# Particulate pollution in Korhogo and Abidjan (Cote d'Ivoire) during the dry season

Sylvain Gnamien, Véronique Yoboué, Cathy Liousse, Sékou Keita, Julien

Bahino, Silué Siélé, Lamine Diaby

► To cite this version:

Sylvain Gnamien, Véronique Yoboué, Cathy Liousse, Sékou Keita, Julien Bahino, et al. Particulate pollution in Korhogo and Abidjan (Cote d'Ivoire) during the dry season. Aerosol and Air Quality Research, 2021, 21 (1), 10.4209/aaqr.2020.05.0201. [https://hal-03040429](https://hal.science/hal-03040429)

HAL Id: hal-03040429

https://hal.science/hal-03040429

Submitted on 4 Dec 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
# Particulate pollution in Korhogo and Abidjan (Cote d'Ivoire) during the dry season.

Sylvain Gnamien<fnref n="1" />,<fnref n="2" />*, Véronique Yoboué<fnref n="1" />, Cathy Liousse<fnref n="2" />, Money Ossohou<fnref n="1" />, Sékou Keita<fnref n="3" />, Julien Bahino<fnref n="1" />, Silué Siélé<fnref n="3" />, Lamine Diaby<fnref n="1" />

## Abstract

Particulate pollution in West African cities seriously impacts public human health. Assessing the levels of pollution to which populations are exposed is problematic, as to date very few countries in Africa have an air quality monitoring network in place. However, given the specific anthropogenic sources present in West African countries and the increase in their projected emissions in the coming years if no regulations are put in place, solutions must be found. This study evaluates with a simple existing methodology particulate air pollution in two West African cities (Korhogo and Abidjan in Cote d'Ivoire) which have different population practices and local meteorology.

From two measurement campaigns at about ten sites during the dry season and using the inverse distance-weighted interpolation method, maps of spatial variation in PM₁₀ and PM₂.₅ concentrations are obtained at the scale of each of these cities, which provide a snapshot of the concentration levels to which populations are actually exposed. The results of this study showed that populations in low- and middle-income areas are exposed to higher concentrations than those in high-income areas. The analysis of the relative contribution of fine particles to coarse particles shows that PM₂.₅ contributes to PM₁₀ at less than 40% in Korhogo but more than 50% in Abidjan. Daily average concentrations of PM₂.₅ are all above WHO standards, and are 2 to 8 times higher in Korhogo, while in Abidjan they are up to 4 times higher. The daily profiles of the mean hourly concentrations of the sites are similar across all sites in Korhogo, while in Abidjan, that of some sites show some differences.

This study, by the methodology used, offers an opportunity for different research teams to assess at lower cost the population's exposure levels to urban particulate pollution.

**Keywords:** Urban air quality; Aerosols; Africa cities; PASMU project.

<footnote n="1">¹Laboratoire de Physique de l'Atmosphère et de Mécanique des Fluides, Université Félix Houphouët-Boigny, Abidjan BPV 34, Côte d'Ivoire</footnote>

<footnote n="2">²Laboratoire d'Aérologie, CNRS, Toulouse, France</footnote>

<footnote n="3">³Université Péléforo Gon Coulibaly, Korhogo, Côte d'Ivoire</footnote>

<footnote>* Corresponding author. Tel: +225 09328731; Fax: +33 (0)562406101
E-mail address: gnkonans@gmail.com</footnote>
36 INTRODUCTION

37 Air pollution is a global public health problem and its extent in the cities of many developing
38 countries is still poorly known (Kirenga et al., 2015). Among all pollutants, particulate pollution is
39 highly harmful to health, especially in low- and middle-income countries (Kalisa et al., 2019).
40 Recent studies have shown alarming levels of particulate pollutants in West African cities (Dieme
41 et al., 2012 and Djossou et al., 2018), due to various sources of pollution that are generally
42 uncontrolled and relatively close to living areas Val et al., (2013) and Adon, (2019) highlighted the
43 inflammatory nature of particles rich in organic carbon (OC) and small in size emitted by urban
44 west African sources. Also, a few studies (Cohen et al., 2005 and Liousse et al., 2019) have shown
45 the importance of premature death estimations for cardiovascular, lung cancer and respiratory
46 diseases causes, due to anthropogenic fine particles in West Africa.

47 Anthropogenic sources are harmful in developing countries because of the absence or
48 ineffectiveness of regulations on pollutant emissions. The typical anthropogenic sources in sub-
49 Saharan Africa are domestic fires (biomass and gas for cooking), road traffic (combustion
50 emissions and dust resuspension), waste combustion, industrial activities (petrochemicals,
51 agriculture, construction. More precisely, air quality in Cote d'Ivoire and in West Africa, is
52 impacted by emissions from a very old vehicle fleet, on average 20 years according to the Ministry
53 of Transports, (2017), using poor quality fuel and from the use of traditional stoves with wood
54 and/or charcoal incomplete consumption. Furthermore, open burning of waste in urban areas of
55 Africa is a very common phenomenon due to the ineffectiveness of municipal waste collection and
56 management policies (Keita et al., 2020). In addition to these local sources, there are important
57 imported sources of aerosols: (1) desert dust and natural savannah fire sources from prevailing
58 Harmattan wind (north wind) as well as (2) marine aerosols brought by monsoon flow (southwest
59 wind).
Unlike developed countries, most cities in the developing countries do not have the capacity to monitor urban air concentrations in order to effectively solve the problem of air pollution. In West Africa, Dakar (in Senegal), is the only city with an air quality monitoring network. Considering the anthropogenic source diversity and their strength which is expected to increase if no regulation is taken (Liousse et al., 2014), it is necessary to expand pollution studies in African cities.

Studies carried out in West Africa have shown high concentrations of PM<sub>2.5</sub> and PM<sub>10</sub> in several cities: Accra in Ghana (Dionisio et al., 2010 and Arku et al., 2008); Dakar in Senegal (Dieme et al., 2012 and Val et al., 2013) and Bamako in Mali (Val et al., 2013 and Antonel and Chowdhury, 2014). In the European program DACCIWA (Dynamic-Aerosol-Chemistry-Cloud Interactions in West Africa), Djossou et al., (2018) observed average concentrations (February 2015 to March 2016) in Abidjan (Cote d'Ivoire) and Cotonou (Benin), well above WHO standards. Similarly, in the POLCA program (POLlution of African Capitals), Doumbia et al., (2012) measured PM<sub>2.5</sub> concentrations at an urban site in Dakar from June 2008 to May 2009, which were above the WHO standard. These various studies highlight overexposure of populations to atmospheric particles whose economic impacts were assessed by Stern and van Dijk, (2017), and mortality by Cohen et al., (2005) and in Global Burden of Disease Study 2010 by Murray et al., (2012). Also, within the framework of DACCIWA, Keita et al., (2020) have calculated emission factors (EF) for particulate pollutants based on sources and considering the specificities of African regions in order to improve emission inventories for these pollutants. These results showed very high EF values, highlighting the importance of particulate pollutant emissions from sources such as domestic fires and traffic, which are very important sources of pollution in West Africa. These different results are a challenge for us and we must act.

The lack of air pollution monitoring networks, even in the most urbanized cities, due to the high cost of acquiring and maintaining air quality measurement equipment can be offset by the
84

installation of low- and medium-cost sensors offering simple and inexpensive alternatives.

85

Although accessible, these sensors cannot be deployed always and everywhere. To overcome this problem, an experimental existing methodology based on the use of low-cost sensors during intensive measurement campaigns and a geostatistical treatment tool is used to assess particulate air pollution in two cities of Cote d'Ivoire. Specifically, this study quantifies the level of PM₁₀ and PM₂.₅ concentrations and their spatial and temporal distribution in these cities of different geography and morphology (Korhogo and Abidjan). This work is carried out within the framework of the project Air Pollution and Health in Urban Areas, Cote d'Ivoire (PASMU).

