Adama Bakayoko
email: bakayokoadma@gmail.com

Corinne Galy-Lacaux
email: corinne.galy-lacaux@aero.obs-mip.fr

Véronique Yoboué

Jonathan E Hickman

Eric Gardrat

Frédéric Julien

Claire Delon

Haywood J M Mercado

Feller R, Moecke E H S Â P Matos

Junior A F, Derner J De Oliveira

Dominant contribution of nitrogen compounds in precipitation 1 chemistry in the Lake Victoria catchment (East Africa) 2

Keywords: Precipitation chemistry, wet deposition, nitrogen wet deposition budget, Lake Victoria 3 basin, Kenya

This work provides a complete chemical characterization of rains collected in the tropical rural site of 14 Mbita (Kenya) on the shores of Lake Victoria (annual rainfall 1259.3 mm). We present a wet nitrogen 15 deposition budget including inorganic and organic dissolved nitrogen in relation with atmospheric 16 sources of gases and particles, precipitation rate and air mass transport. A unique two-year monitoring 17 data set (2017-2019), providing 183 rain samples was collected and analyzed according to 18 international standards (WMO/GAW). Considering that precipitation represents the largest contributor 19 of water to the Lake Victoria (80%), this study gives new insights in the seasonality of nutrients wet 20 deposition inputs in the unique natural resource represented by Lake Victoria and its catchment.

21

Four main contributions to the chemical composition of precipitation, were identified: (1) a 28% 22 terrigenous contribution related to crustal and biomass sources (2) a 14% marine contribution related 23 to Indian ocean air masses intrusion, (3) a 15% organic contribution due to volatile organic carbon 24 emissions from biomass burning and vegetation and (4) a predominant nitrogenous contribution of 25 39% due to livestock and fertilizers, biomass burning and neighboring agricultural fires. Ammonium 26 and nitrate volume weighed mean concentrations are 36.75 and 8.88 μeq L -1 , respectively. Rain in 27 Mbita is alkaline (pH=5.8) highlighting neutralization by heterogeneous chemistry. Total nitrogen wet 28 deposition is 8.54 kgN ha -1 yr -1 , 58,760 tN yr -1 for the entire lake, with 26% attributed to dissolved 29 organic nitrogen. A total atmospheric deposition of 15 kgN ha -1 yr -1 is estimated taking into account 30 dry deposition estimate from literature, showing that the Lake Victoria ecosystem is exposed to 31 eutrophication. An extensive and regular monitoring of wet and dry nitrogen deposition is highly 32

Introduction 6

Determining atmospheric budgets of key chemical compounds is crucial to understand the functioning 7 of ecosystems and biogeochemical cycles. In these budgets, the importance of the chemical 8 composition of wet deposition as a source of nutrients, e.g, nitrogen (N), sulphur (S) or carbon (C) is [START_REF] Zhang | Quantifying Nutrient 347 Budgets for Sustainable Nutrient Management Glob[END_REF]. Sub-Saharan Africa, considered as a "too little" N area, would 23 benefit from a reduction of reactive N pollution to help limited available N sources to go further in 24 supporting food production [START_REF] Sutton | The Nitrogen Fix: From nitrogen cycle pollution to nitrogen 302 circular economy UNEP[END_REF]. The conversion of atmospheric N compounds back to 25 N atmospheric deposition is an important determinant of N availability for ecosystems, but currently, 26 experimental studies conducted on the African continent remain scarce and scattered.

27

Lake Victoria is the second largest freshwater lake in the world with a lake surface area of 68,800 km 2 28 and a total basin area of 195,000 km 2 . Precipitation is the largest contributor of water (80%) to the 29 lake [START_REF] Sutcliffe | Lake Victoria: derivation of a corrected natural water level series / 298 Lac Victoria: dérivation d'une série naturelle corrigée des niveaux d'eau[END_REF]Petersen 2007, Tamatamah et al 2005), leading some authors to describe the lake A c c e p t e d M a n u s c r i p t as "atmosphere controlled" (Kizza et al 2009, Tate et al 2004). The Lake is a great socio-economic 1 resource of the East African Community partner states for fisheries, tourism, transport, water, and 2 energy among others, and a core to East Africa regional integration and development (Nyilitya et al 3 2020). Anthropogenic inputs of N to Lake Victoria through runoff or waste management were 4 described in several studies (Bootsma andHecky 1993, Kayombo andJorgensen 2006, Masso et al 5 2017, Nyilitya et al 2020, [START_REF] Zhou | Regional nitrogen 353 budget of the Lake Victoria Basin, East Africa: syntheses, uncertainties and perspectives 354[END_REF], where the authors also point out that N wet deposition is 6 missing in the budget estimation, due to the lack of available measurements. Indeed, very few studies Assessing wet deposition fluxes and their seasonal variability to Lake Victoria has become important 11 for understanding the bioavailability of nutrients in this ecosystem. Furthermore, assessing the organic 12 part of nitrogen wet deposition with accuracy is a major challenge, while many authors report very 13 few existing data in Africa on this topic (Cape et al 2011, Cornell 2011). We include a particular focus on the N contribution (inorganic and organic) in rain. The climatic and 21 hydrological projections to the lake Victoria basin show an intensification of future annual rainfall by 22 25% in the eastern and 5-10% in the western part of the basin (Olaka et al 2019). In this context, this 23 study will provide (1) a unique quantification of important biogeochemically species wet deposition 24 and (2) a comprehensive atmospheric wet deposition nitrogen budget to the Lake Victoria and its 25 catchment.

26

Sampling site and methods

27

Site description

28

The study site is located at the Centre of Insect Physiology and Ecology (ICIPE) in Mbita (Fig. 1).

29

Mbita is a tropical agricultural area along the shores of Lake Victoria in East Africa. It is located near respectively and 1125 m above mean sea level -MSL (Fig. 1a). Mbita is located at 71 km as the crow 1 flies from the third largest city in Kenya, Kisumu City with a population of 409,928 inhabitants (KNBS 2 2010). The Homa Bay county is divided into eight sub counties including Mbita as one sub county.

3

The total population of the Homa Bay county is 1 131 950 inhabitants with a low density of 307 4 inhabitants per km2 (ADP 2019). Mbita sub county, with 125 000 inhabitants, represents the second 5 lowest contribution of the Homa Bay total population in 2019. Two main relief regions are found, the 6 lakeshore lowlands and the upland plateau. The upland plateau starts at 1219 m above sea level and 7 has an undulating surface resulting from erosion (Fig. 1b). The Kondera natural forest borders Mbita.

8

Mbita is part of the lower midland and its surrounding areas are influenced by biomass burning, mixed Mbita is characterized by a low annual variability of ambient air temperature for the period 1985-2013, 9 with a mean annual value of 24.33 ± 0.80 °C (Fig. 2a) and a mean annual relative humidity of 80.74 10 ± 3.96%. Monthly means of relative humidity do not present any distinct seasonal cycle and vary from 11 76.46 ±13.30 % during the dry season in August to 83.09 ±4.83 % in the wet season in May (Fig. 2a).

