African Anthropogenic Emissions Inventory for gases and particles from 1990 to 2015

. There are very few African regional inventories providing biofuel and fossil fuel emissions. Within the framework of the DACCIWA project, we have developed an African regional anthropogenic emission inventory including the main African polluting sources (wood and charcoal burning, charcoal making, trucks, cars, buses and 15 two-wheeled vehicles, open waste burning and flaring). To this end, a database on fuel consumption and emission factors specific to Africa was established, using the most recent measurements. New spatial proxies (road network, power plant geographical coordinates) were used to convert national emissions into gridded inventories at a 0.1° x 0.1° spatial resolution. This inventory includes carbonaceous particles (black and organic carbon) and gaseous species (CO, NO x , SO 2 and NMVOCs) for the period 1990-2015 with a yearly temporal resolution. We show that 20 all pollutant emissions are globally increasing in Africa during the period 1990-2015 with a growth rate of 95%, 86%, 113%, 112%, 97%, and 130% for BC, OC, NO x , CO, SO 2 and NMVOCs, respectively. We also show that West Africa is the highest emitting region of BC, OC, CO and NMVOCs, followed by East Africa, largely


Introduction
According to the UN (2015) report, "World Population Prospects: The 2015 Revision", Africa is expected to account for more than half of the world's population growth between 2015 and 2050. This rapid increase in 35 population is accompanied by a dramatic increase in anthropogenic emissions of atmospheric pollutants as shown in Liousse et al. (2014).
Pollutant concentration measurements carried out during the POLCA (POLlution des Capitales Africaines) project (Liousse and Galy-Lacaux, 2010) have shown that African urban areas such as Bamako (Mali) and Dakar (Senegal) are already highly polluted and affect the population's health (Doumbia et al., 2012;Val et al., 2013) and therefore 40 the economy of the region. Measurements recently performed as part of the DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) program for Cotonou (Benin) and Abidjan (Cote d'Ivoire) also show that PM2.5 concentrations are 2 to 10 times higher than the WHO standards (Adon et al., 2020;Djossou et al., 2018;Evans et al., 2018). The same results were also observed in Dakar (Dieme et al., 2012). If no measures are taken, air pollution in Africa will worsen since emission regulations have still to be implemented on the continent (Liousse 45 et al., 2014).
An accurate estimation of anthropogenic emission inventories is fundamental for models of air quality and climate change, as well as for the development of control and mitigation strategies. Emission inventories are commonly constructed using a bottom-up approach where available statistics on fuel combustion for anthropogenic sources (e.g. traffic, industry, residential combustion, etc.) are combined with representative emission factors. Many of the 50 inventories that exist today are at the global scale and do not contain detailed information specific to Africa. Such global inventories (Bond et al., 2004;Junker and Liousse, 2008;Granier et al., 2011;Smith et al., 2011;Klimont et al., 2013;Klimont et al., 2017;Hoesly et al., 2018) have been used for air quality and climate modelling in Africa (Deroubaix et al., 2018;Haslett et al., 2019). The few regional inventories that have been published for Africa such as Liousse et al. (2014) for combustion 55 sources and Assamoi and Liousse (2010) for two-wheeled vehicles, have shown that significant uncertainties still remain on fuel consumption, emission factors and spatial distribution of emissions in Africa (e.g. there is a lack of reliable statistic on national activity data and emission factor specific to the sources considered , population density is used as a default spatialization proxy for all sectors). These previous studies indicate that some important sources such as waste burning and flaring sources are not well represented. It is therefore a challenge for policy makers to identify specific emission sources in Africa should be considered as targets for designing effective pollution control regulations and mitigation strategies. This paper presents a comprehensive, consistent and spatially distributed new inventory for Africa, which provides emissions of particles, i.e. black carbon (BC) and organic carbon (OC), and of gaseous compounds, carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2) and non-methane volatile organic compounds 65 (NMVOCs). This inventory covers the 1990-2015 period and considers the main anthropogenic emissions sources specific to Africa, such as open waste burning, charcoal making, flaring emissions as described in Doumbia et al. (2019) and two-wheeled vehicles emissions as described in Assamoi and Liousse (2010), in addition to traffic, domestics fires, industries and power plants. It takes into account the new emission factors reported by Keita et al. (2018). 70 Section 2 describes the methodology and data sources selected for the different emission sources. The results for sectoral emissions, spatial distributions, as well as emission trends are presented in Section 3, which also includes a comparison with other studies together with a discussion on uncertainties.