86

87

88

89

90

91

92

93

# METHODOLOGY

94

95

## Description of the experimental sites

96

97

98

99

100

101

102

103

104

105

106

107

In this study, two measurement campaigns were conducted successively in Korhogo (January 3rd to January 16th) and in Abidjan (January 20th to February 10th, 2019), whose economic activities, transport specificities and types of roads, cooking fuels, population practices and local meteorology differ. As shown in Fig. 1, the population densities of these two cities are also very different, with a population of Abidjan 10 times that of Korhogo. It can also be noticed that population densities from south to center of Cote d'Ivoire are generally higher compared to areas further north. The population density of Korhogo remains high for a northern region due to agricultural industries. In each city, the measurement sites are chosen to allow representative mapping of the variability of pollution including different districts. Generally, measurement sites can be of 3 types: (1) *Local stations*, generally used to control classified establishments, are located in proximity of the polluted source to be monitored and therefore under the direct influence of this pollution source; (2) *Urban background stations*, used to monitor urban pollution, are located in an urban environment but
under the indirect influence of pollution sources, and (3) Background stations, are located in a peri-urban environment. It is important to note that local stations are therefore not included in this study as they only provide very local information (Jeannée et al., 2006). In our study, urban background stations are mainly influenced by major west-African urban sources, i.e. the domestic fires, the waste burning and the road traffic (from combustion or non-combustion). In addition, dust resuspension of particulate matter is also important because of road conditions and urban developments. In this study, 14 non-permanent sites in Korhogo and 20 in Abidjan were sampled for 3 days (2 working days and 1 weekend day). The 2 cities and their sampling sites are described below.

## Korhogo sampling sites

Korhogo is the third largest city in Cote d'Ivoire in terms of population. A large part of the city's area is not built. Korhogo is a city still marked by agricultural activities with a few rare agricultural product factories for processing and packaging. The principal sources of anthropogenic emissions in Korhogo are traffic sources with intense activities of heavy vehicles, national bus line but also motorcycle traffic, which is a particularity of Korhogo. Furthermore, in this city, only main roads are paved and represent, 10% of the urban roads (AGEROUTE, 2020). This implies important road resuspension of aerosols which is also a major source of PM emissions. Another important anthropogenic source is the domestic fire with the use of firewood for economic reasons or for convenience as because these resources are still available in the surrounding villages. In addition to the local anthropogenic sources present in Korhogo, the harmattan wind is a major contributor of PM in the dry season with desert dust, biomass burning and aerosol transport. Fig. 2 shows the geographical distribution of the 14 measurement sites as well as the boundaries of the city of Korhogo. Also, the specificities of each sites are summarized in Table S1 (in Supplementary files), where are mentioned site localizations as well as roads infrastructure and possible sources of
132

pollution around. Additional information like that geographic positions of Korhogo measurement sites is given in Table S2 (in Supplementary files). Sites K₂, K₁₀, K₁₁ and K₁₄ are surrounded by paved roads (i.e. under the influence of the traffic source) and far from other urban sources. The other sites are located in residential areas where wood is used extensively for cooking, with the exception of K₁ site which is in a peri-urban area but close to a major traffic road.

133

134

135

136

137

138

### Abidjan sampling sites

139

Abidjan is the economic capital regrouping the main industrial activities with a population of 4,707,404 inhabitants for an area of 2,119 km² according to the General Census of Population and Housing (RGPH, 2014).

140

141

142

Abidjan is an Autonomous District with different levels of urbanization and includes populations of different social classes. Its principal anthropic sources are also traffic, domestic fires and waste burning sources with different practices depending on the communes. For instance, wood is intensively used in the commune of Yopougon whereas the use of gas dominates in Cocody. Less road dust particles compared to Korhogo are expected since more than 50% of roads are paved according to MCLAU, (2015), (MCLAU for Ministère de la Construction, du Logement, de l'Assainissement et de l'Urbanisme). However, this value also depends on the communes. In Cocody, Le Plateau and Marcory, a higher percentage of paved roads are observed compared to Abobo and Yopougon, where a large part of the city population lives.

143

144

145

146

147

148

149

150

151

Fig. 3 shows the geographical distribution of the measurement sites in Abidjan and boundaries of the sampled communes, which represent the study area of this city. In Supplementary files, Table S5 provides information on the 20 sites sampled in Abidjan. In this study, only the sub-prefecture of Bingerville (Abidjan peripheral municipality) is associated with the 10 municipalities of Abidjan, because Bingerville city is completely integrated into Abidjan. As shown in, in Supplementary files,

152

153

154

155
156 in Table S5, measurement sites are located in neighborhoods inhabited by high-income populations
157 (A₁, A₂, A₁₁, A₂₀, A₄, A₆, A₈, A₁₄, A₁₅), middle (A₁₈) and low (A₃, A₅, A₇, A₉, A₁₀, A₁₂, A₁₃, A₁₆,
158 A₁₇, A₁₉). The latter sites are generally close to unpaved roads. In addition, Table S6 (in
159 Supplementary files), give latitude and longitude of Abidjan measurement sites.

160

161 *Meteorological data and surrounding pollution*

162 To assess the impacts of weather parameters on PM concentration levels measured at each site
163 during the 3-day sampling period, temperature, wind intensity and direction are analyzed at local
164 and regional scales. These meteorological parameters are obtained from the data of the synoptic
165 stations managed by the Agency for the Safety of Air Navigation in Africa and Madagascar
166 (ASECNA) at Korhogo Airport (station No. 65536) and at Felix Houphouët-Boigny Airport in
167 Abidjan (station No. 65578).

168 Average daily temperatures (available on [https://www.accuweather.com/fr/ci/cote-divoire-weather](https://www.accuweather.com/fr/ci/cote-divoire-weather)) show a range of 32 to 36 °C in Korhogo and 31 to 35 °C in Abidjan. These daily averages 169 do not vary by more than one degree over several consecutive days. This may suggest stability of 170 the atmosphere. 171

172 Wind roses presented in Fig. 4 highlight important north (N) and north-east (NE) winds in 173 Korhogo (Fig. 4 (a)) and south-west (SW) winds in Abidjan (Fig. 4 (b)). The regional climate is 174 defined by the position of the Intertropical Convergence Zone (ITCZ), which regulates the 175 succession of weather seasons (Nouaceur and Murarescu, 2020). During measurement campaign, 176 the ITCZ is located above the latitude of Abidjan, favoring the arrival of air masses from the south 177 to Abidjan and air masses from the north to Korhogo where high temperature and low relative 178 humidity are typical for dry seasonal conditions. Such features are confirmed by the use of 179 HYSPLIT trajectories ([https://www ready.noaa.gov/HYSPLIT_traj.php](https://www ready.noaa.gov/HYSPLIT_traj.php)) as shown by Fig. S1 (in
Supplementary files). HYSPLIT trajectories represent the movement of air masses arriving at the study areas during the measurement campaigns. Fig. S1 shows 14- and 21-days back-trajectories before the end dates of each measurement campaign respectively for Korhogo and Abidjan respectively. It gives an overview of the dominant air mass origins over the measurement period. As expected, Korhogo is under the influence of northern trajectories whereas Abidjan is under the influence of southern air masses, associated to air masses coming from the Nigerian coast.

In order to identify which aerosols could be advected into Korhogo and Abidjan using these trajectories, emphasis was put on MODIS active fire maps and Aerosol Optical Thickness data (AOD), obtained from https://neo.sci.gsfc.nasa.gov. Figs. S2 present active fires (a and b) and Aerosol Optical Depth (c and d) in January (a and c) and February (b and d) 2019, representative of the study period. Associated with Figs. S1, this shows that Korhogo is under the influence of regional winds that carry desert dust and biomass burning aerosols coming from the north and south of the city. While at Abidjan these regional winds show that only marine aerosols are expected.

## Material and Methods

### Measurement campaigns and equipment

The first step of the methodology deals with intensive data collection through measurement campaigns conducted at sites in the two cities, as presented in previously. The measurement campaigns of PM₁₀ and PM₂.₅ concentrations were carried out with 4 portables PM measuring devices (Aeroqual monitor S500 with PM₁₀/PM₂.₅ sensor). Particulate pollution is measured over 3 days at each site, with 2 consecutive working days and 1 weekend day, because these 2 periods (working and weekends days) present different activities levels in urban areas. Indeed, studies have shown different levels of pollution at these 2 periods of the week (Adame et al., 2014; Doumbia et
al., 2012; Adon et al., 2020). During the campaigns, the measurements are not simultaneous on all sites of each city because rotations are organized. During the campaigns, the measurements are not simultaneous on all sites of each city. Rotations are organized with at maximum 4 sites running in parallel. This is possible due to atmospheric stability detailed in the meteorological paragraph. This technique of measuring on non-permanent sites during short periods allows to maximize measurements on a large number of non-permanent sites with a small number of equipment.

In this study, optical PM₁₀/PM₂.₅ sensor on S500 Aeroqual monitor is used to measure particulate pollution, because it is easy to use and take up very little space. This sensor covers a concentration range from 0 to 1000 µg m⁻³, and simultaneously measures PM₁₀ and PM₂.₅. These types of sensors are based on light scattering and have an accuracy that can be influenced by the physical and chemical characteristics of aerosols (Wang et al., 2015). The measuring device has been placed in a box to protect it from natural elements. The device is installed at a height of between 2 and 3 m. The time step chosen is 3 minutes, in order to allow fine sampling and to store the data over the entire measurement period. After data retrieval, the data logger of the equipment is formatted before being installed at another site.

*Quality Control data:* The evaluation of the uncertainties associated with low- and middle-cost sensor measurements is the subject of several studies, as shown by Wang *et al.*, (2015) who tested 3 low-cost sensors in combustion chambers with different results. Consequently, it is necessary to evaluate and correct optical sensor concentration measurements against a reference method (Clements *et al.*, 2017). The reference method here proposed, is the gravimetric method. At some of the selected sites, aerosol sampling lines are installed, including a fixed flow pump, a volume meter, a valve, an aerosol size selection system (cane or cut-off head). These lines allow sampling by filtration of aerosols on quartz filters, over the same periods the same sites than the low-cost sensor measurements. The filters are weighed on a submicron balance before and after exposure in
order to deduce the collected aerosol masses. This reference method has been used and validated
in several research projects and programs such as the International Network to study Deposition
and Atmospheric chemistry in Africa (INDAAF) program (ref.) and the DACCIWA program
(Djossou, 2018).

Geometrical means of the measurements obtained with low-cost sensors are compared with
those obtained by the gravimetric method (reference method). Geometrical means concentrations
of optical sensors are consistently lower than the gravimetric concentrations of 2.7±1.1 and 3.5±0.9
for PM₁₀ and PM₂.₅ respectively. The standard deviation of these coefficients is higher for PM₁₀
than for PM₂.₅, the latter having more points of comparison. It is worth noting that these values are
in agreement with those of Zuidema *et al.*, (2019) who showed the need for a correction factor of
2.9 for an optical photometric sensor (GP2Y1010AU0F, Sharp Electronics, Osaka, Japan)
compared to the gravimetric method. Similarly, Egondi *et al.*, (2016) also showed overestimated
concentration values given by the DustTrak II 8532 portable samplers compared to a reference
method. These factors (2.7 and 3.5) are then applied to PM₁₀ and PM₂.₅ concentrations at all
measurement sites.

243

244 *Spatial mapping*

Geographic Information System (GIS) provide a robust method of spatial analysis through
interpolation. The most commonly used methods, kriging (geostatistical method) and reverse
weighting of IDW (deterministic method), have already been compared in works. For example,
Shukla *et al.*, (2020) in the Delhi megacity showed that kriging and IDW methods are associated
to errors close to 22% and 24% respectively. In addition, Bahino *et al.*, (2018) carried out a spatial
distribution of gaseous pollutants (NO₂, SO₂, NH₃, HNO₃ and O₃) in Abidjan (Cote d'Ivoire) and
Cotonou (Benin) during the dry season within DACCIWA Program. The latter study included also

245

246

247

248

249

250

251
a comparison between kriging and IDW, and showed that IDW method is associated to lower uncertainty than kriging method. In addition, our measurement network (number of points and geographic locations) is developed on the basis of the network of Bahino et al., (2018); therefore, in this study, the IDW method is chosen and applied to the collected data.

ArcGIS version 10.3 software (ESRI Inc.) is used because it combines geostatistical data processing and map edition. The shapefiles for map editing are available for free on OpenStreetMap (https://www.openstreetmap.org). Briefly, IDW interpolation allows to predict the concentrations around a sampled point from the following formula presented in Eq. (1).

$$Z_{pj} = \sum_{i=1}^{N} \lambda_{i} * Z_{O_{i}} \quad (1)$$

where,

$Z_{O_i}$ is the observed value at location i; form geometrical mean of measured values at each site during the different measurement campaigns. These geometrical means, averages deviation, minimums and maximum are available, for Korhogo in Table S3 (for PM$_{10}$) and Table S4 (for PM$_{2.5}$) and for Abidjan in Table S7 (for PM$_{10}$) and Table S8 (for PM$_{2.5}$), in Supplementary files for each site.

$Z_{pj}$ is the predicted value for location j;

N is the number of measured sample points surrounding the predicted location that will be used in the prediction;

$\lambda_i$ are weighting factors applied at each measured value at location $O_i$. They decrease according to the separation distance.

The geostatistical analysis of the data enables the values to be evaluated in order to develop the spatial variation maps. Thus, the IDW method calculates values from the collected data for each point (unsampled and sampled) and gives average predicted values everywhere (Rivera-González et al., 2015; Zamri et al., 2012; Bahino et al., 2018).
The Root Mean Square Error (RMSE) (McKeen et al., 2005; Savage et al., 2013; Chai and Draxler, 2014) and the Mean Absolute Error (MAE) (Taylor et al., (2013); Chatterjee et al., (2013) and Jerez et al., (2013) are the two parameters widely used to evaluate the quality of the predictions of interpolation methods. In order to assess the quality of the prediction in the two cities, both RMSE and MAE values are calculated for each measurement campaign following the formulas present in Eqs. (2) and (3).

$$282 \quad RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (Z_{P_i} - Z_{O_i})^2} \quad (2)$$

$$283 \quad MAE = \frac{1}{N} \sum_{i=1}^{N} |Z_{P_i} - Z_{O_i}| \quad (3)$$

where,

$Z_{P_i}$ and $Z_{O_i}$ are respectively the predicted and observed value for a location $i$;

$N$ is the number of measured sample point.

Table 1 presents such values for the 2 cities as well as the coefficient of variation of error. In Abidjan, the MAE values are lower than the RMSE values due to the high uncertainties associated with some sites whereas no marked differences appear in Korhogo. Also, both coefficients are higher in Korhogo than in Abidjan: that could be due to the high concentration values found in Korhogo. As shown in Table 1, the coefficients of variation of error in Abidjan are higher than in Korhogo. These low values obtained in Korhogo may be due to a smaller study area than in Abidjan. This analysis highlights the impact of the distance between measurement sites on the uncertainties.

## RESULTS AND DISCUSSIONS
297 Spatial distribution of PM concentrations

298 Korhogo PM concentrations maps

299 As previously described, the PM₁₀ and PM₂.₅ concentrations obtained by geographical averages
300 allowed the mapping of spatial variations for the Korhogo study area. These maps are presented in
301 Figs. 5(a) and 5(b), respectively for the PM₁₀ and PM₂.₅. Concentrations are very high, between
302 212.2 µg m⁻³ and 534.7 µg m⁻³ for PM₁₀ and 54.3 µg m⁻³ and 218 µg m⁻³ for PM₂.₅ and well above
303 the World Health Organization (WHO) standards which stipulate that PM₂.₅ should not exceed 25
304 µg m⁻³ 24-hours mean and that PM₁₀ not exceed 50 µg m⁻³ 24-hours mean.

305 As shown in Fig. 5(a), the measurement sites in the city downtown have higher PM₁₀ concentrations,
306 namely K₂ with 534.7 µg m⁻³, K₆ with 374.4 µg m⁻³, K₁₁ with 475.1 µg m⁻³ and K₁₄ with 446.7 µg
307 m⁻³ compared to the sites in the northern and southern outskirts of the city such as K₁ with 241.4
308 µg m⁻³, K₈ with 245.1 µg m⁻³ and K₁₂ with 261.2 µg m⁻³. This can be explained by the economic
309 activities mainly occurring in Korhogo downtown, associated with intense road traffic dominated
310 by 2-wheel vehicles and the presence of the urban bus stations. The highest concentrations observed
311 in K₂, K₁₁ and K₁₄ sites are also due to the proximity of busy highways.

312 K₁₃ and K₅ sites have also high PM₁₀ concentrations because they are in neighborhoods with
313 unpaved roads. Moreover, even if downtown roads are mostly paved, a high resuspension of dust
314 by intense traffic is also observed. Indeed, as mentioned earlier, dust deposition on roads are also
315 observed at Korhogo due to northerly air masses transporting desert dust aerosols at the period of
316 this study (Fig. S1). Such aerosols significantly contribute to the pollution level of Korhogo.

317 Finally, Fig. 5(a) shows that in Korhogo, PM2.5 and PM10 spatial variation maps roughly present
318 the same pattern of high and low concentrations despite some differences which are detailed in the
319 following section.