12

Wind speed data show a relatively low mean annual value with 2.03 ± 0.14 m.s -1 . Monthly mean close to 2 and corresponds to the rainiest year (1906 mm) (Fig. 2b). The driest year with an index of -10 1.7 is observed in 1990 with 655.7 mm. Details may be found in the supplementary material. From May 2017 to April 2019, the total rainfall amount was 1942 mm and the collected rain samples 21 represent a total of 1814 mm with 189 samples. Table 1 presents the annual total precipitation (Pt) in 22 mm, the percent total precipitation (%TP) and the interannual variability as a percentage relative to 23 the mean annual rainfall for the 1985-2019 period. As defined by [START_REF] Wmo | Manual for the GAW precipitation chemistry programme[END_REF], %TP is the ratio 24 between the annual precipitation (Pt) and the collected precipitation (Pc). The annual and quarterly 25 Percent Coverage Length (%PCL) is also indicated in Table 1. According to the quarterly %PCL, data from April to December in 2017 and from January to April in 14 2019 will only be used to calculate monthly averages of Volume Weighed Mean (VWM) and Wet 15 Deposition (WD). Annual VWM and WD for Mbita will be computed only for year 2018 where the 16 %TP is 94% and the PCL 100% (Table 1). In reference to the WMO international standards, we assume 17 the precipitation collection at Mbita in 2018 to be representative of the studied period according to the 18 parameters calculated in Table 1. The chemical analyses of the samples were performed at the Chemical Laboratory of the Laero, in 22 Toulouse for the major inorganic (Na + , NH4 + , K + , Mg 2+, Ca 2+ , Cl -, NO3 -, SO4 2-, NO2 -) and organic ions 23 (HCOO -, CH3COO -, C2H5COO -, C2O4 2-) using Ionic Chromatography (IC) as described in Galy-24 Lacaux and Modi (1998) The most important ions in Mbita rainwater samples in 2018 are NH4 + and Ca 2+ (Table 2) representing 9 about 52% of total cations VWM concentrations. NO3 -and HCOO -are the most abundant anions 10 representing 38% of total anions VWM concentrations. Concentration of cations follow a general

11 pattern NH4 + > Ca 2+ > Na + > K + >Mg 2+ , while concentrations of anions follow a general pattern NO3 - 12 > HCOO ->Cl -> SO4 2-> CH3COO -> HCO3 -> C2O4 2-> C2H5COO -> NO2 -.

Marine and terrigenous contribution 1 2

The Cl -/ Na + ratio at Mbita (1.06) is close to the seawater ratio (1.16). The high correlation between 3 Na + and Cl -(r=0.99) associated to the Enrichment Factor (EF, defined in Sup. Mat.) of Cl -(<1) 4 indicates that both compounds have a marine origin (Cao et al 2009). Lower correlations are found 5 between Na + and K + , Mg 2+ and SO4 2-(r = 0.40, 0.29 and 0.33 respectively), suggesting a non-marine 6 additional origin for these species. Sea Salt Fractions (SSF, for Ca 2+ , Mg 2+ , K + and SO4 2-represent 7 2%, 50%, 3% and 12% respectively.

24

The authors indicate that 26% of the aerosols in Mbita originate from marine sources.

25

The Ca 2+ contribution is the second most important after NH4 + (Table2). Ca 2+ is significantly 26 correlated to K + , Mg 2+ and SO4 2-(r = 0.80, r = 0.97, r = 0.88) emphasizing the contribution of potential 27 crustal (terrigenous) and biomass burning sources. Non-Sea Salt Fraction (nSSF) contributions for 28 Ca 2+ , Mg 2+ , K + , SO4 2-are 98%, 50%, 97%, 88% respectively. Monthly VWM evolution of Ca 2+ , Mg 2+ , 29 K+, SO4 2-concentrations over the period 2017-2019 exhibit two peaks in JJA and in January (Fig. 3a).

30

The analysis of back trajectories arriving at Mbita on 5 January 2018 0700 UTC (Fig. 4c,4d) from 31 850 to 750 hPa pressure levels confirms that the air is of continental origin owing to a northerly flow Equator lead to a cross-equatorial flow which brings relatively dry and dusty air to Lake Victoria. The 3 airflow arriving at Mbita at 800 and 750 hPa is relatively strong as it took about 24 h to cover 500 km 4 at 5.8 m s -1 (Fig. 4d). The origin of air at 850 hPa is much closer as it stays very close to the surface 5 level. Ca 2+ is usually used as a reference element for continental crust and considered as a typical In addition to this terrigenous contribution, Ca 2+ , Mg 2+ , K + , SO4 2-particles can also be emitted to the 6 atmosphere by biomass burning; furthermore, K + in the sub-micron mode is considered as an terrigenous and biomass burning tracers in JJA (Fig. 3). The annual marine contribution to the total ionic precipitation content was calculated with Sea Salt 9 Fraction VWM concentrations as:

([Na + ]+ SSF [K + ]+ SSF [Ca 2+ ]+ SSF [Mg 2+ ]+ [Cl -]+ SSF [SO4 2-]) / 10
Total VWM ionic concentrations. The terrigenous/biomass burning contribution to the total ionic 11 precipitation content was calculated with Non-Sea Salt Fraction (nSSF) VWM concentrations as:

12 (nSSF [K + ]+ nSSF [Ca 2+ ]+ nSSF[Mg 2+ ]+ nSSF [SO4 2-]) / Total VWM ionic concentrations. For 2018, 13
the marine and terrigenous/biomass burning contribution to the rain chemical composition at Mbita 14 was 14% and 28% respectively (Fig. 6).This contribution is comparable to the South African dry August 2018 leads to large WD for all compounds (Fig. 3). The main potential acidifying components of rains are SO4 2-and NO3 -for mineral acidity and HCOO - 27 , CH3COO -, C2O4 2-and C2H5COO -for organic acidity (Table 3). These concentrations reflect potential Akpo et al. (2015) Tropical rural site-Mbita Kenya (2018) This study* Dry savanna-Banizoumbou Niger (1994-2009) Laouali et al. (2012) Dry savanna-Louis Trichardt S. Africa (2009-2014)* Conradie et al. (2016) Page 16 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1

A c c e p t e d M a n u s c r i p t C2H5COO -. Organic and mineral acids represent 50.63% and 49.37% of the potential acidity, 1 respectively (Table 3). Acetic and formic acids are generally derived from vegetation, biomass burning The pH of the rainwater at Mbita for the year 2018 varies between 4.5 and 7.5 with an annual average 10 value of 5.8 ± 0.6 (Fig. 7). As a comparison, Visser (1961) observed a median pH of 7.9 near Kampala 11 (Lake Victoria, Kenya side), [START_REF] Rodhe | Chemical composition of precipitation in East Africa. 280 Kenya[END_REF] reported a median of 6.1 for nine East African sites.

