X., Helas, G., and Lobert, J.: Nitrogen compound emission

from biomass burning in tropical African savanna FOS/DECAFE 1991 experiment (Lamto, Ivory Coast), J. Atmos. Chem., 22, 175–193, https://doi.org/10.1007/BF00708188, 1995.

Delon, C., Galy-Lacaux, C., Boone, A., Liousse, C., Serça, D., Adon, M., Diop, B., Akpo, A., Lavenu, F., Mougin, E., and Timouk, F.: Atmospheric nitrogen budget in Sahelian dry savannas, Atmos. Chem. Phys., 10, 2691–2708, https://doi.org/10.5194/acp-10-2691-2010, 2010.

Delon, C., Galy-Lacaux, C., Adon, M., Liousse, C., Serça, D., Diop, B., and Akpo, A.: Nitrogen compounds emission and deposition in West African ecosystems: comparison between wet and dry savanna, Biogeosciences, 9, 385–402, https://doi.org/10.5194/bg-9-385-2012, 2012.

Delon, C., Galy-Lacaux, C., Serça, D., Loubet, B., Camara, N., Gardrat, E., Saneh, I., Fensholt, R., Tagesson, T., Le Dantec, V., Sambou, B., Diop, C., and Mougin, E.: Soil and vegetation-atmosphere exchange of NO, NH₃, and N₂O from field measurements in a semi arid grazed ecosystem in Senegal, Atmos. Environ., 156, 36–51, https://doi.org/10.1016/j.atmosenv.2017.02.024, 2017.

Delon, C., Galy-Lacaux, C., Serça, D., Personne, E., Mougin, E., Adon, M., Le Dantec, V., Loubet, B., Fensholt, R., and Tagesson, T.: Modelling land-atmosphere daily exchanges of NO, NH₃, and CO₂ in a semi-arid grazed ecosystem in Senegal, Biogeosciences, 16, 2049–2077, https://doi.org/10.5194/bg-16-2049-2019, 2019.

de Rouw, A. and Rajot, J.-L.: Soil organic matter, surface crust-ing and erosion in Sahelian farming systems based on man-nuring or fallowing, Agr. Ecosyst. Environ., 104, 263–276, https://doi.org/10.1016/j.agee.2003.12.020, 2004.

Diawara, A., Yoroba, F., Kouadio, K. Y., Kouassi, K. B., Assamoi, E. M., Diedhiou, A., and Assamoi, P.: Climate Variability in the Sudano-Guinean Transition Area and Its Impact on Vegetation: The Case of the Lamto Region in Côte D'Ivoire, Adv. Meteorol., 2014, 1–11, https://doi.org/10.1155/2014/831414, 2014.

Erisman, J. W., Galloway, J. N., Seitzinger, S., Bleeker, A., Dise, N. B., Petrescu, A. M. R., Leach, A. M., and de Vries, W.: Consequences of human modification of the global nitrogen cycle, Phil. Trans. R. Soc. B, 368, 20130116, https://doi.org/10.1098/rstb.2013.0116, 2013.

Feng, L., Smith, S. J., Braun, C., Crippa, M., Gidden, M. J., Hoesly, R., Klimont, Z., van Marle, M., van den Berg, M., and van der Werf, G. R.: The generation of gridded emissions data for CMIP6, Geosci. Model Dev., 13, 461–482, https://doi.org/10.5194/gmd-13-461-2020, 2020.

Ferm, M.: A sensitive Diffusional Sampler, IVL publication B – 1020, 1–12, ISSN 0283-877X, 1991.

Fowler, D., Sutton, M. A., Smith, R. I., Pitcairn, C. E. R., Coyle, M., Campbell, G., and Stedman, J.: Regional mass budgets of oxidized and reduced nitrogen and their relative contribution to the nitrogen inputs of sensitive ecosystems, Environ. Pollut., 102, 337–342, https://doi.org/10.1016/S0269-7491(98)80052-3, 1998.

Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W., Howarth, R. W., Seitzinger, S. P., Asner, G. P., Cleveland, C. C., Green, P. A., Holland, E. A., Karl, D. M., Michaels, A. F., Porter, J. H., Townsend, A. R., and Vöosmarty, C. J.: Nitrogen Cycles: Past, Present, and Future, Biogeochemistry, 70, 153–226, https://doi.org/10.1007/s10533-004-0370-0, 2004.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.

Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): analysis of burned area, J. Geophys. Res.-Biogeosciences, 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.

Guo, X., Wang, R., Pan, D., Zondlo, M. A., Clarisse, L., Van Damme, M., Whitburn, S., Coheur, P., Clerbaux, C., Franco, B., Golston, L. M., Wendt, L., Sun, K., Tao, L., Miller, D., Mikoviny, T., Müller, M., Wisthaler, A., Tevlin, A. G., Murphy, J. G., Nowak, J. B., Roscioli, J. R., Volkamer, R., Kille, N., Neuman, J. A., Eilerman, S. J., Crawford, J. H., Yacovitch, T. I., Barrick, J. D., and Scarino, A. J.: Validation of IASI Satellite Ammonia Observations at the Pixel Scale Using In Situ Vertical Profiles, J. Geophys. Res.-Atmos., 126, e2020JD033475, https://doi.org/10.1029/2020JD033475, 2021.

Hickman, J. E., Dammers, E., Galy-Lacaux, C., and van der Werf, G. R.: Satellite evidence of substantial rain-induced soil emissions of ammonia across the Sahel, Atmos. Chem. Phys., 18, 16713–16727, https://doi.org/10.5194/acp-18-16713-2018, 2018.

Hickman, J. E., Andela, N., Dammers, E., Clarisse, L., Coheur, P.-F., Van Damme, M., Di Vittorio, C. A., Ossohou, M., Galy-Lacaux, C., Tsigaridis, K., and Bauer, S. E.: Changes in biomass burning, wetland extent, or agriculture drive atmospheric NH₃ trends in select African regions, Atmos. Chem. Phys., 21, 16277–16291, https://doi.org/10.5194/acp-21-16277-2021, 2021.

Hirsch, R. M., Slack, J. R., and Smith, R. A.: Techniques of trend analysis for monthly water quality data, Water Resour. Res., 18, 107–121, https://doi.org/10.1029/WR018i001p00107, 1982.

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.

Jaeglé, L., Martin, R. V., Chance, K., Steinberger, L., Kurosu, T. P., Jacob, D. J., Modi, A. I., Yoboué, V., Sigha-Nkamdjou, L., and Galy-Lacaux, C.: Satellite mapping of rain-induced nitric oxide emissions from soils, J. Geophys. Res., 109, D21310, https://doi.org/10.1029/2004JD004787, 2004.

Kendall, M. G.: Rank Correlation Methods, 4th edn., Charles Griffin, London, ISBN 9780852641996, 1975.

Koziel, J. A., Aneja, V. P., and Baek, B.-H.: Gas-to-Particle Conversion Process between Ammonia, Acid Gases, and Fine Particles in the Atmosphere, Animal Agriculture and the Environment, National Center for Manure and Animal Waste Management

White Papers, 201–224, https://doi.org/10.13031/2013.20254, 2006.

Kumar, M., Parmar, K. S., Kumar, D. B., Mhawish, A., Broday, D. M., Mall, R. K., and Banerjee, T.: Long-term aerosol climatology over Indo-Gangetic Plain: Trend, prediction and potential source fields, Atmos. Environ., 180, 37–50, https://doi.org/10.1016/j.atmosenv.2018.02.027, 2018.

Le Roux, X., Abbadie, L., Fritz, H., and Leriche, H.: Modification of the Savanna Functioning by Herbivores, in: Lamto, vol. 179, edited by: Abbadie, L., Gignoux, J., Le Roux, X., and Lepage, M., Springer New York, New York, NY, 185–198, https://doi.org/10.1007/978-0-387-33857-6_10, 2006.

Lobert, J. M., Scharffe, D. H., Hao, W. M., and Crutzen, P. J.: Importance of biomass burning in the atmospheric budgets of nitrogen-containing gases, Nature, 346, 552–554, https://doi.org/10.1038/346552a0, 1990.

Lutsch, E., Strong, K., Jones, D. B. A., Ortega, I., Hannigan, J. W., Dammers, E., Shephard, M. W., Morris, E., Murphy, K., Evans, M. J., Parrington, M., Whitburn, S., Van Damme, M., Clarisse, L., Coheur, P., Clerbaux, C., Croft, B., Martin, R. V., Pierce, J. R., and Fisher, J. A.: Unprecedented Atmospheric Ammonia Concentrations Detected in the High Arctic From the 2017 Canadian Wildfires, J. Geophys. Res.-Atmos., 124, 8178–8202, https://doi.org/10.1029/2019JD030419, 2019.

Malm, W. C., Schichtel, B. A., Pitchford, M. L., Ashbaugh, L. L., and Eldred, R. A.: Spatial and monthly trends in specified fine particle concentration in the United States: specified fine particle concentration, J. Geophys. Res., 109, D03306, https://doi.org/10.1029/2003JD003739, 2004.

Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.

Mayaux, P., Bartholomé, E., Fritz, S., and Belward, A.: A new land-cover map of Africa for the year 2000: New land-cover map of Africa, J. Biogeogr., 31, 861–877, https://doi.org/10.1111/j.1365-2699.2004.01073.x, 2004.

McCalley, C. K. and Sparks, J. P.: Controls over nitric oxide and ammonia emissions from Mojave Desert soils, Oecologia, 156, 871–881, https://doi.org/10.1007/s00442-008-1031-0, 2008.

Mitani, M., Yamagiwa, J., Oko, R. A., Moutsamboté, J.-M., Yumoto, T., and Maruhashi, T.: Approaches in Density Estimates and Reconstruction of Social Groups in a Western Lowland Gorilla Population in the Ndoki Forest, Northern Congo, Tropics, 2, 219–229, https://doi.org/10.3759/tropics.2.219, 1993.

Nicholson, S. E., Some, B., McCollum, J., Nelkin, E., Klotter, D., Berte, Y., Diallo, B. M., Gaye, I., Kpabeba, G., Ndiaye, O., Noukpozounkou, J. N., Tanu, M. M., Thiam, A., Toure, A. A., and Traore, A. K.: Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part I: Validation of GPCC Rainfall Product and Pre-TRMM Satellite and Blended Products, J. Appl. Meteorol., 42, 1337–1354, https://doi.org/10.1175/1520-0450(2003)042<1337:VOTAOR>2.0.CO;2, 2003.

O'Rourke, P., Smith, S., Mott, A., Ahsan, H., Mcduffie, E., Crippa, M., Klimont, Z., Mcdonald, B., Wang, S., Nicholson, M., Hoesly, R., and Feng, L.: CEDS v_2021_04_21 Gridded emissions data, DataHub [data set], https://doi.org/10.25584/PNNLDATAHUB/1779095, 2021.

Ossohou, M., Galy-Lacaux, C., Yoboué, V., Hickman, J. E., Gardrat, E., Adon, M., Darras, S., Laouali, D., Akpo, A., Ouafao, M.,
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.

Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): analysis of burned area, J. Geophys. Res.-Biogeosciences, 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.

Guo, X., Wang, R., Pan, D., Zondlo, M. A., Clarisse, L., Van Damme, M., Whitburn, S., Coheur, P., Clerbaux, C., Franco, B., Golston, L. M., Wendt, L., Sun, K., Tao, L., Miller, D., Mikoviny, T., Müller, M., Wisthaler, A., Tevlin, A. G., Murphy, J. G., Nowak, J. B., Roscioli, J. R., Volkamer, R., Kille, N., Neuman, J. A., Eilerman, S. J., Crawford, J. H., Yacovitch, T. I., Barrick, J. D., and Scarino, A. J.: Validation of IASI Satellite Ammonia Observations at the Pixel Scale Using In Situ Vertical Profiles, J. Geophys. Res.-Atmos., 126, e2020JD033475, https://doi.org/10.1029/2020JD033475, 2021.

Hickman, J. E., Dammers, E., Galy-Lacaux, C., and van der Werf, G. R.: Satellite evidence of substantial rain-induced soil emissions of ammonia across the Sahel, Atmos. Chem. Phys., 18, 16713–16727, https://doi.org/10.5194/acp-18-16713-2018, 2018.

Hickman, J. E., Andela, N., Dammers, E., Clarisse, L., Coheur, P.-F., Van Damme, M., Di Vittorio, C. A., Ossohou, M., Galy-Lacaux, C., Tsigaridis, K., and Bauer, S. E.: Changes in biomass burning, wetland extent, or agriculture drive atmospheric NH₃ trends in select African regions, Atmos. Chem. Phys., 21, 16277–16291, https://doi.org/10.5194/acp-21-16277-2021, 2021.

Hirsch, R. M., Slack, J. R., and Smith, R. A.: Techniques of trend analysis for monthly water quality data, Water Resour. Res., 18, 107–121, https://doi.org/10.1029/WR018i001p00107, 1982.

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.

Jaeglé, L., Martin, R. V., Chance, K., Steinberger, L., Kurosu, T. P., Jacob, D. J., Modi, A. I., Yoboué, V., Sigha-Nkamdjou, L., and Galy-Lacaux, C.: Satellite mapping of rain-induced nitric oxide emissions from soils, J. Geophys. Res., 109, D21310, https://doi.org/10.1029/2004JD004787, 2004.

Kendall, M. G.: Rank Correlation Methods, 4th edn., Charles Griffin, London, ISBN 9780852641996, 1975.

Koziel, J. A., Aneja, V. P., and Baek, B.-H.: Gas-to-Particle Conversion Process between Ammonia, Acid Gases, and Fine Particles in the Atmosphere, Animal Agriculture and the Environment, National Center for Manure and Animal Waste Management

White Papers, 201–224, https://doi.org/10.13031/2013.20254, 2006.

Kumar, M., Parmar, K. S., Kumar, D. B., Mhawish, A., Broday, D. M., Mall, R. K., and Banerjee, T.: Long-term aerosol climatology over Indo-Gangetic Plain: Trend, prediction and potential source fields, Atmos. Environ., 180, 37–50, https://doi.org/10.1016/j.atmosenv.2018.02.027, 2018.

Le Roux, X., Abbadie, L., Fritz, H., and Leriche, H.: Modification of the Savanna Functioning by Herbivores, in: Lamto, vol. 179, edited by: Abbadie, L., Gignoux, J., Le Roux, X., and Lepage, M., Springer New York, New York, NY, 185–198, https://doi.org/10.1007/978-0-387-33857-6_10, 2006.

Lobert, J. M., Scharffe, D. H., Hao, W. M., and Crutzen, P. J.: Importance of biomass burning in the atmospheric budgets of nitrogen-containing gases, Nature, 346, 552–554, https://doi.org/10.1038/346552a0, 1990.

Lutsch, E., Strong, K., Jones, D. B. A., Ortega, I., Hannigan, J. W., Dammers, E., Shephard, M. W., Morris, E., Murphy, K., Evans, M. J., Parrington, M., Whitburn, S., Van Damme, M., Clarisse, L., Coheur, P., Clerbaux, C., Croft, B., Martin, R. V., Pierce, J. R., and Fisher, J. A.: Unprecedented Atmospheric Ammonia Concentrations Detected in the High Arctic From the 2017 Canadian Wildfires, J. Geophys. Res.-Atmos., 124, 8178–8202, https://doi.org/10.1029/2019JD030419, 2019.

Malm, W. C., Schichtel, B. A., Pitchford, M. L., Ashbaugh, L. L., and Eldred, R. A.: Spatial and monthly trends in specified fine particle concentration in the United States: specified fine particle concentration, J. Geophys. Res., 109, D03306, https://doi.org/10.1029/2003JD003739, 2004.

Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.

Mayaux, P., Bartholomé, E., Fritz, S., and Belward, A.: A new land-cover map of Africa for the year 2000: New land-cover map of Africa, J. Biogeogr., 31, 861–877, https://doi.org/10.1111/j.1365-2699.2004.01073.x, 2004.

McCalley, C. K. and Sparks, J. P.: Controls over nitric oxide and ammonia emissions from Mojave Desert soils, Oecologia, 156, 871–881, https://doi.org/10.1007/s00442-008-1031-0, 2008.

Mitani, M., Yamagiwa, J., Oko, R. A., Moutsamboté, J.-M., Yumoto, T., and Maruhashi, T.: Approaches in Density Estimates and Reconstruction of Social Groups in a Western Lowland Gorilla Population in the Ndoki Forest, Northern Congo, Tropics, 2, 219–229, https://doi.org/10.3759/tropics.2.219, 1993.

Nicholson, S. E., Some, B., McCollum, J., Nelkin, E., Klotter, D., Berte, Y., Diallo, B. M., Gaye, I., Kpabeba, G., Ndiaye, O., Noukpozounkou, J. N., Tanu, M. M., Thiam, A., Toure, A. A., and Traore, A. K.: Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part I: Validation of GPCC Rainfall Product and Pre-TRMM Satellite and Blended Products, J. Appl. Meteorol., 42, 1337–1354, https://doi.org/10.1175/1520-0450(2003)042<1337:VOTAOR>2.0.CO;2, 2003.

O'Rourke, P., Smith, S., Mott, A., Ahsan, H., Mcduffie, E., Crippa, M., Klimont, Z., Mcdonald, B., Wang, S., Nicholson, M., Hoesly, R., and Feng, L.: CEDS v_2021_04_21 Gridded emissions data, DataHub [data set], https://doi.org/10.25584/PNNLDATAHUB/1779095, 2021.

Ossohou, M., Galy-Lacaux, C., Yoboué, V., Hickman, J. E., Gardrat, E., Adon, M., Darras, S., Laouali, D., Akpo, A., Ouafao, M.,