320
321 Abidjan PM concentrations maps

322 The spatial variation maps of PM₁₀ and PM₂.₅ concentrations (obtained from the geographical 323 averages of the measured concentration time series) in Abidjan study area are given respectively 324 in Figs. 6(a) and 6(b). The average concentrations are between 38.1 µg m⁻³ and 160.4 µg m⁻³ for 325 PM₁₀ and 23.8 µg m⁻³ and 113.4 µg m⁻³ for PM₂.₅. For PM₁₀, the highest concentrations are found 326 at sites A₇ (160.4 µg m⁻³), A₁₈ (125.3 µg m⁻³) and A₂₀ (120.7 µg m⁻³). The A₇ and A₂₀ sites are 327 located nearby unpaved roads (Table S2) therefore, dust from road resuspension contribute to the 328 increase of concentrations. In A₁₈ site, the high concentrations may be explained by the proximity 329 of important road traffic and industrial activities. In addition, A₁₈ and A₁₉ sites are located on the sea 330 front, and are therefore subject to a high contribution of marine aerosols. 331

332 Sites included in areas with better quality infrastructures and with middle- or high-class 333 populations, have generally lower PM₁₀ concentrations (e.g. A₂, A₁₁) compared to sites in 334 neighborhoods with poor quality infrastructures (e.g. A₃, A₅, A₇, A₈, A₁₆, A₁₈ and A₂₀). 335

336 Compared to PM₁₀, PM₂.₅ spatial variation (Fig. 6(b)) presents high concentrations over most of 337 the study area, despite some very localized low concentrations in A₁ (32.9 µg m⁻³), A₂ (24.9 µg m⁻³), A₅ (30.8 µg m⁻³), A₁₄ (23.8 µg m⁻³) and A₁₉ (24.5 µg m⁻³). PM₂.₅ concentrations in northern 338 Abidjan are higher than in southern Abidjan. Indeed, the wind rose of Abidjan shows a dominance 339 of south, southwest winds, which could lead to a transport and an accumulation of pollutants in the 340 northern area of the city, as for Bahino *et al.*, (2018). Values for these sites (A₇ with 113.4 µg m⁻³, 341 A₉ with 91.7 µg m⁻³, A₁₂ with 73.2 µg m⁻³ and A₂₀ with 91 µg m⁻³) are 2 to 6 times higher than the 342 WHO daily standard (25 µg m⁻³). Moreover, these sites mainly appear in low income neighborhood. 343 Indeed, in such areas, high concentrations result from the combination of the use of wood for 344 cooking, the proximity of public transport (small bus: gbaka, municipal taxis: wôrô-wôrô) with 345 very old and poorly maintained vehicles and the burning of solid domestic waste due to collection 346
345

failure. In contrary, the less densely populated areas (high-class districts) have lower concentrations (sites A₁, A₂, A₁₄) than the medium and low-class districts. In these areas (sites A₁, A₂, A₁₄), people have higher incomes and are more likely to use gas for cooking than wood and charcoal for cooking activities. Lower concentrations are observed in A₅ and A₁₉ sites, although located in popular districts. That may be due to less intensive combustion activities than usually observed in these districts for the A₅ site and to the proximity of the sea front for A₁₉ site.

346

347

348

349

350

351

352

## Relative importance of fine particles

353

In order to highlight the relative contribution of particulate size and aerosol sources, spatial variation maps of the percentage of relative contribution of PM<sub>2.5</sub> to PM<sub>10</sub> are presented in Figs. 7 and 8 for Korhogo and Abidjan respectively. Low values represent a high contribution of PM<sub>2.5</sub> to PM<sub>10</sub>, while high percentages represent a low contribution of PM<sub>2.5</sub> to PM<sub>10</sub>. It is important to note that these figures show totally different values for the two cities. In Korhogo, more than 50% of PM<sub>10</sub> are coarse particles, with the highest values in the north and south of the city and the lowest values in the city center, where most urban activities take place. In Abidjan, the situation is totally different: coarse particles in PM<sub>10</sub> represent less than 40%, with the highest values in A<sub>4</sub>, A<sub>5</sub>, A<sub>14</sub>, A<sub>18</sub> and A<sub>19</sub> sites and the lowest values in A<sub>6</sub>, A<sub>9</sub>, A<sub>11</sub>, A<sub>12</sub>, A<sub>13</sub> and A<sub>15</sub>.

354

355

356

357

358

359

360

361

362

In both cities, the lowest values associated with high PM<sub>2.5</sub> are found particularly in densely populated sites or in economically very active neighborhoods with intense combustion sources and fine particle emissions. In addition, the percentages of coarse particles (PM<sub>2.5-10</sub>) relative to PM<sub>10</sub> are high near unpaved roads and sometimes in areas with heavy traffic.

363

364

365

366

In Abidjan, low contributions of PM<sub>2.5</sub> to PM<sub>10</sub> are also observed in industrial areas (site A<sub>18</sub>). In these areas, heavy truck traffic damages the roads and causes dust resuspension. In addition, a contribution from marine aerosols is possible due to the south-westerly (SW) wind direction. These

367

368
values are in line with our expectations and confirm the good representation of the measurement
sites and the good performance of the spatial distribution, because they reflect the main sources of
pollution that influence each city.

372

373 **Daily distribution of PM concentrations**

374 Daily profiles of fifteen minutes average concentrations of PM are shown in Fig. 9. The average
375 daily profiles of PM10 and PM2.5 concentrations in Korhogo are presented in Figs. 9(a) and 9(b)
376 respectively. For Abidjan, Figs. 9(c) and 9(d) represent the sites with similar patterns of PM2.5
377 concentrations and the other sites respectively. Since PM10 and PM2.5 daily profiles are similar in
378 Abidjan, only PM2.5 concentrations are presented here. Note that the daily profiles of PM10
379 concentrations in Abidjan are shown in Figs. S5(a) and S5(b).

380 In Korhogo, all the sites show the same daily average concentration profiles for PM10 and PM2.5
381 respectively in Figs. 9(a) and 9(b), with two very important daily peaks. One peak in the morning
382 and one in the evening. These peaks are due to the mix of several local sources: mainly, household
383 sweepings (twice a day), use of firewood (morning and evening), and road traffic (dominated by
384 2-wheelers). Focusing on these peaks, PM2.5 morning and evening peaks are of similar importance
385 whereas PM10 peak is higher in the evening than in the morning. This could be due to favorable
386 conditions for dust resuspension due to the higher dryness of the soil in the evening caused by
387 sunshine during the day. According to Cheng and Li (2010) and Gualtieri et al., (2015),
388 meteorological parameters such as temperature and relative humidity influence particulate matter
389 concentrations. Moreover, the low values observed during the day, twenty times lower than in
390 morning could be due to a decrease of urban activities. However, it is interesting to note that such
391 low average concentrations observed in Korhogo are still higher than the WHO daily standard.
In Abidjan, Fig. 9(c) shows morning peaks of concentrations which are more important than those of the evening. Traffic source are mainly responsible of these peaks. The difference between the morning and evening peaks can be explained by the fact that the flow of road traffic in the evening is more spread out in time compared to that of the morning. In Fig. 9(d), some sites show no clear daily variation. For example, A₁₈ and A₂₀ sites show high concentrations throughout the day, characteristic of local economic activities. Indeed, the A₈, A₁₈ and A₂₀ sites are respectively located in a low-income neighborhood (mainly using wood and charcoal for cooking), in a neighborhood under construction and in an industrial area respectively. The daily minimum average concentrations are 19.3 µg m⁻³ for PM₁₀ and 14 µg m⁻³ for PM₂.₅. These low values are compensated by the high values observed during peaks (beyond 350 µg m⁻³ as well as PM₁₀ and PM₂.₅), thus providing a daily average concentration well above WHO standards. To conclude, this data collection methodology provides daily profiles of PM concentrations, which are important in the analysis of population exposure levels.

405

## Comparison with other Sub-Saharan Africa cities

Concentrations of PM₁₀ and PM₂.₅ in Korhogo and Abidjan are generally above the WHO daily exposure standard. As shown previously the local variability of these concentrations is strongly related to the predominant local sources.

Concentrations vary on average from 212.2 µg m⁻³ to 534.7 µg m⁻³ for PM₁₀ and from 57.4 µg m⁻³ to 230.4 µg m⁻³ for PM₂.₅ in Korhogo and in Abidjan, from 38.1 µg m⁻³ to 160.4 µg m⁻³ for PM₁₀ and from 23.8 µg m⁻³ to 113.4 µg m⁻³ for PM₂.₅. The mean standard deviations of measured concentrations are of 89 µg m⁻³ and 23.5 µg m⁻³ for PM₁₀ and of 41.5 µg m⁻³ and 17.9 µg m⁻³ for PM₂.₅ respectively in Korhogo and Abidjan. The standard deviation values are lower in Abidjan compared to Korhogo, which shows a greater variability in the latter. The maximum concentrations

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415
416 in Korhogo are respectively more than 3 to 5 times higher than in Abidjan whereas the minimum
417 are 2 to 3 times higher. As already mentioned, this may be explained by additional aerosol sources
418 occurring in Korhogo such as dust aerosol coming from northern African areas and/or from
419 unpaved roads and the 2-wheel emissions.

420 Table 2 presents the daily concentrations measured in African urban areas by this study,
421 compared to literature results for Sub-Saharan Africa. PM2.5 concentrations measured in this study
422 are lower than those measured with the individual exposures of Xu et al., (2019) in Abidjan.
423 Personal exposure measurements are generally closer to the emission sources than our sites.

424 The PM10 measurements performed by Ana et al., (2014) from January to March 2008 in Ibadan
425 (Nigeria) are on the same order of magnitude as the concentrations measured at Korhogo in January
426 2019. These PM10 concentration levels are characteristic of the dry season in West Africa. For the
427 same period of the year, Antonel and Chowdhury (2014) found lower PM10 concentrations (105 µg
428 m⁻³) in Bafossam (Cameroon), which is further south, in a forest area, therefore less exposed to
429 dust. Furthermore, the results obtained by Dionisio et al., (2010) showed that concentrations in
430 low-income areas are higher than in high-income areas in Accra, which is in agreement with the
431 results of this study. Finally, the ranges of concentrations obtained at Korhogo (in Tables S3 and
432 S) and Abidjan (in Tables S7 and S8) measurement sites are on the same order of magnitude as
433 those obtained in other studies carried out in Sub-Saharan Africa. Values from other studies
434 conducted in Africa are presented in Table S9 and agree very well with the results of this study.

435

436

CONCLUSIONS

437

438 This study provides an assessment of air quality in the cities of Korhogo and Abidjan in Cote
439 d'Ivoire by proposing for the first time to our knowledge, spatial variation maps of PM10 and PM2.5
440

concentrations. It is part of the objectives of the PASMU project, which aims to characterize urban
air pollution and its impact on health in Cote d'Ivoire.

441

442

The use of low-cost sensors at 14 sites in Korhogo and 20 in Abidjan resulted in daily average PM₁₀ concentrations ranging from 212.2 µg m⁻³ to 534.7 µg m⁻³ in Korhogo and from 38.1 µg m⁻³ to 160.4 µg m⁻³ in Abidjan. PM₂.₅ concentrations range from 57.4 µg m⁻³ to 230.4 µg m⁻³ and from 23.8 µg m⁻³ to 113.4 µg m⁻³ in Korhogo and Abidjan respectively.

443

444

445

446

The spatial variation maps obtained through the application of GIS in Korhogo show that the high activity areas (market and national bus station) have higher PM₁₀ and PM₂.₅ concentration levels than other areas. Our study also highlights that the relative contribution of PM₂.₅ to PM₁₀ is very different in Korhogo and Abidjan, varying from 50 to 100% in Abidjan, while 20 to 50% in Korhogo. In Abidjan, a difference between the concentrations is observed in high- and low-income neighborhoods, with higher concentration levels in low- and middle-income neighborhoods. One likely reason is that, high-income areas have better services (roads, waste collection, ...), and also use less polluting cooking methods (e.g. gas). This result can be also explained by the status of the city: (1) in Korhogo, the higher presence of coarse particles is due to anthropogenic (road dust resuspension) and natural sources (desert dust transport); (2) Abidjan is an economic capital with a higher population density, road traffic and generally a higher activity density than in Korhogo. The concentrations measured in this work are comparable to previous studies conducted in Abidjan and in Africa.

447

448

449

450

451

452

453

454

455

456

457

458

459

In conclusion, this study indicates that this simple and reproductive method provides a quality
assessment of particulate pollution at the city level. Note that this method could be also used for
other purposes such as performing a quick assessment of the impact of a new industrial unit or
infrastructure on the surrounding air quality. Finally, the original results obtained on urban

460

461

462
particulate pollution in Korhogo and Abidjan, relative contribution of fine particle to coarse and
daily variations of PM concentrations will be very useful for urban modelling in West African cities.

465

466 ACKNOWLEDGEMENTS

467

This project is funded by the Education and Research Ministry of Cote d'Ivoire, as part of the
Debt Reduction-Development Contracts (C2Ds) managed by Institut de Recherche pour le
Développement (IRD). The authors would like to thank AMRUGE-CI scholarship (Appui à la
Modernisation et à la Réforme des Universités et Grandes Ecoles de Côte d'Ivoire).

Special thanks to the populations and some institutions in Abidjan and Korhogo for hosting the
measuring equipment.

The authors acknowledge the contribution of all the members of the Aerosols and Pollution team
in Abidjan and Korhogo to this study, specially Doctor Alima Dajuma, and more generally the
Laboratoire de Physique de l'Atmosphère et de Mécanique des Fluides (LAPA-MF, Côte d'Ivoire)
and Laboratoire d'Aérologie (LA, France).

478

479 DISCLAIMER

480

The authors decline any commercial link with the companies producing equipment or software
used in this study.

483
REFERENCES

Adame, J.A., Hernández-Ceballos, M.Á., Sorribas, M., Lozano, A., Morena, B.A.D. la, 2014. Weekend-Weekday Effect Assessment for O3, NOx, CO and PM<sub>10</sub> in Andalusia, Spain (2003–2008). Aerosol Air Qual. Res. 14, 1862–1874. https://doi.org/10.4209/aaqr.2014.02.0026

Adon, A.J., 2019. Évaluation de l'impact sur la santé de l'aérosol de combustion pour différentes sources urbaines en Afrique de l'Ouest en saison sèche et humide : caractérisation physico-chimique et toxicologique. Université Toulouse III - Paul Sabatier, Toulouse.

Adon, A.J., Liousse, C., Doumbia, E.T., Baeza-Squiban, A., Cachier, H., Léon, J.-F., Yoboue, V., Akpo, A.B., Galy-Lacaux, C., Zoutien, C., Xu, H., Gardrat, E., Keita, S., 2020. Physico-chemical characterization of urban aerosols from specific combustion sources in West Africa at Abidjan in Côte d'Ivoire and Cotonou in Benin in the frame of DACCIWA program. Atmos. Chem. Phys. 1–69. https://doi.org/10.5194/acp-2019-406

AGEROUTE, 2020. Etendue du Réseau Routier Ivoirien [WWW Document]. URL https://ageroute.ci/index.php/gestion-du-reseau/reseau-routier/repartition-cartographie (accessed 3.14.20).

Ana, G., Odeshi, T., Sridhar, M., Ige, M., 2014. Outdoor respirable particulate matter and the lung function status of residents of selected communities in Ibadan, Nigeria. Perspect Public Health 134, 169–175. https://doi.org/10.1177/1757913913494152

Antonel, J., Chowdhury, Z., 2014. Measuring ambient particulate matter in three cities in Cameroon, Africa. Atmospheric Environment 95, 344–354. https://doi.org/10.1016/j.atmosenv.2014.06.053
Arku, R.E., Vallarino, J., Dionisio, K.L., Willis, R., Choi, H., Wilson, J.G., Hemphill, C., Agyei-Mensah, S., Spengler, J.D., Ezzati, M., 2008. Characterizing air pollution in two low-income neighborhoods in Accra, Ghana. Sci. Total Environ. 402, 217-231. https://doi.org/10.1016/j.scitotenv.2008.04.042

Bahino, J., Yoboué, V., Galy-Lacaux, C., Adon, M., Akpo, A., Keita, S., Liousse, C., Gardrat, E., Chiron, C., Ossohou, M., Gnamien, S., Djossou, J., 2018. 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

Boman, J., Lindén, J., Thorsson, S., Holmer, B., Eliasson, I., 2009. A tentative study of urban and suburban fine particles (PM2.5) collected in Ouagadougou, Burkina Faso. Xray Spectrom 38, 354-362. https://doi.org/10.1002/xrs.1173

Chai, T., Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE) – Arguments against avoiding RMSE in the literature. Geosci Model Dev 7, 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

Chatterjee, A., Engelen, R.J., Kawa, S.R., Sweeney, C., Michalak, A.M., 2013. Background error covariance estimation for atmospheric CO2 data assimilation. J. Geophys. Res. Atmos. 118, 10,140-10,154. https://doi.org/10.1002/jgrd.50654

Cheng, Y.-H., Li, Y.-S., 2010. Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station. Aerosol Air Qual. Res. 10, 456-462. https://doi.org/10.4209/aaqr.2010.04.0025

Clements, A.L., Griswold, W.G., Rs, A., Johnston, J.E., Herting, M.M., Thorson, J., Collier-Oxandale, A., Hannigan, M., 2017. Low-Cost Air Quality Monitoring Tools: From
Research to Practice (A Workshop Summary). Sensors 17, 2478.
https://doi.org/10.3390/s17112478

Cohen, A.J., Ross Anderson, H., Ostro, B., Pandey, K.D., Krzyzanowski, M., Künzli, N., Gutschmidt, K., Pope, A., Romieu, I., Samet, J.M., Smith, K., 2005. The Global Burden of Disease Due to Outdoor Air Pollution. J. Toxicol. Environ. Health Part A 68, 1301–1307.
https://doi.org/10.1080/15287390590936166

Dieme, D., Cabral-Ndior, M., Garçon, G., Verdin, A., Billet, S., Cazier, F., Courcot, D., Diouf, A., Shirali, P., 2012. Relationship between physicochemical characterization and toxicity of fine particulate matter (PM2.5) collected in Dakar city (Senegal). Environ. Res. 113, 1–13.
https://doi.org/10.1016/j.envres.2011.11.009

Dionisio, K.L., Rooney, M.S., Arku, R.E., Friedman, A.B., Hughes, A.F., Vallarino, J., Agyei-Mensah, S., Spengler, J.D., Ezzati, M., 2010. Within-Neighborhood Patterns and Sources of Particle Pollution: Mobile Monitoring and Geographic Information System Analysis in Four Communities in Accra, Ghana. Environ. Health Perspect. 118, 607–613.
https://doi.org/10.1289/ehp.0901365

Djossou, J., 2018. Analyse de la pollution atmosphérique en zones urbaines en Côte d'Ivoire et au Bénin dans le cadre du programme DACCIWA : Pollution particulaire.

Djossou, J., Léon, J.-F., Akpo, A.B., Liousse, C., Yoboué, V., Bedou, M., Bodjrenou, M., Chiron, C., Galy-Lacaux, C., Gardrat, E., Abbey, M., Keita, S., Bahino, J., Touré N&emsp;apos;Datchoh, E., Ossohou, M., Awanou, C.N., 2018. Mass concentration, optical depth and carbon composition of particulate matter in the major southern West African cities of Cotonou (Benin) and Abidjan (Côte d'Ivoire). Atmos. Chem. Phys. 18, 6275–6291.
https://doi.org/10.5194/acp-18-6275-2018
Doumbia, E.H.T., Liousse, C., Galy-Lacaux, C., Ndiaye, S.A., Diop, B., Ouafo, M., Assamoi, E.M., Gardrat, E., Castera, P., Rosset, R., Akpo, A., Sigha, L., 2012. Real time black carbon measurements in West and Central Africa urban sites. Atmospheric Environ. 54, 529-537.
https://doi.org/10.1016/j.atmosenv.2012.02.005

Egondi, T., Muindi, K., Kyobutungi, C., Gatari, M., Rocklöv, J., 2016. Measuring exposure levels of inhalable airborne particles (PM 2.5) in two socially deprived areas of Nairobi, Kenya. Environ. Res. 148, 500-506. https://doi.org/10.1016/j.envres.2016.03.018

Garrison, V.H., Majewski, M.S., Konde, L., Wolf, R.E., Otto, R.D., Tsuneoka, Y., 2014. Inhalable desert dust, urban emissions, and potentially biotoxic metals in urban Saharan–Sahelian air. Sci. Total Environ. 500–501, 383–394. https://doi.org/10.1016/j.scitotenv.2014.08.106

Gualtieri, G., Toscano, P., Crisci, A., Di Lonardo, S., Tartaglia, M., Vagnoli, C., Zaldei, A., Gioli, B., 2015. Influence of road traffic, residential heating and meteorological conditions on PM10 concentrations during air pollution critical episodes. Environ Sci Pollut Res 22, 19027-19038. https://doi.org/10.1007/s11356-015-5099-x

Jeannée, N., Mosqueron, L., Nedellec, V., Elichegaray, C., Bouallala, S., Desqueyroux, H., Guillaume, B., Liousse, C., Lagache, R., 2006. Évaluation de l'exposition en zones urbaines à la pollution atmosphérique : méthodes existantes et application aux PM10 en France métropolitaine. Pollution atmosphérique. https://doi.org/10.4267/pollution-atmospherique.1517

Jerez, S., Montavez, J.P., Jimenez-Guerrero, P., Gomez-Navarro, J.J., Lorente-Plazas, R., Zorita, E., 2013. A multi-physics ensemble of present-day climate regional simulations over the Iberian Peninsula. Clim Dyn 40, 3023-3046. https://doi.org/10.1007/s00382-012-1539-1

Kalisa, E., Archer, S., Nagato, E., Bizuru, E., Lee, K., Tang, N., Pointing, S., Hayakawa, K., Lacap-Bugler, D., 2019. Chemical and Biological Components of Urban Aerosols in Africa:
Current Status and Knowledge Gaps. Int. J. Environ. Res. Public Health 16, 941.
https://doi.org/10.3390/ijerph16060941

Keita, S., Liousse, C., Assamoi, E.M., Doumbia, E.H.T., Toure, N., Elguindi, N., Granier, C., Yoboue, V., 2020. African Anthropogenic Emissions Inventory for gases and particles from 1990 to 2015. in submission.

Kirenga, B., Meng, Q., van Gemert, F., Aanyu-Tukamuhebwa, H., Chavannes, N., Katamba, A., Obai, G., Molen, T., Schwander, S., Mohsenin, V., 2015. The State of Ambient Air Quality in Two Ugandan Cities : A Pilot Cross-Sectional Spatial Assessment. Int. J. Environ. Res. Public Health 12, 8075-8091. https://doi.org/10.3390/ijerph120708075

Liousse, C., Assamoi, E., Criqui, P., Granier, C., Rosset, R., 2014. Explosive growth in African combustion emissions from 2005 to 2030. Environ. Res. Lett. 9, 035003.

Liousse, C., Toure, N., Roblou, L., Baeza-Squiban, A., Annesi-Maesano, I., 2019. Dynamics-aerosol-chemistry-cloud interactions in West Africa - Inflammatory risk regional maps (No. D2.5).

McKeen, S., Wilczak, J., Grell, G., Djalalova, I., Peckham, S., Hsie, E.-Y., Gong, W., Bouchet, V., Menard, S., Moffet, R., McHenry, J., McQueen, J., Tang, Y., Carmichael, G.R., Pagowski, M., Chan, A., Dye, T., Frost, G., Lee, P., Mathur, R., 2005. Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004. J. Geophys. Res. 110, D21307. https://doi.org/10.1029/2005JD005858

MCLAU, 2015. Le projet de développement du Schema Directeur des transports urbains du grand Abidjan (Rapport Final No. Volume III). Ministère de la Construction, du Logement, de l'Assainissement et de l'Urbanisme.
Ministry of Transports, 2017. Ministère des Transports [WWW Document]. URL http://www.transports.gouv-ci/actualites/importation-et-exploitation-des-vehicules-importes-le-gouvernement-adopte-deux-decrets (accessed 3.16.20).

Murray, C.J.L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A.D., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J.A., Abdalla, S., Aboyans, V., Abraham, J., Ackerman, I., Aggarwal, R., Ahn, S.Y., Ali, M.K., AlMazroa, M.A., Alvarado, M., Anderson, H.R., Anderson, L.M., Andrews, K.G., Atkinson, C., Baddour, L.M., Bahalim, A.N., Barker-Collo, S., Barrero, L.H., Bartels, D.H., Basáñez, M.-G., Baxter, A., Bell, M.L., Benjamin, E.J., Bennett, D., Bernabé, E., Bhalla, K., Bhandari, B., Bikbov, B., Abdulhak, A.B., Birbeck, G., Black, J.A., Blencowe, H., Blore, J.D., Blyth, F., Bolliger, I., Bonaventure, A., Boufous, S., Bourne, R., Boussinesq, M., Braithwaite, T., Brayne, C., Bridgett, L., Brooker, S., Brooks, P., Brugha, T.S., Bryan-Hancock, C., Bucello, C., Buchbinder, R., Buckle, G., Budke, C.M., Burch, M., Burney, P., Burstein, R., Calabria, B., Campbell, B., Canter, C.E., Carabin, H., Carapetis, J., Carmona, L., Cella, C., Charlson, F., Chen, H., Cheng, A.T.-A., Chou, D., Chugh, S.S., Coffeng, L.E., Colan, S.D., Colquhoun, S., Colson, K.E., Condon, J., Connor, M.D., Cooper, L.T., Corriere, M., Cortinovis, M., de Vaccaro, K.C., Couser, W., Cowie, B.C., Criqui, M.H., Cross, M., Dabhadkar, K.C., Dahiya, M., Dahodwala, N., Damsere-Derry, J., Danaei, G., Davis, A., Leo, D.D., Degenhardt, L., Dellavalle, R., Delossantos, A., Denenberg, J., Derrett, S., Des Jarlais, D.C., Dharmaratne, S.D., Dherani, M., Diaz-Torne, C., Dolk, H., Dorsey, E.R., Driscoll, T., Duber, H., Ebel, B., Edmond, K., Elbaz, A., Ali, S.E., Erskine, H., Erwin, P.J., Espindola, P., Ewoigbokhan, S.E., Farzadfar, F., Feigin, V., Felson, D.T., Ferrari, A., Ferri, C.P., Fèvre, E.M., Finucane, M.M., Flaxman, S., Flood, L., Foreman, K., Forouzanfar, M.H., Fowkes, F.G.R., Fransen, M., Freeman, M.K., Gabbe, B.J., Gabriel, S.E., Gakidou, E., Ganatra, H.A., Garcia, B., Gaspari, F., Gillum, R.F., Gmel, G., Gonzalez-
Medina, D., Gosselin, R., Grainger, R., Grant, B., Groeger, J., Guillemin, F., Gunnell, D., Gupta, R., Haagsma, J., Hagan, H., Halasa, Y.A., Hall, W., Haring, D., Haro, J.M., Harrison, J.E., Havmoeller, R., Hay, R.J., Higashi, H., Hill, C., Hoen, B., Hoffman, H., Hotez, P.J., Hoy, D., Huang, J.J., Ibeanusi, S.E., Jacobsen, K.H., James, S.L., Jarvis, D., Jasrasaria, R., Jayaraman, S., Johns, N., Jonas, J.B., Karthikeyan, G., Kassebaum, N., Kawakami, N., Keren, A., Khoo, J.-P., King, C.H., Knowlton, L.M., Kobusingye, O., Koranteng, A., Krishnamurthi, R., Laden, F., Lalloo, R., Laslett, L.L., Lathlean, T., Leasher, J.L., Lee, Y.Y., Leigh, J., Levinson, D., Lim, S.S., Limb, E., Lin, J.K., Lipnick, M., Lipshultz, S.E., Liu, W., Loane, M., Ohno, S.L., Lyons, R., Mabweijano, J., MacIntyre, M.F., Malekzadeh, R., Mallinger, L., Manivannan, S., Marcenes, W., March, L., Margolis, D.J., Marks, G.B., Marks, R., Matsumori, A., Matzopoulos, R., Mayosi, B.M., McAnulty, J.H., McDermott, M.M., McGill, N., McGrath, J., Medina-Mora, M.E., Meltzer, M., Memish, Z.A., Mensah, G.A., Merriman, T.R., Meyer, A.-C., Miglioli, V., Miller, M., Miller, T.R., Mitchell, P.B., Mock, C., Mocumbi, A.O., Moffitt, T.E., Mokdad, A.A., Monasta, L., Montico, M., Moradi-Lakeh, M., Moran, A., Morawska, L., Mori, R., Murdoch, M.E., Mwaniki, M.K., Naidoo, K., Nair, M.N., Naldi, L., Narayan, K.M.V., Nelson, P.K., Nelson, R.G., Nevitt, M.C., Newton, C.R., Nolte, S., Norman, P., Norman, R., O'Donnell, M., O'Hanlon, S., Olives, C., Omer, S.B., Ortblad, K., Osborne, R., Ozgediz, D., Page, A., Pahari, B., Pandian, J.D., Rivero, A.P., Patten, S.B., Pearce, N., Padilla, R.P., Perez-Ruiz, F., Perico, N., Pesudovs, K., Phillips, D., Phillips, M.R., Pierce, K., Pion, S., Polanczyk, G.V., Polinder, S., Pope, C.A., Popova, S., Porrini, E., Pourmalek, F., Prince, M., Pullan, R.L., Ramaiah, K.D., Ranganathan, D., Razavi, H., Regan, M., Rehm, J.T., Rein, D.B., Remuzzi, G., Richardson, K., Rivara, F.P., Roberts, T., Robinson, C., De Leòn, F.R., Ronfani, L., Room, R., Rosenfeld, L.C., Rushton, L., Sacco, R.L., Saha, S., Sampson, U., Sanchez-Riera, L.,
Sanman, E., Schwebel, D.C., Scott, J.G., Segui-Gomez, M., Shahraz, S., Shepard, D.S., Shin, H., Shivakoti, R., Silberberg, D., Singh, D., Singh, G.M., Singh, J.A., Singleton, J., Sleet, D.A., Sliwa, K., Smith, E., Smith, J.L., Stapelberg, N.J., Steer, A., Steiner, T., Stolk, W.A., Stovner, L.J., Sudfeld, C., Syed, S., Tamburlini, G., Tavakkoli, M., Taylor, H.R., Taylor, J.A., Taylor, W.J., Thomas, B., Thomson, W.M., Thurston, G.D., Tleyjeh, I.M., Tonelli, M., Towbin, J.A., Truelsen, T., Tsilimbaris, M.K., Ubeda, C., Undurraga, E.A., van der Werf, M.J., van Os, J., Vavilala, M.S., Venketasubramanian, N., Wang, M., Wang, W., Watt, K., Weatherall, D.J., Weinstock, M.A., Weintraub, R., Weisskopf, M.G., Weissman, M.M., White, R.A., Whiteford, H., Wiebe, N., Wiersma, S.T., Wilkinson, J.D., Williams, H.C., Williams, S.R., Witt, E., Wolfe, F., Woolf, A.D., Wulf, S., Yeh, P.-H., Zaidi, A.K., Zheng, Z.-J., Zonies, D., Lopez, A.D., 2012. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 380, 2197–2223.
https://doi.org/10.1016/S0140-6736(12)61689-4

Ngo, N.S., Asseko, S.V.J., Ebanega, M.O., Allo'o Allo'o, S.M., Hystad, P., 2019. The relationship among PM2.5, traffic emissions, and socioeconomic status: Evidence from Gabon using low-cost, portable air quality monitors. Transp Res D Transp Environ 68, 2–9.
https://doi.org/10.1016/j.trd.2018.01.029

Nouaceur, Z., Murarescu, O., 2020. Rainfall Variability and Trend Analysis of Rainfall in West Africa (Senegal, Mauritania, Burkina Faso). Water 12, 1754.
https://doi.org/10.3390/w12061754

RGPH, 2014. Indicateurs Démographiques. Institut National de la Statistique.

Rivera-González, L.O., Zhang, Z., Sánchez, B.N., Zhang, K., Brown, D.G., Rojas-Bracho, L., Osornio-Vargas, A., Vadillo-Ortega, F., O'Neill, M.S., 2015. An assessment of air pollutant
exposure methods in Mexico City, Mexico. Journal of the Air & Waste Management Association 65, 581–591. https://doi.org/10.1080/10962247.2015.1020974

Savage, N.H., Agnew, P., Davis, L.S., Ordóñez, C., Thorpe, R., Johnson, C.E., O&apost;apos;Connor, F.M., Dalvi, M., 2013. Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation. Geosci. Model Dev. 6, 353–372. https://doi.org/10.5194/gmd-6-353-2013

Shukla, K., Kumar, P., Mann, G.S., Khare, M., 2020. Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustain. Cities Soc. 54, 101997. https://doi.org/10.1016/j.scs.2019.101997

Stern, D.I., van Dijk, J., 2017. Economic growth and global particulate pollution concentrations. Clim Change 142, 391–406. https://doi.org/10.1007/s10584-017-1955-7

Taylor, M.H., Losch, M., Wenzel, M., Schröter, J., 2013. On the Sensitivity of Field Reconstruction and Prediction Using Empirical Orthogonal Functions Derived from Gappy Data. J. Climate 26, 9194–9205. https://doi.org/10.1175/JCLI-D-13-00089.1

Val, S., Liousse, C., Doumbia, E.H.T., Galy-Lacaux, C., Cachier, H., Marchand, N., Badel, A., Gardrat, E., Sylvestre, A., Baeza-Squiban, A., 2013. Physico-chemical characterization of African urban aerosols (Bamako in Mali and Dakar in Senegal) and their toxic effects in human bronchial epithelial cells: description of a worrying situation. Particle and Fibre Toxicology 10, 10. https://doi.org/10.1186/1743-8977-10-10

Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., Biswas, P., 2015. Laboratory Evaluation and Calibration of Three Low-Cost Particle Sensors for Particulate Matter Measurement. Aerosol Sci Technol 49, 1063–1077. https://doi.org/10.1080/02786826.2015.1100710

Xu, H., Léon, J.-F., Liousse, C., Guinot, B., Yoboué, V., Akpo, A.B., Adon, J., Ho, K.F., Ho, S.S.H., Li, L., Gardrat, E., Shen, Z., Cao, J., 2019. Personal exposure to PM2.5 emitted from typical
anthropogenic sources in southern West Africa: chemical characteristics and associated health risks. Atmos. Chem. Phys. 19, 6637-6657. https://doi.org/10.5194/acp-19-6637-2019

Zamri, M., Ismail, M., Kim, Y., 2012. Mapping the Spatial Distribution of Criteria Air Pollutants in Peninsular Malaysia Using Geographical Information System (GIS), in: Khare, M. (Ed.), Air Pollution - Monitoring, Modelling and Health. InTech. https://doi.org/10.5772/32902

Zuidema, C., Stebounova, L.V., Sousan, S., Thomas, G., Koehler, K., Peters, T.M., 2019. Sources of error and variability in particulate matter sensor network measurements. J Occup Environ Hyg 1-11. https://doi.org/10.1080/15459624.2019.1628965
**List of Table titles**

**Table 1.** Interpolation error statistics, where RMSE is root mean absolute error in µg m⁻³; MAE is mean average error in µg m⁻³ and CV is mean of coefficient of variation of error in %.

**Table 2.** Comparison between PM₁₀ and PM₂.₅ daily concentrations obtained in this study and other studies Sub-Saharan Africa.
<table><caption>Table 1.</caption><thead><tr><th rowspan="2">Pollutants</th><th colspan="2">Korhogo</th><th colspan="2">Abidjan</th></tr><tr><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th></tr></thead><tbody><tr><td>Root Mean Absolute Error (RMSE)</td><td>97.0</td><td>43.2</td><td>41.5</td><td>24.9</td></tr><tr><td>Mean Average Error (MAE)</td><td>105.6</td><td>40.0</td><td>31.0</td><td>19.98</td></tr><tr><td>% Coefficient of Variation of error</td><td>33.4 ± 12.6</td><td>36.7 ± 31.7</td><td>42.5 ± 41.8</td><td>39.7 ± 29.2</td></tr></tbody></table>
<table><caption>Table 2.</caption><thead><tr><th>City, Country</th><th>Site characteristic</th><th>Samplings period</th><th>PM<sub>2.5</sub> (µg m<sup>-3</sup>)</th><th>PM<sub>10</sub> (µg m<sup>-3</sup>)</th><th>Reference</th></tr></thead><tbody><tr><td>Korhogo, Cote d'Ivoire</td><td>14 urban sites</td><td>Jan-2019</td><td>54.3 to 218</td><td>212.2 to 534.7</td><td></td></tr><tr><td>Abidjan, Cote d'Ivoire</td><td>20 urban sites</td><td>Jan. to Feb-2019</td><td>23.8 to 113.4</td><td>38.1 to 160.4</td><td>This study</td></tr><tr><td>Abidjan, Cote d'Ivoire</td><td>Urban (WB<sup>a</sup>)</td><td></td><td>494.3</td><td>-</td><td></td></tr><tr><td>Cotonou, Benin</td><td>Urban (DF<sup>b</sup>)</td><td>Jan. and July-2016</td><td>358.8</td><td>-</td><td>Xu et al., (2019)</td></tr><tr><td>Accra, Ghana</td><td>Traffic</td><td></td><td>335.1</td><td>-</td><td></td></tr><tr><td></td><td>Traffic-highest SES<sup>c</sup></td><td></td><td>21</td><td>49</td><td>Dionisio et al., (2010)</td></tr><tr><td></td><td>Traffic-lowest SES</td><td>2007</td><td>39</td><td>96</td><td></td></tr><tr><td>Dakar, Senegal</td><td>Urban</td><td>Jun-2008 to Jun-2009</td><td>44.4</td><td>-</td><td>Doumbia et al., (2012)</td></tr><tr><td></td><td>Commercial area</td><td></td><td>-</td><td>422.3</td><td></td></tr><tr><td>Ibadan, Nigeria</td><td>Traffic</td><td>Jan. and Mar-2008</td><td>-</td><td>328.1</td><td>Ana et al., (2014)</td></tr><tr><td></td><td>Industrial</td><td></td><td>-</td><td>319.7</td><td></td></tr><tr><td></td><td>Residential</td><td></td><td>-</td><td>257.4</td><td></td></tr><tr><td>Bamako, Mali</td><td>Urban</td><td>Sep. to Oct-2012 and Jul-2013</td><td>43</td><td>210</td><td>Garrison et al., (2014)</td></tr><tr><td>Ouagadougou, Burkina Faso</td><td>Urban</td><td>Nov. to Dec-2017</td><td>86</td><td>-</td><td>Boman et al., (2009)</td></tr><tr><td>Accra, Ghana</td><td>Traffic</td><td>Jun. to Jul-2006</td><td>27.4</td><td>71.8</td><td>Arku et al., (2008)</td></tr><tr><td>Bafoussam, Cameroun</td><td></td><td></td><td>67</td><td>105</td><td></td></tr><tr><td>Bamenda, Cameroun</td><td>Urban</td><td>Jan. to Mar-2012</td><td>132</td><td>141</td><td>Antonel and Chowdhury (2014)</td></tr><tr><td>Yaoundé, Cameroun</td><td></td><td></td><td>49</td><td>65</td><td></td></tr><tr><td>Libreville, Gabon</td><td></td><td></td><td>35.8</td><td>-</td><td>Ngo et al., (2019)</td></tr><tr><td>Port-Gentil, Gabon</td><td>Urban</td><td>Jun. to Jul-2015</td><td>60.9</td><td>-</td><td></td></tr></tbody></table>

<footnote>(a) WB: Waste Burning; (b) DF: Domestic Fire; (c) Social Economic Status; "-": not measured</footnote>
## Figure Captions

**Fig. 1.** Map of the population density of Cote d'Ivoire, the position of two application cities in Cote d'Ivoire is shown by green diamond. Green diamond represents each measurement cities. Data from https://www.diva-gis.org/gdata.

**Fig. 2.** Localization of measurement sites in Korhogo (Cote d'Ivoire). Green diamond represents each measurement site with its corresponding ID, described in the Table S1 and S3 and the black lines represent the boundaries of Korhogo city.

**Fig. 3.** Localization of measurement sites in Abidjan (Cote d'Ivoire). Green diamond represents each measurement site with its corresponding id site, described in the Table S2 and S4 and black lines represent the boundaries of communes included in this study.

**Fig. 4.** Wind rose from synoptic station measurements during the study periods in Korhogo (a) and Abidjan (b) in Cote d'Ivoire.

**Fig. 5.** Spatial variation of PM₁₀ (a) and PM₂.₅ (b) concentrations in Abidjan (Cote d'Ivoire). Black diamond represents each measurement site, line represent the main roads of Abidjan and dotted line represent administrative boundaries.

**Fig. 6.** Spatial variation of PM₁₀ (a) and PM₂.₅ (b) concentrations in Abidjan (Cote d'Ivoire). Black diamond represents each measurement site, line represent the main roads of Abidjan and dotted line represent administrative boundaries.

**Fig. 7.** Spatial variation of relative difference between PM₁₀ and PM₂.₅ in percentage in Korhogo (Cote d'Ivoire). Black diamond represents each measurement site.

**Fig. 8.** Spatial variation of relative difference between PM₁₀ and PM₂.₅ in percentage in Abidjan (Cote d'Ivoire). Black diamond represents each measurement site.

**Fig. 9.** Daily averages of PM₁₀ and PM₂.₅ concentrations in Korhogo (a, b) and PM₂.₅ concentrations in Abidjan (c, d).
<figure><img src="image_1.png" /><figcaption>Fig. 1.</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>Fig. 2.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 3.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 4.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig.5.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 6.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 7.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 8.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 9.</figcaption></figure>