12

The pH distribution shows that 50.6% of the analyzed rains are alkaline, 35.3% acid and 14.1% neutral 13 compared to a pH of 5.6 considered as neutral for rain (Fig. 7). [START_REF] Yoboué | Rainwater Chemistry and Wet Deposition 339 over the Wet Savanna Ecosystem of Lamto (Côte d'Ivoire)[END_REF] The pH value alone is not sufficient to give information about acidity of rain water. Reactions between 4 acidic and alkaline species in rainwater determine the final rainwater pH values. Using potential 5 acidity (Sup. Mat. Eq. 1) and fractional acidity (Sup. Mat. Eq. 2), 87% of potential rain acidity (27.8 6 μeq L -1 ) is found to be neutralized by alkaline ions (Table 3 The mean and standard deviation (±SD) of DIN concentrations (NH4 + + NO3 -+ NO2 -) in Mbita is 18 1.41±1.6 mgN.L -1 . In comparison to older studies in the same region, our results show higher values.

19 Indeed, data from the literature give DIN concentration of 0.87 mg.L -1 at Kampala (Uganda, Lake 2

The sum of NH4 + , NO3 -and NO2 -VWM concentrations represents 39% of the total chemical content 3 of rain, in contrast to other remote African sites where the values ranged from 17 to 23% (Fig. 6).

4

Ammonium content in precipitation results from the inclusion of gaseous ammonia and particles. The mean and standard deviation (range) of WD fluxes for NH4 + -N, NO3 --N, and DON, are 0.42±0.28 1 (0.01-0.96), 0.10±0.06 (0.003-0.23), and 0.19±0.21 (0.001-0.92) kgN ha -1 month -1 respectively. On a 2 monthly time, scale, maximum DIN and DON deposition fluxes are observed during wet seasons (Fig. 3 9b). DON contribution varies from 4 to 58% of TDN, NH4 + -N contribution from 30 to 78%, and NO3 -

4

-N from 8 to 27%. DIN contribution at the monthly scale remains larger during the whole period, with The annual WD fluxes for NH4 + -N, NO3 --N, and DON are 5.06, 1.25 and 2.32 kgN ha -1 yr -1 (Table2, 13 Table 4). Annual DIN WD represents 6.31 kgN ha -1 yr -1 , with long (MAM) and short (ON) rain seasons (1972) in Brazil;Eklund et al (1997) in Costa Rica, Table 4). On the 23 other hand, DIN WD fluxes from our study are two times lower than those estimated from annual 24 rainfall and VWM given in Visser (1961) in Lake Victoria, and larger than those estimated from 25 Bootsma et al (1996) and Bootsma and Hecky (1993) in Lake Malawi. These differences may be 26 attributed to collection protocols and analysis techniques (colorimetric or spectroscopic) of the 27 samples as well as the time scales which differ from one study to another. Our study was carried out 28 over a much longer sampling period (2 years), continuously (at the rain event), thus giving a more 29 representative average characterization of the total N WD flux than previously found in older studies.

30

The sampling method of the cited authors was either in bulk, weekly or monthly, sometimes even Wet N deposition (DIN+DON) in Mbita gives a WD flux of 8.54 kgN ha -1 yr -1 . From Kayombo and 7 Jorgensen ( 2006) we estimated a 38% contribution of dry deposition to the atmospheric (wet+dry) 8 deposition in Lake Victoria. With this value, a total atmospheric deposition (wet+dry) of 15 kgN ha -1 9 yr -1 may be estimated from our results, to be compared to 12.43 kgN ha -1 yr -1 in Lake Malawi and to 10 values ranging from 7.5 to 19 kgN ha -1 yr -1 reported by [START_REF] Scheren | Estimation of water pollution sources in 284 Lake Victoria, East Africa: Application and elaboration of the rapid assessment methodology 285[END_REF] for remote non marine 11 tropical watersheds. These values exceed the 10 kgN ha -1 yr -1 threshold given by Bobbink et al (2010) 12 above which African ecosystems will be subject to stress due to N deposition excess. According to the 13 global distribution map of mean critical loads for eutrophication (Bouwman et al 2002), the Lake 14 Victoria ecosystem is exposed to eutrophication.

15

From DIN WD fluxes reported in Table 4 and with the surface of African great lakes, we estimated 16 the total DIN input in lakes Kivu (2, 400 km 2 ), Tanganyika (32, 600 km 2 ) and Victoria (68, 800 km 2 ), 17 with 2, 176 tN yr -1 , 20, 000 tN yr -1 and 44, 032 tN yr -1 respectively. Taking into account the total N 18 WD flux from this study, including DON estimation, Lake Victoria receives 58, 760 tN yr -1 . According 19 to [START_REF] Zhou | Regional nitrogen 353 budget of the Lake Victoria Basin, East Africa: syntheses, uncertainties and perspectives 354[END_REF], Nile river and fishery N export from Lake Victoria represent 40, 000 tN yr -1 and 20 4, 000 tN yr -1 respectively, leading to 14, 400 tN yr -1 staying in the lake and contributing to This study presents for the first time a complete characterization of the precipitation chemistry in a 2 tropical rural site in Kenya on the shores of the Lake Victoria, based on the monitoring of quality 3 controlled in situ measurements from May 2017 to April 2019. Wet deposition fluxes of major ionic 4 species considered as important nutrient inputs to ecosystems were characterized and quantified at the 5 annual and monthly scales. We emphasize that wet deposition is influenced by the variation in 6 emission source strength, precipitation rate and the origin of air masses. In the Lake Victoria basin, 7 four main contributions to the chemical composition of precipitation were related to potential 8 atmospheric sources of gases and particles of importance. Marine species, with a contribution of 14%, 9 were clearly related to marine air masses coming from the Indian Ocean. The organic contribution in 10 precipitation is estimated for the first time on the shores of the lake Victoria and represents about 15%, 11 due to VOC from biomass burning combustions from both the southern and northern hemisphere, and 12 by BVOC from vegetation (forests in the vicinity of the site). Terrigenous species were attributed to 13 the crustal and biomass burning sources contributing 28% of the total rain chemical content, due to 14 biomass burning sources in both hemispheres and dust emissions from arid areas in the North and East 15 of Kenya. The most important result is the nitrogen species' largest contribution to the total chemical 16 content of the rains (39%), with the predominance of NH4 + ion in precipitation due to local sources 17 such as livestock and fertilizers, as well as regional and continental biomass burning and nearby 18 agricultural fires. This means that nitrogen deposition to Lake Victoria is strongly influenced by 19 anthropogenic sources.

20

This study gives unprecedented insights in the seasonality of N wet deposition inputs. DON 21 contribution to the high N load in rains is 26% of the total WD flux, highlighting the crucial need to 22 quantify organic N in rains in tropical ecosystems. With a total N atmospheric input to Lake Victoria 23 of 103, 200 tN yr -1 estimated thanks to our results and literature results, and according to critical loads 24 for N eutrophication, we assume that the Lake Victoria ecosystem is exposed to eutrophication.