Methodology
The quantification of biofuel and fossil fuel emission inventories from 1990 to 2015 use a bottom-up methodology 75 based on the relationship: where i, j, k represents the pollutant, fuel and sector, respectively. E represents the emission of pollutant (i), EF is the emission factor (g of pollutant per kg of burned fuel), CE is the efficiency of combustion and C is the annual 80 fuel consumption in kilotons (kt). Note that this methodology follows the work of Junker and Liousse (2008) and Liousse et al. (2014). Mean CE values obtained by Keita et al. (2018)  Details are retained for the four sub-sectors within the traffic sector (road, rail, domestic navigation and aviation).

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The UNSTAT fossil fuel (FF) consumption database does not mention two-wheeled (TW) vehicle consumption specifically, though this is a common and highly polluting source in Africa. Previous work has indeed shown that TW vehicles use mainly smuggled fuel, particularly in the neighboring countries of Nigeria's first African crude oil producer and exporter (Assamoi and Liousse, 2010). Our estimation of the number of TW vehicles and their fuel consumption is based on the work of Assamoi and Liousse (2010). Assamoi and Liousse (2010) (Corsi et al., 2012) statistical data for 9 countries with non-negligible TW vehicle numbers (i.e. when the consumption of the TW fleet is more than one-tenth of the country's gasoline consumption) and for which Assamoi and Liousse (2010) do not provide data.
Finally, we estimate TW vehicle number per year for the entire study period , based on linear 120 extrapolation techniques, using Assamoi and Liousse (2010) and DHS values. Fuel consumption for TW vehicles is also calculated based on Assamoi and Liousse (2010). For the whole period 1990-2015, we use the mean values obtained from minimum and maximum assumptions given by this paper for TW vehicle characteristics (6 as number of traffic days, 1.875 liters as daily consumption for taxis and 0.75 liters for private use only and 754 kg/m 3 as fuel density). TW vehicles are indeed used for public transportation in addition to private use in 6 west and 125 central African countries (Benin, Cameroon, Chad, Niger, Nigeria and Togo).

Emission factors (EF)
Emission factors are dependent on fuel-type, activity, technology and emission reduction regulations. However, in Africa, information on technology and regulations is not available for each fuel/activity and country. To take into account this limitation, our methodology is based on a "lumping" procedure designed to manage available 130 experimental data and account for the main factors of variability. Technologies and regulations are assumed to be country-dependent and all 54 African countries are classified into two groups, developing (1) Liousse et al. (2014), with the exception of the non-road traffic sub-sectors. For rail and domestic aviation using diesel (DL), aviation gasoline (AV) and jet fuel (JF), EFs for developing countries 145 are taken to be the highest value found in the literature as presented in Table 1. For these sub-sectors, the same EF is used for semi-developed and developing countries.
It should be noted that, as reported in Keita et al. (2018), new EF values for BC and OC for motor gasoline, diesel oil, and wood burning are higher than those reported by Liousse et al. (2014) (for example for motor gasoline by a factor of 1.5 and 4 respectively for OC and BC), and slightly lower in the case of charcoal burning, charcoal making 150 and two-wheel vehicles (for example for charcoal making by a factor of 0.8 and 0.9 respectively for OC and BC).

Method for open waste burning (WB) emissions
The inventory for open waste burning has been built following the IPCC guidelines (Chapters 2 and 5 available at Open waste burning is estimated using the following expression: where WB is the amount of solid waste that is burned residentially and in uncontrolled dump and EFi is the emission Pfrac is assumed to be based on national income status, urban versus rural population, and waste collection practices. In Africa, Pfrac may be considered to be 100% following Wiedinmyer et al. (2014). This value is the value obtained for countries with low income, low middle income, and upper middle income, following the classification of World Bank (http://data.worldbank.org/country). In this context, data for semi-developed countries 175 are not available. Therefore, an overestimation of waste burning practices may be expected in some countries (e.g. South Africa, Morocco, Egypt). However, currently no data exists to avoid this overestimation.
Note that it is possible to distinguish waste burning emissions which occur near the residence to those in uncontrolled dumps. Such practices are highly different in rural and in urban areas. WB can also be calculated using the following equation: where WBR and WBU represent waste burning in rural and urban areas, respectively, and are defined by: In Africa, the rural population is assumed to have no organized waste collection, therefore in rural areas, 185 (PfracRes)R linked to residential burning is assumed to be equal to 100% whereas (PfracDump)R linked to uncontrolled dumps is 0%. In urban areas, (PfracDump)U is country-dependent: for example, the fraction of uncollected waste is 0.77, 0.30, 0.40, and 0.76 for Benin, Côte d'Ivoire, Ghana and Nigeria, respectively (Wiedinmyer et al., 2014). Therefore, in Benin for example (PfracRes)U is assumed to be equal to 77% and (PfracDump)U is 23%. Rural (Prural) and urban (Purban) populations are provided by the World Bank database 190 (http://data.worldbank.org/indicator, accessed 02 November 2016). Rural/urban distinction for estimating WB emissions are an important improvement to our inventory, providing details at the local and regional levels which are missing in global inventories.

Spatial distribution of the fossil fuel, biofuel and waste burning sources
The (3) population density grid for residential combustion sources, industries and waste burning. In the future, we plan to use gridded rural and urban population densities to better disaggregate waste burning and residential emissions in Africa.

Method for flaring emissions
Flaring emissions are taken from the inventory developed by Doumbia et al. (2019) for the years 1994 to 2015 using the following equation: where EXflaring is the emission rate of a pollutant X (kiloton) and GFvolume is the volume of gas flared in billions series as the 2011 DMSP data cannot be used due to an orbital degradation that led to solar contamination.
XEF is the emission factor (EF) for species X in g/kg of fuel burned and ρ is the density of the fuel gas. Typically, the density of the fuel (natural gas) varies between 0.75 and 1.2 kg/m 3 depending on the fraction of heavy hydrocarbons present in the fuel (US Standard Atmosphere, 1976). In this inventory, we assume a gas density of 1.0 kg/m 3 for converting volume of associated gas to mass (E&P Forum, 1994). EFs for various species (CO, NOx,  % per year over the past 20 years, corresponding to a roughly 64 % increase over the period 1990-2015, which is of the same order of magnitude as the increase in biofuel emissions. Biofuel emissions have increased at a higher rate than other sources due to an increase in low-income population in sub-Saharan Africa where biomass constitutes about 80% of the total energy consumption (Ozturk and Bilgili, 2015).  Africa. This is due to their large amount of industrial activities compared to the other regions of Africa. In terms of SO2 emissions, the highest emitting region is Southern Africa, the most industrialized region in Africa, with emissions of 1.19 Tg (73%) in 1990 and 2.24 Tg (63%) in 2015. Southern Africa is followed by North Africa, East Africa, West Africa and Middle Africa, respectively. Over the 1990-2015 period, the highest rate of increase 265 in SO2 emissions occurred in West Africa (224%) followed by Middle Africa (71%), East Africa (56%), Southern Africa (47%) and North Africa (33%), respectively. As for BC, the highest rates of increase in SO2 emissions during this period are observed in regions where the population growth rate is the highest, i.e. West Africa (2.66%yr -1 ), Middle Africa (3.10% yr -1 ) and East Africa (2.71% yr -1 ) whereas the lowest rates are found where the population growth rate is the lowest i.e. Southern Africa (1.64% yr -1 ) and North Africa (1.87% yr -1 ). Africa. Table 2 also indicates that for Africa, residential combustion is the major source of carbonaceous particles for BC (40%) and OC (77%). It is also the main source of CO and NMVOCs with a contribution of 72% and 53%, respectively. Open waste burning is the second most important source of BC, OC and NMVOCs representing 35%, 15% and 22% of the total, respectively. The energy sector is the major source of SO2 and NOx emissions which constitute 54% and 29% of the total, respectively. For NOx emissions, the energy sector is followed by the traffic 280 sector (26%) whereas industry is the second largest contributor to SO2 emissions (32%).

Temporal trends of African emissions
The regional contribution from each sector is presented in Figure 4. The residential sector contributes to more than 50% of BC emissions in East and West Africa, and just under 50% in Middle Africa (Figure 4a) for the year 2015.
In Southern and Northern Africa, it contributes to less than 25% and 10% of the total BC emissions, respectively.
In these two regions, waste burning is the largest source of BC emissions with significant contributions from the 285 industry and traffic sources compared to East and West Africa. Waste burning is the second largest source of BC emissions in East and West Africa. For NOx, the traffic sector is the largest contributor in West and North Africa, with 30% and 41% of the total emissions, respectively (Figure 4b). In East Africa, the residential sector (32%) is the largest contributor of NOx emissions, followed by the traffic sector (23%). In Southern Africa, the two largest contributors to NOx emissions are the energy (52%) and industry (27%) sectors, respectively. 290 Figure 5 shows the sectoral contributions of BC emissions for a few countries in Southern West Africa (SWA) and for South Africa with different predominant sources. As previously mentioned, for countries in SWA, BC emissions are dominated by the residential sector, followed by open solid waste burning and the other sources, whose relative importance differs depending on the country. In Nigeria, industry and flaring BC emissions are much more important than in other countries in SWA. It is also interesting to see that traffic is the largest contributor to BC 295 emissions in Benin, which can be explained by the high number of two-wheeled vehicles which are more polluting than four-wheel vehicles. In Benin, TW vehicles represent 34% of road traffic emissions and only 16% in Cote d'Ivoire for example. In South Africa, waste burning is the predominant sector contributing 42% to the total BC emissions, (18% residentially and 24% in dump locations), followed by the residential (24%), industry (24%), energy (13%), traffic (9%) and flaring sectors. In Côte d'Ivoire, the residential sector is the most important (58%), 300 followed by waste burning (26% with 16% residentially and 10% in dump locations), traffic (9%), other sector (5%), industry (1%) and energy (0.1%