25

In a near future, this study on wet N deposition on the Lake Victoria basin will be complemented by 26 a quantification of N dry deposition fluxes. We highly recommend extensive and regular monitoring 27 of atmospheric deposition including wet and dry processes over seasonal representative temporal 28 periods in Kenya, Tanzania and Uganda both in-shore and off-shore to provide guidance and improve 29 the efficiency of nitrogen use in agricultural areas, and reduce losses to the Lake Victoria environment.

30

This could be done through the organization of awareness-raising campaigns toward end users. In 31 order to reduce nitrogen pollution of the lake, it will be necessary in the future to improve agricultural

9

  widely recognized(Duce et al 2009), and shows a high spatial and temporal variation. This 10 composition reflects various interacting physical and chemical mechanisms in the atmosphere 11 including biogenic and anthropogenic sources of pollutants, atmospheric transport and chemical 12 transformation processes as well as removal processes (Galy-Lacaux et al 2009). The comprehensive 13 global assessment on precipitation chemistry and biogeochemically important trace species deposition 14 by Vet et al (2014) emphasized that the African continent is under-sampled and lacks quality 15 controlled measurements. 16 80% of African countries are affected by soil nitrogen deficiency because of inadequate use of 17 fertilizers, and insufficient quantity and poor quality of organic inputs (Masso et al 2017). In Africa it 18 is expected that nitrogen (N) deposition to ecosystems will increase by 50% until 2100 (Lamarque et 19 al 2013), especially through wet deposition, due to increasing demography and changes in climate, 20 land uses and atmospheric concentrations. The impact of nitrogen management and nitrogen 21 deposition in Africa has become a major societal challenge related to food security, global change and 22 biodiversity loss

7

  on precipitation chemistry and wet deposition of nutrients in Great Lakes exist (Lakes Victoria, 8 Malawi, Tanganyika, Kivu) and give an incomplete view of the chemical content of rain (Bootsma et 9 al 1996, Gao et al 2018, Vuai et al 2013), highlighting the necessity to accurately quantify this input.

  10

14

  This study is part of the East African demonstration area of the Global Environmental Facility -United 15 Nations Environmental Program (GEF-UNEP) International Nitrogen Management System (INMS) 16 project, and of the French program Cycle de l'Azote entre la Surface et l'Atmosphere en afriQUE 17 (CASAQUE). This work is based on a two year monitoring period (May 2017 -April 2019) of wet 18 deposition on the shore of the Lake Victoria to assess the chemical composition of rain in the area, as 19 well as the sources of chemical compound emissions influencing the precipitation chemistry content.

20

 20

30the

  Southwestern border of Kenya and Uganda at the latitude and longitude of 0.44˚S, 34.18˚E 31 Page 3 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t

9

  and anthropogenic sources, and dust aerosols(Boiyo et al 2018b, 2017b, Makokha and Angeyo 2013). 10 In addition to local sources, the long-range transport of aerosols towards the site could significantly 11 influence the aerosol load (Boiyo et al 2017a, Gatebe et al 2001). Agriculture plays a crucial role to 12 food and nutrition security in Homa bay county. Most of the income of Homa bay county is derived 13 from crop, livestock and fisheries activities but 50% of the population is food insecure (GoK 2013). 14 Mbita is charasteristic of a mixed crop site with maize, sorghum, beans, millet and agroforestry 15 systems (GoK 2014). Large scale farms are found in Mbita sub county where large stocks of livestock 16 are kept (zebu cattle, red massai sheep, goat and chicken) (MoALF 2016). In Mbita and on the borders 17 of Lake Victoria, the density of cattle and small ruminants represents more than 100 cattle per square 18 kilometer (FAO 2005).

Figure 1 :

 1 Figure 1: (a) Map of the study area on the African Continent (shown top left in the insert). Location of the site of 23 Mbita (red circle). Blue labeling represents the major African lakes and ocean. (b) Topographic map within the study 24 domain. (c) Mean precipitation at Mbita according to seasonal climatology for the period 1985-2019 with March 25

  8

  13calculations show higher values in the main wet season with a maximum in May (2.30 ± 0.23 m s -1 ) 14 and a minimum in the dry season in February (1.83 ± 0.09 m s -1 ). These results are similar to the ones 15 presented in other studies in East Africa (Boiyo et al 2017b, Ogwang et al 2015, Yang et al 2015).

Figure 2 :5

 2 Figure 2: Monthly mean meteorological parameters measured at Mbita from 1985 to 2013. (a) Rain depth 2 (mm) from 1985 to 2019 and ambient air temperature (°C) from 1985 to 2013, (b) air relative humidity (%) 3 from 1999 to 2013; (c) Standardized Precipitation Index (SPI) over the period 1985-2019. 4 5

  Mbita were collected using an automatic precipitation collector designed for the 14 INDAAF (International Network to study Deposition and Atmospheric composition in AFrica) 15 network (see details Sup. Mat. section sample collection protocol). At the site, a local operator 16 collected each rain event in 50 mL greiner tube immediately stored at ICIPE center in a deep freezer 17 (-18°C). During the day, samples were collected just after each rainfall and when rain occurred at 18 night, sampling wass done between 6 and 8 AM. After collection, samples were sent back for analysis 19 to the Laboratoire d'Aérologie (Laero, Toulouse, France) in the strict respect of the cold chain.

20

 20

1

  Back trajectories initiated at Mbita (0.44˚S, 34.18˚E, 1125 m MSL equivalent to 890 hPa pressure 2 level) were used to identify the origin of air masses, for three initial pressure levels at 850 hPa (about 3 1550 m altitude), 800 hPa (2065 m), and 750 hPa (2610 m), using ERA-5 climate reanalysis from 4 ECMWF (Hersbach et al 2020).

Figure 3 :

 3 Figure 3: (a) Monthly Volume Weighed Mean (VWM) concentrations measured in precipitation at Mbita and (b) monthly Wet Deposition fluxes in kg ha -1 month -1 for each species from May 2017 to April 2019. Ht stands for rain depth.

8

  Monthly variations of ionic VWM concentrations indicate the predominance of NH4 + for all months 9 (Fig.3a), except in July, August and September (JAS) 2017 where the marine ions Na + and Cl -are the 10 most abundant. Indeed, during the rain event of 15 July, very high contents of Na + (285.6 μeq L -1 ) and 11 of Cl -(293.8 μeq L -1 ) were measured. The analysis of back trajectories arriving in Mbita on 15 July 12 2017 1900 UTC (Fig.4a, 4b) from 850 to 750 hPa pressure levels confirms that the air is of oceanic 13 origin owing to a southeasterly flow which crosses the central Kenyan highlands and the Rift valley 14 before arriving over the Lake Victoria plateau. The duration of the trip from the Indian Ocean to Mbita 15 is about 3.5 days for the air observed at 800 and 750 hPa at Mbita. The same course took 5.5 days for 16 the 850 hPa level with strong ascent from 1500 to 4500 m altitudes on 11 July 2017 probably in relation 17 with cloud formation on the high terrain slopes, leading to rainfall at Mbita strongly loaded in Na + and 18 Cl -. From July to September 2017, 7 rain events of this type were registered leading to maximum 19 monthly VWM concentrations from 65 to 96 μeq L -1 (Fig. 3a), clearly originating from the Indian 20 Ocean (figures not shown) whereas the majority of rain events over the period 2017-2019 show 21 concentrations ten times lower (1 to 50 μeq L -1 ). The analysis of monthly Aerosol Optical Depth 22 (AOD) measurements by Boiyo et al (2019, 2018b) obtained at Mbita ICIPE from 2007 to 2015 23 confirms this marine influence with a peak in June, July, August (JJA) associated to marine aerosols.

32

  Page 12 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t which crosses South Sudan and Uganda before arriving over the Lake Victoria plateau. During the 1 southern hemisphere summer, high temperatures at surface and in the low atmosphere south of the 2

6

  lithospheric element (Ding et al 2013). The positive relationship between K + , Mg 2+ , SO4 2-and Ca 2+ 7 confirms the importance of the particles coming from arid and semiarid regions in the chemical 8 composition of rainfall. The North African desert areas (Sahel and Sahara) as well as the Arabian 9 Peninsula are probably the most important mineral aerosol sources (Jish Prakash et al 2015, Kaufman 10 2005). Boiyo et al (2019, 2018b, 2018a) show that long-range transport of aerosols from Saharan and 11 Arabian desert enhances dust atmospheric loading during the local dry season in Mbita. Due to the 12 partial dissolution of soil dust terrigeneous components, rain in the tropical rural site of Mbita is loaded 13 with dissolved calcium and carbonates (calcite). In addition to calcite, dust contains dolomite and 14 gypsum, which may explain the enrichment of Mg 2+ , SO4 2-and K + (Avila et al 1997). Terrigenous 15 contributions were also found in other African ecosystems and regions of the world influenced by arid 16 areas (Celle-Jeanton et al 2009, Desboeufs et al 2010, Galy-Lacaux et al 2009, Kulshrestha et al 2009, 17 Laouali et al 2012).

Figure 4 :

 4 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t 1 Back trajectories initiated in Mbita on the horizontal (left side) and vertical (right side) plans (a,b):15 2 July 2017 1900 UTC and (c,d) 5 January 2018 0700 UTC for 850 hPa (red line), 800 hPa (green line) and 750 3 hPa (blue line). 4 5

7

  atmospheric tracer of biomass burning (Andreae et al 1983, de Mello 2001) and SO4 2-as a tracer of 8 ammonium sulfate aerosols (Malavelle et al 2019). Maximum monthly VWM K + concentrations (10 9 to 16 μeq L -1 ), twice as large as the annual VWM (5.57 μeq L -1 ), are measured in JJA 2018. The 10 analysis of the mean monthly annual cycle of burned area from 2017 to 2019 using GFED4s fire 11 emissions shows two periods of strong influence of biomass burning in the region: one in December, 12 January, February (DJF) coming from the Northern part of Africa (South Sudan, Central African 13 Republic and Democratic Republic of Congo) and one in JJA coming from the southern part of Africa 14 (Tanzania and south of the Democratic Republic of Congo) (Fig. 5). These results are consistent with 15 annual African biomass burning temporal dynamics described by Roberts et al (2009). In Mbita, Boiyo 16 a b c d Page 14 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t et al (2018a) showed that high AOD in JJA was attributed to a combination of fine and coarse mode 1 particles emphasizing the combined sources of dust and biomass burning. The fine mode of biomass 2 burning in JJA is two times larger than in DJF, and in DJF coarse mode dust aerosols are predominant 3 in mixture with fine combustion aerosols. Fig. 5 confirms the influence of biomass burning in the two 4 seasons with a closer vicinity of burned surface in the south of Mbita in JJA and in the north in DJF. 5 Mbita aerosols from biomass burning are related to biomass burning at the continental scale but also 6 to neighboring agricultural zones and industrial-vehicular emissions due to the proximity of urbanized 7 regions and roads (Boiyo et al 2017b, Makokha and Angeyo 2013). The main source contributing the 8 most to aerosols from biomass burning are local activities linked to agriculture. Indeed, every year, 9 bush fires are lit by farmers in different parts of the lake basin in order to prepare the land for 10 cultivation (Makokha and Angeyo, 2013; Boiyo et al., 2017b). During March, April, May (MAM) and 11 DJF, Mbita experiences significant local activities (land clearing and biomass burning) (Boiyo et al 12 2017b). The mixed type of aerosols combining marine and terrigenous contributions, combustion 13 aerosols from biomass burning and anthropogenic activities is present in all seasons at Mbita, with the 14 highest contribution in JJA as confirmed by maximum monthly VWM concentrations of marine,15

Figure 5 :Figure 6 :

 56 Figure 5 : Monthly mean burned area using GFED4s fire emissions from January 2017 to May 2019. The site 18 of Mbita is marked as a blue star. 19

  15 savanna (Conradie et al 2016) influenced by combustion and crustal sources (31%) but lower than in 16 the Sahelian dry savanna (Laouali et al 2012) and the Benin wet savanna (Akpo et al 2015) where it 17 contributes to 45 to 51% (Fig. 6). 18 WD fluxes are modulated by the monthly precipitation amounts and maximum monthly WD were 19 recorded in MAM in the core of the 2018 wet season (Fig. 3b). Marine species maximum monthly 20 WD were measured from June to October 2017 with a combination of high VWM and high unexpected 21 rain depth at this period. Biomass burning and terrigenous influence in DJF is more important in 2019 22 than in 2018 due to larger rain amounts. The combination of large concentrations and high rain in

  23

  28 contributions to the acidity in Mbita rains of NO3 -(27.72%), HCOO -(26.56%), SO4 2-(21.66%) , 29 CH3COO -(17.83%), C2O4 2-(5.48%) and C2H5COO -(0.77%), and a negligible contribution of 30

2

  and bio fuel, fossil fuel, agricultural emissions and soils(Paulot et al 2011). Wet deposition is one of 3 the major sinks of organic acids. Monthly VWM concentrations of organic species in rain exhibit 4 peaks in JF and JAS. Mbita rain presents a total VWM organic acids concentration of 16.22 µeq L -1 5 representing 15% of the total ionic content in rain. This result is comparable to values found in the 6 wet savanna of Djougou in Benin (Akpo et al 2015) and in the South African dry savanna (14-16%,7Conradie et al 2016), but higher than in Sahelian dry savanna of Niger (5%, Laouali et al 2012) (Fig.

  9

Figure 7 :

 7 Figure 7: Frequency distribution of pH measured in Mbita rainwater in 2018. 2 3

17

  chemistry processes of neutralization between alkaline dust particles, gaseous nitric and sulfuric acid 18 and organic ions.Galy-Lacaux et al (2001) showed that this capture could be completed and could 19 Page 18 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t explain to a large extent the neutralization processes of rain's potential acidity. This result has been 1 widely emphasized in other parts of the world where desert soil dust emissions influence rain 2 chemistry, as for example in Spain or in the Mediterranean basin (Avila et al 1998, 1997, Herut et al 3 2000), and in Asia(Hu et al 2003, Kulshrestha et al 2005[START_REF] Sigha-Nkamdjou | Rainwater 290 Chemistry and Wet Deposition over the Equatorial Forested Ecosystem of Zoétélé (Cameroon) 291[END_REF].

20

  Victoria shore,(Visser 1961), 0.09 to 0.13 mg.L -1 on the shore of Lake Malawi (Bootsma and Hecky 21 Page 19 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t 1999), from 0.012 to 4.2 mgN L -1 at Lake Victoria sites in Tanzania (Vuai et al 2013), and 0.92 mgN 1 L -1 on Lake Tanganyika (Gao et al 2018).

5Figure 8 :(

 8 Figure 8: (a) Monthly mean OMI tropospheric NO2 from January 2017 to May 2019 and (b) IASI tropospheric 2 NH3 Vertical Column Densities (VCDs) in 10 15 molecules cm -2 from January 2018 to May 2019. The site of 3 Mbita is marked as a blue star. 4 5 6 7

Figure 9 :

 9 Figure 9: (a) Monthly concentrations in mg L -1 of Dissolved Organic Nitrogen (DON) and Dissolved Inorganic Nitrogen (DIN) in the forms of N-NO3 - and N-NH4 + (b) Wet deposition Nitrogen budget at Mbita over the period 2017-2019. Dotted blue line is monthly rain depth (Ht, mm) at Mbita over the period 2017-2019.

5a

  large predominance of NH4 + -N wet deposition fluxes, except during the special period around July 6 2017 where DON contribution exceeds DIN contribution, due to the high unexpected marine 7 influence. Maximum DIN WD fluxes (> 1 kgN ha -1 month -1 ) are observed at the beginning of the long 8 rain season in March and April 2018, and in December 2018, because of strong rains and large DIN 9 concentrations. Gao et al (2018) also found large WD DIN fluxes (2.37 kgN ha -1 month -1 ) into Lake 10 Tanganyika at the beginning of the rain season, attributed to soil preparation by farmers (stubble 11 burning) before short and intense rains.

  12

14

  accounting for 36 and 21% respectively, while rainfall amounts for the same seasons represent 47 and 15 22% respectively. 16 Annual DIN WD fluxes in Mbita are comparable to WD measured in other sites of Lake Victoria by 17 Vuai et al (2013), and in other East African great lakes, such as Lake Tanganyika (Gao et al 2018, 18 Langenberg et al 2003), and Lake Kivu ((Namugize 2015), Table 4). The predominance of N-NH4 + in 19 DIN WD fluxes reported by Gao et al (2018) is in accordance with our results. Similar DIN WD fluxes 20 were also measured in West and Central Africa (Galy-Lacaux and Delon 2014) and in industrialized 21 south African dry savannas (Conradie et al 2016). In other tropical regions DIN WD fluxes show 22 comparable values (Amazoniana

31

  Page 24 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1A c c e p t e d M a n u s c r i p t seasonally and for shorter periods than the ones reported in this work. Local climatic conditions and 1 air mass movements could also explain this difference. Furthermore, few DON estimations are 2 available in the literature for tropical ecosystems, and are often inferred from bulk deposition, as given 3 by Van Langenhove et al (2020) for a tropical forest in French Guiana with 8.75 kgN ha -1 yr -1 (Table44), or inBauters et al (2018) reporting DIN and DON contributions in the Congo forest (5.5 and 12.7 5 kgN ha -1 yr -1 respectively).

  6

37F,

  Makelele I, Cizungu Ntaboba L, Spencer R G M and Boeckx P 2018 High fire-derived 38 nitrogen deposition on central African forests Proc. Natl. Acad. Sci. 115 549-54 39 Behera S N and Sharma M 2010 Investigating the potential role of ammonia in ion chemistry of fine 40 particulate matter formation for an urban environment Sci. Total Environ. 408 3569-75 41 Bobbink R, HICKS K, GALLOWAY J, SPRANGER T, ALKEMADE R, ASHMORE M, 42 BUSTAMANTE M, CINDERBY S, DAVIDSON E, DENTENER F, EMMETT B, 43 ERISMAN J-W, FENN M, GILLIAM F, NORDIN A, PARDO L and DE VRIES W 2010 44 Page 29 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t Bouwman A F, Van Vuuren D P, Derwent R G and Poch M 2002 A GLOBAL ANALYSIS OF 71 ACIDIFICATION AND EUTROPHICATION OF TERRESTRIAL ECOSYSTEMS 349-82 72 Brocard D C, Lacaux J P and Yoboué V 1996 Emissions from the combustion of biofuels in western 73 Africa 350-60 74 Calderón S M, Poor N D and Campbell S W 2006 Investigation of the Ultraviolet Photolysis Method 75 for the Determination of Organic Nitrogen in Aerosol Samples J. Air Waste Manag. Assoc. Z, Wang S, Zhang G, Luo J and Lu S 2009 Chemical characteristics of wet precipitation at an 78 urban site of Guangzhou, South China Atmospheric Res. 94 462-9 79 Cape J N, Cornell S E, Jickells T D and Nemitz E 2011 Organic nitrogen in the atmosphere -Where 80 does it come from? A review of sources and methods Atmospheric Res. 102 30-48 81 Cape J N, Tang Y S, González-Benítez J, Mitošinková M, Makkonen U, Jocher M and Stolk A 2012 82 Organic nitrogen in precipitation across Europe Biogeosciences Discuss. 9 8093-109 83 Celle-Jeanton H, Travi Y, Loÿe-Pilot M-D, Huneau F and Bertrand G 2009 Rainwater chemistry at a 84 Mediterranean inland station (Avignon, France): Local contribution versus long-range supply 85 Atmospheric Res. 91 118-26 86 Chan M N, Choi M Y, Ng N L and Chan C K 2005 Hygroscopicity of water-soluble organic 87 compounds in atmospheric aerosols: amino acids and biomass burning derived organic species. 88 Environ. Sci. Technol. 1555-1562. 89 Chao C Y and Wong K K 2002 Residential indoor PM10 and PM2.5 in Hong Kong and the elemental 90 composition Atmos. Environ. 36 265-77 91 Cheng Y, Liu Y, Huo M, Sun Q, Wang H, Chen Z and Bai Y 2011 Chemical characteristics of 92 precipitation at Nanping Mangdang Mountain in eastern China during spring J. Environ. Sci. 2004 PM2.5 episodes as observed in the speciation trends network Atmos. Environ. 38 5237-Ogwang B A, Chen H, Tan G, Ongoma V and Ntwali D 2015 Diagnosis of East African climate and 251 the circulation mechanisms associated with extreme wet and dry events: a study based on 252 RegCM4 Arab. J. Geosci. 8 10255-65 253 Okech E, Kitaka N, Oduor S and Verschuren D 2018 Trophic state and nutrient limitation in Lake 254 Baringo, Kenya Afr. J. Aquat. Sci. 43 169-73 255 Olaka L A, Ogutu J O, Said M Y and Oludhe C 2019 Projected Climatic and Hydrologic Changes to 256 Lake Victoria Basin Rivers under Three RCP Emission Scenarios for 2015-2100 and Impacts 257 on the Water Sector Water 11 1449 258 Ossohou M, Galy-Lacaux C, Yoboué V, Hickman J E, Gardrat E, Adon M, Darras S, Laouali D, Akpo 259 A, Ouafo M, Diop B and Opepa C 2019 Trends and seasonal variability of atmospheric NO2 260 and HNO3 concentrations across three major African biomes inferred from long-term series of 261 ground-based and satellite measurements Atmos. Environ. 207 148-66 262 Paulot F, Wunch D, Crounse J D, Toon G C, Millet D B, DeCarlo P F, Vigouroux C, Deutscher N M, 263 González Abad G, Notholt J, Warneke T, Hannigan J W, Warneke C, de Gouw J A, Dunlea E 264 J, De Mazière M, Griffith D W T, Bernath P, Jimenez J L and Wennberg P O 2011 Importance 265 of secondary sources in the atmospheric budgets of formic and acetic acids Atmospheric Chem. 266 Phys. 11 1989-2013 267 Pöschl U 2005 Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects 268 Angew. Chem. Int. Ed. 44 7520-40 269 Potter S, Randerson T, Field B, Matson A, Vitousek P M, Mooney H A and Klooster S A 1993 270 Terrestrial ecosystem production: A process model based on global satellite and surface data 271 Glob. Biogeochem. Cycles 7 811-41 272 Randerson J T, Chen Y, Van Der Werf G R, Rogers B M and Morton D C 2012 Global burned area 273 and biomass burning emissions from small fires J. Geophys. Res. G Biogeosciences 117 274 G04012 275 Rastogi N and Sarin M 2005 Chemical characteristics of individual rain events from a semi-arid region 276 in India: Three-year study Atmos. Environ. 39 3313-23 277 Page 38 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 A c c e p t e d M a n u s c r i p t

Table 1 :

 1 Rainwater

	1	collection at Mbita (2017-2019): Annual Total Precipitation (Pt, mm), Interannual
	2	variability (%), Collected precipitation (Pc, mm) and Number of collected rain event (Nc), Percent total
	3	precipitation (%TP), Annual percent coverage length (%PCL) and in brackets: %PCL for each quarter (0 and 1
	4	means 0% and 100% respectively).
		5
		6
		7
		8
		9
		10
		11
		12
	13	* %TP only concerns four months of collection (January-April)

and at the Laboratoire d'Ecologie fonctionnelle et environnement (Toulouse,

			AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1	Page 8 of 42
		Year	2017	2018	2019
		Pt (mm	930.95	1070.7	1004
		Inter annual Variability (%)	-26%	-15%	-20%
		Pc (mm) and Nc	592.25 (70)	1004.2 (94)	217.5 (25)
		% TP (%)	64	94	22*
		% PCL Annually	75	100	25
		% (quarterly)	(0111)	(1111)	(1000)
	25
	26	France) for TDN (For detailed procedures, see Sup. Mat.).
	27
	28	2.5 Satellite data
	29	Observations of atmospheric NH3 (ANNI-NNv3) from the Atmospheric Infrared Sounding
	30	Interferometer (IASI) on board the European Space Agency's Metop-A were analyzed, providing near-
	31	daily global coverage. As for NO2, NASA's standard IMO NO2 v3 tropospheric column density
	32	product, on board NASA's Aura satellite, was used. (For more details, see Sup. Mat.).
	33

Table 2 :

 2 Annual Volume Weighted Mean (VWM) (µeq L -1 ) and Wet Deposition (WD) (kg ha -1 yr -1 ) in 2018. 1

	Species	VWM	WD
	H +	4.21	0.05
	Na +	6.62	1.63
	NH4 +	36.75	5,51*
	K +	5.57	2.33
	Ca 2+	15.22	3.30
	Mg 2+	3.01	0.40
	NO3 -	8.88	1.33*
	Cl -	7.02	2.66
	SO4 2-	6.94	1.20*
	NO2 -	0.07	0,01*
	HCOO -	8.79 (8.51)	(4.10)
	CH3COO -	6.98 (5.71)	(3.61)
	C2H5COO -	0.32 (0.25)	(0.19)

  ). Analyses of Neutralization Factors (Sup.Mat. Eq. 3) suggest that NH4 + and Ca 2+ are responsible for most of the acid neutralization with NF

	8
		values for NH 4
	11
	12	by the Ammonium availability Index (AAI, Sup. Mat. Eq. 4) of 161 % in 2018, indicating that
	13	ammonium is sufficient to completely neutralize the sulfuric and nitric acids in Mbita rainwater
	14	(Behera and Sharma 2010). Ratios between cations and anions (Ca 2+ +Mg 2+ +NH4 + )/(NO3 -+SO4 2-) =
	15	3.48 and (NO3 -+SO4 2-)/(Ca 2+ +Mg 2+ ) = 0.87 reflect the alkaline nature of rainwater and the influence
	16	of Ca 2+ and Mg 2+ compounds described earlier. In general, acidity was not correlated with mineral
		acids, and weakly correlated with organic acids. These results confirm the presence of heterogeneous

7 + , Ca 2+ of 1.32 and 0.55 respectively. The large NH4 + / NO3 -and NH4 + /SO4 2-ratios (4.14 9 and 5.19) suggest that nitrogenous species are completely neutralized in the atmosphere as a result of 10 the formation of NH4NO3 and (NH4)2SO4 aerosols

(Duan et al 2003, Seinfeld 1986)

. This is confirmed

Table 3 :

 3 Analysis of potential contributions of mineral and organic acids to total acidity in Mbita rain in 2018 6 7

	Organic Acidity	VWM (µeqL -1 ) Mineral Acidity	VWM (µeqL -1 )
	HCOO -	8.51	NO3 -	8.88
	CH3COO -	5.71	SO4 2-	6.94
	C2H5COO -	0.25
	C2O4 2-	1.75
	Total Organic (TO)	16.22	Total Mineral (TM)	15.82
	Potential Acidity (pA)	32.04
	= TO+TM
	Calculated H +	4.21
	pA-H +	27.83
	8
	Nitrogenous contribution
	17

9

NH4 + is the dominant nitrogen ion in precipitation in Mbita at both monthly and annual timescales 10 (Table

2

). The predominance of NH4 + in rainwater has also been observed in dry savanna (19.1 to 11 25.2 µeq L -1 ; (Laouali et al 2012)), including savannas influenced by industrial emissions (~29 µeq L - 12 1 ; (Conradie et al 2016)), as well as in wet savanna sites (14.3 to 16.8 µeq L -1 ; (Yoboué et al (2005); 13 Akpo et al (2015)), though concentrations in Mbita are higher than in these studies. Annual VWM 14 NO3 -concentrations at Mbita are comparable to those obtained in wet savannas (8.2 to 8.9 µeq L -1 , 15 Yoboué et al 2005, Akpo et al 2015), but lower than those found in dry savannas (10.4 to 14.6 µeq L - 16 1 , Laouali et al 2012, Conradie et al 2016).

Table 4 :

 4 Dissolved Inorganic Nitrogen (DIN), Dissolved Organic Nitrogen (DON) and Total Dissolved Nitrogen (TDN) in kgN ha -1 yr -1 in tropical ecosystems and African great lakes.

	Site Kampala (Uganda) Lake Victoria North shore (Kenya) Itumbiri (Tanzania) Tanzania side Tanzania side Lake Victoria Mbita (Kenya) Lake Tanganyika Burundi-RDC-Tanzania-Zambia Burundi-Tanzania-Zambia -Lake Malawi Monkey Bay (Malawi) Senga Bay (Malawi) Lake Kivu (RDC) Catchment La Selva (Costa Rica) West and Central Africa Sahelian dry savannas Wet savannas Forest South Africa Amersfoort Vaal Triangle Louis Trichardt Skukuza Other tropical regions Paracou ( French Guiana) Lake Valencia (Venezuela) Amazon basin(Brazil) Amazon basin(Venezuela) Congo forest (RDC) A c c e p t e d M Pi (mm) Characteristics 1300 ND 1000 1050 1050 ND ND Rural Peri Urban Urban 1070 Tropical Rural 819 1200 ND Tropical ecosystem Tropical-Agriculture ND ND ND ND ND ND Agricultural land 3500-4500 Tropical forest 449-744 1261-1274 1557 Rural sites 729.75 956.43 728.2 583.23 Semi-arid 3100 Tropical forest ND ND ND ND ND ND Tropical forest AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1 N-NH4 + N-NO3 -N-NO2 -DIN DON TDN 6.36 5 11.36 11.96 7.7 6.8 5.14 1.25 0.01 6.4 2.32 8.54 4.18 1.67 5.86 6.72 12.05 0.85-1.56 1.5 0.27 3.74 4.01 2.6 -5 1.8 -3.4 4.4-8.4 2-3.4 3.5-5.3 3.6 6.32 6.97 1.87 2.12 0.677* 1.092* 8.75* 2.43 3.15 21.4 1.28 2.52 0.05 3.76 5.67 5.46 1.33* 12.74 18.20 6.4 a n u s c	References This study (Gao et al 2018) (Langenberg et al 2003) (Bootsma and Hecky 1993) (Bootsma and Hecky 1993) (Bootsma and Hecky 1999) (Namugize 2015) (Eklund et al 1997) (Galy-Lacaux and Delon 2014) (Conradie et al 2016) Van Langenhove et al. (2020) (Lewis 1981) (Amazoniana 1972) (Jordan, et al 1979) (Bauters et al 2018) (Visser 1961) (Bootsma and Hecky 1993) (Vuai et al 2013)	Page 26 of 42

  techniques and management of human and animal waste. Finally, ongoing and future studies on N 1 deposition are very important and should be communicated to policy makers to manage the unique 2 natural resource represented by the Lake Victoria and its catchment and to mitigate nutrient budgets Annual Development Plan (ADP) 2020/2021. (Homa Bay County: Department of Finance,

	Page 27 of 42	AUTHOR SUBMITTED MANUSCRIPT -ERL-110179.R1
	3 21
	32 4 5 6 7 8 9 10 22 23 24 25 26 35 A c c e p t e d M in the great African lakes ecosystems. Acknowledgements: This study was supported by the United Nations Environmental Program (GEF-UNEP) International Nitrogen Management System (INMS) project, and by the French program Cycle de l'Azote entre la Surface et l'Atmosphere en afriQUE (CASAQUE) funded by Les Enveloppes Fluides et Environnement (LEFE) / Ecosphère Continentale et Côtière (EC2CO) of the Institut National des Sciences de l'Univers-Centre National de la Recherche Scientifique (INSU-CNRS). This project has received funding from the European Union's Horizon 2020 research and innovation Economic planning and service delivery, Homa Bay government.) Akpo A B, Galy-Lacaux C, Laouali D, Delon C, Liousse C, Adon M, Gardrat E, Mariscal A and Darakpa C 2015 Precipitation chemistry and wet deposition in a remote wet savanna site in West Africa: Djougou (Benin) Atmos. Environ. 115 110-23 Amazoniana 1972 Regenwasseranalyseanus Zentralamazoniena, usgefuhrt in Manaus, Amazonas, Brasilien, von Dr. Harold Ungemach, 3(2) 186-98 a n u s c r i p t Soil Pollution, 130, 451-456. 130, 451-456.

11

programme under the Marie Skłodowska-Curie grant agreement No 871944. We acknowledge Peter 12 Nyongesa and Francis Ncodimba from ICIPE Center for hosting this project and facilitating logistics.

13

We are particularly grateful to the technician field, Sylas Okumu, for his work. IASI is a joint mission 14 of EUMETSAT and the Centre National d'Etudes Spatiales (CNES, France). The version 3 of the 15 IASI-NH3 dataset was created by the SQUARES group at ULB (Université libre de Bruxelles (ULB), 16 Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, 17 Belgium). We would like to thank Martin Van Damme, Lieven Clarisse, and Pierre Francois Coheur 18 for the NH3 dataset. 19 ADP 2019 27 Andreae M O, Byrd J T and Froehlich P N 1983 Arsenic, antimony, germanium, and tin in the Tejo 28 estuary, Portugal: modeling a polluted estuary Environ. Sci. Technol. 17 731-7 29 Avila A, Alarcón M and Queralt I 1998 The chemical composition of dust transported in red rains-30 its contribution to the biogeochemical cycle of a holm oak forest in Catalonia (Spain) Atmos. 31 Environ. 32 179-91 32 Avila A, Queralt-Mitjans I and Alarc´on, M 1997 Minerological composition of Africa dust delivered 33 by red rains over northeastern J. Geophys. Res. 21977-96 34 Balasubramanian 2001 Chemical and statistical analysis of precipitation in Singapore, Water, Air and 36 Bauters M, Drake T W, Verbeeck H, Bodé S, Hervé-Fernández P, Zito P, Podgorski D C, Boyemba

 : quantities 1950[START_REF] Sigha-Nkamdjou | Rainwater 290 Chemistry and Wet Deposition over the Equatorial Forested Ecosystem of Zoétélé (Cameroon) 291[END_REF], aquaculture production: values 1984[START_REF] Sigha-Nkamdjou | Rainwater 290 Chemistry and Wet Deposition over the Equatorial Forested Ecosystem of Zoétélé (Cameroon) 291[END_REF]capture 139 production: 1950[START_REF] Sigha-Nkamdjou | Rainwater 290 Chemistry and Wet Deposition over the Equatorial Forested Ecosystem of Zoétélé (Cameroon) 291[END_REF]commodities production andtrade: 1950-2004;total production: 140 1970-2003, Vers. 2.30. Data andstatistics unit. FAO Fisheries Department, Fishery 141Information Online: www.fao.org/fi/statist/FISOFT/FISHPLUS.asp).