Comparison with previous emission inventories
We  respectively 5 and 7 times larger than the values used in DICE-Africa).
We also compared our inventory for Africa to emissions from the following global inventories : ECLIPSEv5a  inventories. The EFs used in the DACCIWA inventory are taken from direct measurements at the sources in Africa as described in Keita et al. (2018), whereas the EFs used in global inventories are largely based on measurements taken from other regions such as Europe and the U.S. which are often not appropriate for Africa.
A comparative analysis of sectoral BC and NOx emissions from the different emission inventories considered in Figure 6 is performed, which provides further information on the differences between the inventories. For this 360 comparison, we use the year 2010 which is the most recent common year for these inventories. The Fugitive sector in CEDS and EDGARv4.3 inventories mainly consists of flaring emissions; in the ECLIPSEv5a and CEDSGBD-MAPS inventory, fugitive emissions are included in the Energy sector. Figure 7 shows the relative contribution from each sector to BC and NOx emissions in 2010 for the DACCIWA inventory and the four global inventories. While large disparities exist between the inventories, it is clear that the residential sector is the highest contributor to BC 365 emissions in all the inventories. The main differences in BC emissions are largely due to the residential and waste sectors. The contribution of emissions from the waste sector varies greatly among the inventories (35%, 7%, 5% and 1% for DACCIWA, CEDS, CEDSGBD-MAPS and EDGARv4.3, respectively). It should be noted that ECLIPSEv5a does not consider a waste sector. The traffic and energy sectors are the largest emitters of NOx for both the CEDS (31% and 27%, respectively), CEDSGBD-MAPS (18% and 38%, respectively) and EDGARv4.3 (34% 370 and 32%, respectively) inventories. In the DACCIWA inventory, the contribution from the energy sector (28%) is slightly larger than from the transport sector (25%).
We also compared OC and SO2 emission trends from our inventory and the four global inventories, as shown in Figure S1. The DACCIWA inventory shows the highest value for OC emissions. This is mainly due to the value of the OC emission factor used for domestic fires , which is higher than previous values found 375 in the literature based on measurements from other regions of the world with different wood species. In contrast to OC, SO2 emissions from DACCIWA have the lowest values compared to other inventories. This could be due to differences in activity database and to the EF used for NOx and BC emissions.  Figure S2 for the REF and CCC* scenarios. We have linearly extrapolated the DACCIWA emissions for these two species, as shown by the plain lines in Figure S2. Our estimates for BC and OC are higher than the best-case scenario values and lower than the 385 worse scenario values of Liousse et al. (2014). However, OC values are being much closer to the worse scenario and BC values closer to the best-case scenario. These results demonstrate that emission mitigation measures need to be implemented urgently in Africa in order to avoid such elevated emissions in 2030.  (Frey and Zheng, 2002;Frey and Li, 2003;Zhao et al., 2011). Parametric distributions and standard deviation linked to the reliability and accuracy of data introduced by fuel consumption statistics and non-national emission factors are provided in the literature (e.g. IPCC 2006;Zhao et al., 2011;Bond et al., 2004) and expert judgment. In this study, we assume that, when the coefficient of variation is less than 30%, 400 the distribution is normal (IPCC, 2006). When the coefficient of variation is larger and the quantity is non-negative, an asymmetric lognormal distribution is assumed. The Monte Carlo method was then used to propagate these uncertainties to obtain the uncertainty on emissions for each fuel per sector. The Monte Carlo analysis consisted of selecting random values of activity and emission factors data from the respective distributions to obtain the corresponding emissions. This calculation was repeated 100,000 times to obtain the average value of the 100,000 405 emission values and their distributions. The standard deviations for these distributions were estimated with a 95% confidence interval. The uncertainty on the total emission per pollutant was obtained by combining values obtained by fuel oil and by sector.

Conclusion
Within the framework of the DACCIWA project, a new African emission inventory has been developed for fossil Comparisons with other inventories reveal significant differences between both regional inventories (Liousse et al. (2014) and DICE-Africa) and global inventories. Differences with Liousse et al. (2014)  obtaining data and constructing the inventory data files. Nellie Elguindi and Claire Granier provided comments on emission levels compared to global inventories, helped proofread the English and make data files available through the ECCAD system. Sekou Keita prepared the manuscript with contributions from all co-authors.

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The authors declare that they have no conflict of interest.  Tables   Table 1: