Anthropogenic VOC in West Africa: from Source Quantification to Atmospheric Impacts

Anthropogenic VOC in Abidjan, southern West Africa: from source quantification to atmospheric impacts

Abstract

In the framework of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project, several field campaigns were deployed to measure a large range of atmospheric constituents. Here, we present the analysis of a comprehensive dataset integrating up to fifty-six VOCs from ambient sites and emission sources. VOCs were collected on sorbent tubes in Abidjan, Côte d’Ivoire, in February 2016 and later analysed by gas chromatography coupled with flame ionization and mass spectrometer detectors (GC-FID and GC-MS).

The similarity between VOC emission source profile and ambient profile suggests the strong impact of two wheelers and domestic fires on the atmospheric composition of Abidjan’s atmosphere. However, despite the high VOC concentrations observed near-source, relatively low levels were observed at the ambient sites (by a factor of 10 to 4000). Beyond photochemistry, reported high wind speeds suggest that meteorology is an important factor that regulates the concentration of gaseous pollutant in Abidjan. Indeed, a wide-ranging spatial variability in ambient concentrations was observed along with a southwest-east spatial gradient, consistent with the main wind direction reported during the field campaign. Emission ratios (VOC/CO) were established based on real-wold measurements achieved on a selected number of representative combustion sources. Maximal molar mass contributions were observed from two wheelers (TW) emissions, overpassing other regional sources by an average factor of 125±90. This source also largely governs the potential total contributions in terms of OH reactivity, secondary aerosol formation (SOAP) and ozone production (POCP). Regarding the speciation, while alkanes and aromatics generally dominated the molar mass contribution, monoterpenes rule the VOC-OH reactivity in most of the profiles under analysis.

Emission factors were used to retrieve and quantify VOC emissions from the main anthropogenic source sectors. Our detailed VOC emissions estimation suggests that the road transport sector is the dominant source in Cote d’Ivoire by emitting around 1200 Gg yr⁻¹ of gas-phase VOCs. These new estimations are 100 and 160 times larger than previous global inventory estimations like MACCity or Edgar. Additionally, the residential sector is also largely underestimated by a factor of 13 to 43. Moreover, the dearth of waste burning, fuel wood burning and charcoal representation in regional inventories urgently need to be addressed, particularly in low-income areas where these types of activities are important sources of VOC emissions. Thus, the quantification of emissions from local observations need to be addressed to provide not only reliable profiles but also realistic magnitudes. The establishment of emission ratios are useful constraints for a better quantification of regional emissions, especially in those regions where the dearth of emission information implies in widely misestimation by the global models. Consequently, the new emissions ratios established here will provide valuable constraints to improve regional to global estimations of anthropogenic VOCs and, therefore, the input for atmospheric models. Given the large underestimation of VOC emissions from transport and residential sectors for the only Côte d’Ivoire country there is an urgent need for such an effort in the whole West Africa region for building more realistic emission inventories.

Keywords: VOCs, emissions inventories, West Africa, air pollution, emission ratios.

 

1.  Introduction

Western Africa region, located in the north of the Gulf of the Guinea, is one of the most populated areas in Africa with more than 300 million of inhabitants in 2016 (United Nations, 2017). The increase in population by a factor of five since 1950, converted West Africa as the fastest growing of any of the world’s regions. Furthermore, future projections indicate an increase in the population density of developing countries, which will be much higher in Africa than in other parts of the world. The population of Africa could represent 40% of the world’s population in 2100 (United Nations, 2017). The explosive unplanned growth of urban conglomerations is the main issue in the region, with water access, air pollution, health problems and unregulated emissions pointed out as the principal shortcomings. Consequently, these emissions will produce diverse effects on atmospheric chemistry enlarged by intense photochemical conditions and dynamical atmospheric interactions. The atmospheric composition over West Africa is affected by air masses transported from remote sources, like aerosol dust from the Sahara desert or biomass burning aerosol and local urban pollution (Knippertz et al., 2017; Mari et al., 2011). Observations performed during the AMMA (African Monsoon Multidisciplinary Analyses) campaign have shown that air quality issues are mainly related to traffic and combustion emissions (Mari et al., 2011). Residential emissions in Southern West Africa (SWA) are attributed to charcoal and wood burning as the main source of domestic energy, widely used for cooking and heating activities. Biomass burning is a large source of carbonaceous aerosols and volatile organic compounds that can have effects in public health and in climate through the formation of secondary pollutants (Gilman et al., 2015; Knippertz et al., 2015; Sommers et al., 2014). Additionally, in most of SWA cities, traffic emissions are important sources of air pollution (Assamoi and Liousse, 2010). The road transport sector in largely disorganized due to the underdevelopment of road networks and to the absence of a policy regulation for public transport (Assamoi and Liousse, 2010). In this context, two-wheelers vehicles are widely use in the cities as short-distance and transport, replacing the public transport modes. Furthermore, the vehicular fleet has increased in the last year, which is characterized, in most of the cities, by a large number of old vehicles (Keita et al., 2018).

In the next years, African emissions from the combustion of fossil fuels, biofuels and refuse are predicted to increase considerably, and could represent about 50% of the global emissions of organic carbon (Knippertz et al., 2017; Liousse et al., 2014). Nevertheless, detailed emissions are still required for a better estimating of their impacts on climate change and health over this highly sensitive region (Knippertz et al., 2017).

Volatile organic compounds (VOCs) include a large quantity of species which can affect air quality by producing secondary pollutants such as ozone and secondary organic aerosols (Seinfeld and Pandis, 2006). Given the reactive nature of VOCs (Atkinson and Arey, 2003a), their emissions need to be disaggregated on species or species groups, for a better representation of their chemical features and thus, their impacts on the secondary formation processes. VOCs are important pollutants present in the urban atmospheres, therefore in-situ VOC observations are necessary to provide direct implications on air pollution exposure and to improve the prediction of secondary products formation.

Several field campaigns have been developed in the last twenty years all over the world with the purpose to identify VOC species and their fate in the atmosphere, as well as to quantify their emission sources (Bechara et al., 2010; Bon et al., 2011; Borbon et al., 2013; Brito et al., 2015; Dominutti et al., 2016; Kumar et al., 2018; Salameh et al., 2015; Wang et al., 2014; Warneke et al., 2016). Indeed, VOCs field observations have also been deeply used as constraints for the development of reliable emission inventories (Borbon et al., 2013; Salameh et al., 2016b). Some studies pointed out large discrepancies between inventory estimations and emission ratios derived from ambient measurements, implying some limitations in the accurate modelling of VOCs impact. In addition, there are still missing observations of VOCs in sensitive places with high anthropogenic pressure like Africa and South America (Huang et al., 2017). Particularly for Africa, the emission inventories frequently used are those developed for global scale and it involves numerous uncertainties (Keita et al., 2018; Liousse et al., 2014). The main differences are associated to the emission source estimations, source categories and spatiotemporal characterization. For VOCs, discrepancies up to a factor of 10, were observed in the comparison of emission inventories to the observations in Northern mid-latitude cities (Borbon et al., 2013; Boynard et al., 2014). Furthermore, global emission inventories commonly reported the total mass of VOCs. Nonetheless, the fate and contribution of each species can change depending on emission source, fuel quality, combustion technologies, and main regional activities (Huang et al., 2017). Therefore, the use of local activity data and emission factor derived from local measurements on specific sources may help reducing uncertainties in emission inventories. A recent study calculated the emission factors (EFs) of different compounds and activities in SWA (Keita et al., 2018). A comparison with the emission calculated from the EFs with those observed from EDGAR inventory showed a marked discrepancy, by a factor of 50, for Côte d’Ivoire. This work has demonstrated the urgency to consider African anthropogenic emissions at regional scales. African anthropogenic emissions require more detailed studies to reduce the uncertainties of emission estimation which currently cannot be assessed due to the scarcity of suitable data. Therefore, the characterization and quantification of these emissions, is a key input to improve the comprehension of anthropogenic and natural sources contribution in the atmospheric composition over SWA and to assess their impact on human and air quality conditions.

Several intensive field campaigns in the framework of the Dynamics-aerosol-chemistry-cloud-interactions in West Africa (DACCIWA) project were deployed in 2015 and 2016. Here, we present the results obtained from the VOC field campaigns at different sites in one of the major SWA city: Abidjan in Côte d’Ivoire, which included ambient and near-source measurements. Speciated VOCs were collected off-line by using sorbent tubes and then analysed and quantified at the laboratory applying gas chromatography-mass spectrometry and flame ionisation (GC-MS/FID) techniques. These data provide the first constraints for the construction of regional emission inventory and for the understanding of the role of VOC anthropogenic emissions in the regional atmospheric chemistry.

The aim of this work is to establish VOC speciated profiles of major anthropogenic sources in Abidjan by performing sampling close to emission sources in real-condition operation as far as possible. These sources include traditional and regional-specific ones, such us road transportation (gasoline, diesel, and two wheelers emissions), charcoal fabrication and burning emissions from domestic cooking fires, charcoal, landfill waste and fuel hardwood. A critical analysis of the profiles is also provided by comparing to ambient measurements performed in nine different sites in Abidjan. All the measurements were performed in different periods during wet and dry seasons (2015 and 2016). VOC ambient concentrations are also compared with those from other cities worldwide to discuss discrepancies and commonalities. The dataset obtained in our study provide substantial information enabling the quantification of VOC emissions for several sources in Cote d’Ivoire. These sources profiles are analysed here and contrasted with those provided by global emission inventories. Finally, the impact due to the use of region-specific sources is tackled regarding the implication on air quality in terms of reactivity and secondary pollutants formation.

2. Materials and Methods

As part of the DACCIWA project (Knippertz et al., 2015) intense field campaigns were performed in 2015 and 2016, focusing for the first time on the most populated southern coastal region of West Africa. The DACCIWA campaign had an emphasis on atmospheric composition, including questions of air pollution and cloud-aerosol interactions (Knippertz et al., 2015). Here we present the results obtained from intensive ambient measurements in Abidjan and the extended VOC speciation acquired from source emission measurements (Keita et al., 2018). These results are part of the activities that were developed under the work package 2 (WP2): Air pollution and Heath, which aims to link and quantify emission sources, air pollution and related health impacts over differentiated urban sources in West Africa (https://www.dacciwa.eu).

 

2.1 Sampling

The field campaigns were conducted in Abidjan, Côte d’Ivoire during summer and winter time and there were focused on the strategic directions of the DACCIWA WP2. Firstly, direct source emission measurements were performed to obtain emission profiles from the main sources in Abidjan, SWA. They integrated a wide range of human activities susceptible of VOCs emissions. The assessed sources combine traditional ones like road transportation and regional-specific of SWA such as domestic waste-burning, charcoal fabrication, charcoal-burning as well as fuel wood-burning (Table1). Part of these results for a selected number of VOC (fifteen species), BC and particles were already discussed in a recent published paper (Keita el al, 2018).

• For the road transportation analysis different vehicle exhausts tailpipe measurements were carried out. Samples integrate heavy-duty diesel vehicles (HDDV, trucks and buses), light-duty diesel vehicles (LDDV, diesel cars), light-duty gasoline vehicles (LDGV, gasoline cars), two-wheelers 2 strokes (TW 2T) and two-wheelers 4 strokes (TW 4T) vehicles. Differences in motorizations (gasoline and diesel) and the fleet age have been considered.
• Regarding domestic waste-burning (WB), samples were obtained at the official domestic landfill site located at the east of Abidjan (AD, Figure 1 and Table 2). The sampling was performed inside the waste burning plume to integrate the different combustion processes involved.
• Charcoal-burning (CH) and fuel wood-burning (FW) are commonly cooking and heating practices in African urban areas. FW emissions were obtained by measuring the fire plume of tropical African hardwood, specifically Hevea (Hevea brasiliensis). FW and CH were burned in two types of stoves traditionally used in the SWA region for cooking, which are made of metal and of baked earth. The measurements included all the combustion phases (Keita et al., 2018).
• The charcoal-making (CHM) profile was obtained by measuring emissions from traditional kilns, that use different types of dense wood. The kiln was covered with a layer of leaves and a layer of soil of about 10 cm thick. The smoke was sampled at the holes made in the CHM kiln, which are located in the horizontal plane and provide the air circulation for the pyrolysis propagation (pictures of this source are shown in Keita et al., 2018).

All samples were obtained in the emission plume at around 1–1.5m from the source, except for vehicles where samples were taken at the tailpipe outlet while the vehicle’s engine idling. The EF values estimated from different samples were later averaged for every source and each VOC species. For the road transport sector, the equivalent means were calculated considering the fleet characteristics in Côte d’Ivoire, as detailed in(Keita et al., 2018).

Secondly, VOC ambient measurements were collected to analyse the spatial distribution of these compounds from different sites in Abidjan, Cote d’Ivoire. Ambient measurements were performed in nine different sites, which are showed in Figure 1. The distribution of the sampling points was selected to cover the main sources and activities developed in the city. The selection was also in agreement with the near-source measurements, previously described. Therefore, residential, road transport, domestic fires, waste burning, industrial and background sites were selected. The characteristics and geographical location of each site are reported in Table 2. The ambient campaigns were conducted for 1 month (February 2016) and samples were collected once a week at different daytime. The sampling strategy selected for that was the integration of the volume sample by measuring several times in the same sorbent tube at each ambient site. In this way, a short-time sampling was conducted at different daytime for a total volume of 600 mL.

2.2 Analytical instrumentation

Active sampling of VOCs using a manual pump (Accuro 2000, Dräger) was carried out on sorbent cartridges due to their easily handle and to the possibility to obtain profiles from a large spatial area. Cartridges deployed for VOC measurements were composed of Tenax TA 60-80 mesh (250 mg) or Carbopack C (150 mg). The combination of both sorbent material allowed the sampling of 10 aromatics (C6-C9), 22 n-alkanes (C5-C16), 10 monoterpenes, 7 aldehydes, isoprene and other oxygenated compounds. All compounds are reported in Table SM1. Prior to the sampling, multi-sorbent filled cartridges were conditioned by flowing purified nitrogen through them, at a flow of 100 mL.min^-1, during 5 hours at 320 °C.

Duplicate measurements were performed and lately analysed in two different laboratories to investigate the reproducibility of analytical techniques and with the purpose to acquire a wide range of VOC species. The analysis of the Tenax TA tubes was performed at the Laboratoire de Météorologie Physique (LaMP, Clermont-Ferrand, France) by using a gas chromatograph-mass spectrometer system (GC/MS, Turbomass Clarus 600, Perkin Elmer®) coupled to an automatic thermal desorption (Turbomatrix ATD).  Each tube was desorbed at 270 °C during 15 min at a flow rate of 40 mL. min^-1 and pre-concentrated on a second trap, at -30°C containing Tenax TA. After the cryofocussing, the trap was rapidly heated to 300°C (40°. s^-1) and the target compounds were flushed into the GC. Due to the high concentrations in some samples, special splits were adopted, split in 5 mL.min^-1 and split out 2 mL min^-1. The analytical column applied was a PE-5MS (5% phenyl – 95% PDMS, 60m×0.25mm×0.25μm) capillary column (Perkin Elmer) and the temperature profile of the GC was ramped to guarantee the VOCs separation (35°C for 5 minutes, heating at 8°C min⁻¹ to 250°C, hold for 2 minutes). The mass spectrometer was operated in a Total Ion Current (TIC) from 35 to 350 m/z amu. Chromatography parameters were optimized to enable good separation of fifteen identified compounds by a complete run of 34 minutes on each cartridge. Calibration was performed by analysing conditioned cartridges doped with known masses of each compound, present in standard low-ppb-level gas solutions, purchased from the National Physical Laboratory (NPL, UK). The cartridges were then analysed with the aforementioned method and calibration curves were obtained for each compound, as it was already discussed in Keita et al., (2018).

Carbopack tubes analysis were carried out by applying a gas chromatograph-flame ionization detector (ATD-GC-FID, Perkin Elmer) system at the SAGE Department (IMT Lille Douai). The cartridges were previously thermo-desorbed at 350 °C during 15 minutes with a helium flow of 20 mL.min^-1. This method allowed the separation and identification of up to 56 compounds, from C5–C16 VOCs, including 7 carbonyls, 4 ketones, 10 monoterpenes and 6 VOCs of intermediate volatility. More details of this technique can be found else-where (Ait-Helal et al., 2014; Detournay et al., 2011). The application of both methods allowed the comparison of common compounds that were measured in ambient sites and sources (benzene, toluene, ethylbenzene, m+p-xylene, o-xylene, trimethylbenzenes, n- heptane, iso-octane, n-octane, α-pinene, β-pinene, limonene, isoprene) and the analysis of analytical techniques’ performance. Likewise, the combination of different sorbent tubes and analytical strategies allowed the quantification of a higher number of VOC species, and therefore an extensive analysis of source contributions. The uncertainties and quality control parameters for both analytical methods can be found else-were (Detournay et al., 2011; Keita et al., 2018)

2.3 Metrics and calculations

Several calculations were implemented to assess the VOC emissions and their impacts in Abidjan. Here, we provide the equation basis for each parameter investigated. Firstly, the emission factors (EF) which were previously obtained for a restricted number of VOC species (Keita et al., 2018), were also computed for the whole comprehensive VOC database (56 compounds). EF were later use for the estimation of VOC emissions in Cote d’Ivoire for each source under study, further described in section 3.4. Secondly, the emission ratios (ER) of each VOC species related to CO for all the emission sources were established. Finally, the reported ER were subsequently used to evaluate the implications on atmospheric reactivity by applying common VOC metrics.

2.3.1 Emission factors

VOC emission factors were estimated from the concentrations measured for all the emission sources described above, as follows

EFVOC= ∆VOC∆CO+ ∆CO2 x MWVOC12x fc x 103                                               (1)

where EF (VOC) is the emission factor of the specific VOC in gram per kilogram of burned fuel (g kg⁻¹);

∆VOC= [VOC]emission − [VOC]background is the VOC mixing ratio in the emission and background air, respectively; MW_VOC is the molar weight of the specific VOC (in g mol⁻¹), 12 is the molar weight of carbon (g mol⁻¹) and fc is the mass fraction of carbon in the fuel analysed. The fc values used were obtained from literature and applied to each source. The EF for fifteen VOCs were already published and more details about the method can be found else-where (Keita et al; 2018). Here, we applied the same method for the whole VOC database.

2.3.2 Emission ratios

Emission ratios (ER) were obtained for each VOC compound related to carbon monoxide (CO), and there were calculated as follows:

ER=VOC ppbv∆CO ppmv                                                                                (2)

We selected CO as a combustion tracer because most of VOCs and CO are co-emitted by the target sources.  Furthermore, ratios to CO are regularly reported in the literature for biomass burning and urban emissions (Baker et al., 2008; Borbon et al., 2013; Brito et al., 2015; Gilman et al., 2015; de Gouw et al., 2017; Koss et al., 2018; Wang et al., 2014), which can be useful for further comparisons. CO₂ emissions were also measured in order to analyse the modified combustion efficiency (MCE), already discussed in the  study (Keita et al., 2018). Emission ratios were calculated in units of ppb_v of VOC per ppm_v of CO, which is equivalent to a molar ratio (mmol VOC per mol CO). Molar mass (MM) emissions ratios were also computed. MM (µgm⁻³) is the mass emitted per ppm_v CO, obtained from the ER previously calculated and then converted by using the VOC molecular weight (g mol−1), and the molar volume (24.86 L at 1 atm and 30 ◦C). Table SM1 includes the emission ratios obtained for each VOC and MW values used.

2.3.3 VOC-OH reactivity

VOC-OH reactivity represents the sink reaction of each VOC with the hydroxyl radical (OH) and is equal to

VOCOH reactivity =ER x kOH x A  ,                                                             (3)

where ER is the emission ratio for each VOC related to CO (ppb_v per ppm_v), kOH is the second-order reaction rate coefficient of the specific VOC with the hydroxyl radical (x10-12 cm³ molec⁻¹ s⁻¹), and A is a molar concentration conversion factor (2.46×10¹⁰ molec cm⁻³ ppb_v⁻¹ at 1 atm and 25 ◦C)(Gilman et al., 2015). kOH values for all VOC species were obtained from Atkinson and Arey (2003a) and from the NIST Chemical Kinetics Database (Manion et al., 2015).

2.3.4 Ozone formation potential

The ozone formation potential represents the ability of each VOC to produce tropospheric ozone and it was calculated as follows

 

VOC-ozone formation potential=ER x POCP,                                                (4)

where the ER is the emission ratio of each VOC related to CO (ppb_v of VOC per ppm_v of CO) and POCP is the photochemical ozone creation potentials developed by Derwent et al (Derwent et al., 2007, 2010a; Jenkin et al., 2017). POCP values were obtained by deploying the Master Chemical Mechanism (MCM), a highly detailed chemical mechanism, applied on a realistic air mass trajectory from urban sites. This model estimates the change in ozone production by incrementing the mass emission of each VOC (Derwent et al., 1998).  POCPs for an individual VOC is estimated by quantifying the effect of a small increase in its emission on the concentration of the modelled ozone formed, respective to that resulting from the same increase in the emission of ethene (POCP value for ethene is therefore 100). In this study POCP values were also analysed on VOC family basis, which were obtained from a recent study (Huang et al., 2017) or adapted from individual POCP values.

2.3.5 SOA formation potential

The secondary organic aerosol formation potential represents the propensity of each VOC to form organic aerosols and is equal to

SOA-VOC formation potential=ER x SOAP,                                               (5)

where ER is the emission ratio for each measured VOC related to CO (ppb_v of VOC  per ppm_v of CO) and SOAP is a non-dimensional model-derived SOA formation potential (Derwent et al., 2010b; Gilman et al., 2015). All SOAP values represent the modelled mass of organic aerosol formed per mass of VOC reacted on an equal mass emitted basis relative to toluene. Toluene was selected as the reference compound due to its well-known emissions and it is usually documented as an important anthropogenic SOA precursor (Derwent et al., 2010b).

ER, kOH, SOAP and POCP values for each VOC are detailed in Table SM1, In absence of those values for specific compounds, we estimated the values (indicated in Table SM1 by a +) by using those of comparable compounds based on similar chemical properties, as suggested in the study of Gilman et al (2015).

2.4 Ancillary data

Meteorological observations were provided by the NOAA Integrated Surface database (ISD; https:// www.ncdc.noaa.gov/isd for more details). Daily rainfall, air temperature and wind speed and direction measurements were recorded at the Abidjan Felix Houphouet Boigny Airport. Figure 1 gives the coordinates location of the meteorological station and ambient sampling sites.

3. Results and discussion

3.1 Local and synoptic meteorological conditions

Abidjan is the economic capital of Côte d’Ivoire with a population of 6.5 million (in 2016), representing more than 20 per cent of the overall population of the country (United Nations, 2017). Characterized by a high level of industrialization and urbanization, Abidjan together with the autonomous district encompass an area of 2,119 km². In summer, West Africa region is influenced by monsoon phenomenon which is mainly driven by the surface pressure contrast between the relatively cool waters of the tropical Atlantic Ocean and the Saharan heat low (Knippertz et al., 2017). This seasonal circulation characterized the wet (summer) and dry (winter) periods in the region. During the dry season (November to February) most of the region is dominated by dry north-easterly winds from the Sahara and the precipitation is confined to the coast, where the sea-breeze circulation provides moister air and produces near-surface convergence. The monsoon starts its development and south-westerly moist winds begin to enter deeper into the continent producing more clouds and precipitation between July and August, the strong pressure and temperature gradients between the Atlantic Ocean and the Sahara, drive the strong monsoon flow together with south-westerlies reaching  higher latitudes, up to 20° N (Knippertz et al., 2015).

Meteorological data from Abidjan, Côte d’Ivoire are reported in Figure 2. Weekly accumulated precipitation and weekly air temperature means were analysed during the year of field campaigns. Meteorological conditions in Abidjan are also characterized by the monsoon phenomenon and it establishes two well defined seasons: a wet season between March to August and a dry season from November to February. The weekly mean air temperature observed was between 24.6 and 29.4 °C, reaching a maximum during the beginning of the wet season (Figure 2). The precipitation pattern shows an enlarged rate during the monsoon period, however, this year negative anomalies were observed compared with previous years (Knippertz et al., 2017). Wind patterns observed during the field campaign period showed a predominant contribution from the south-westerly with maximum speed during daytime reaching up to 13 m.s^-1. The high wind speed records reported in Abidjan are larger than those observed in other polluted urban atmospheres (Dominutti et al., 2016; Salameh et al., 2016a; Zhang et al., 2014). Deroubaix et al (2018) analysed the dispersion of urban plumes from SWA coastal cities. In this study, the inland transport of anthropogenic coastal pollutants together with biomass burning emission was observed and their later northward transport into continental SWA. The proximity of Abidjan to the ocean and the intrusion of the sea-breeze circulation can facilitate the dispersion processes and, consequently, the urban emissions dilution.

3.2 Ambient concentrations and spatial distribution of VOC in Abidjan

The location coordinates and the main activities related to each sampling point are detailed in  REF _Ref521493680 h  * MERGEFORMAT Table 2Table 2, as well as their spatial distribution can be observed in Figure 1. The concentration sum of the fifteen quantified VOCs in the ambient samples ranged from 6.25 to 72.13 µg.m^-3. Higher VOC concentrations were reported in KSI, BIN CRE and PL sites (Figure 3, pie chart sizes Figure 5) and, in the overall, anthropogenic VOCs dominated the ambient profiles by a factor of 5 to 20 compared to biogenic .

BTEX (benzene, toluene, ethylbenzene, and m+p and o-xylenes), a subgroup of aromatics VOC, are emitted by fossil fuel combustion from transport and residential sources and evaporation processes such as fuel storage and solvent uses (Borbon et al., 2018). Figure 3 reports the VOCs spatial distribution at each site, detailed by the BTEX composition. BTEX compounds were pointed out as a significant fraction of VOCs in urban atmospheres reaching up to 47% in volume (Borbon et al., 2018; Boynard et al., 2014; Dominutti et al., 2016). In Abidjan, their contribution ranged from 30% to 76 % of the total VOC burden measured in the ambient sites (Figure 3). In an overall perspective, benzene predominates the ambient profiles. However, its contribution as well as these from other BTEX, varied according to the sampling sites. Higher BTEX fractions were observed near landfill (AD), industrial (ZI YOP) and traffic (ABO) sites. The divergences in their spatial distribution could be related to the main activities that are involved in the emission of these compounds, since the provided dilution rate remains constant in the atmosphere. Figure 4 shows the BTEX distribution and the ambient profiles composition observed in Abidjan. It can be noted a similar BTEX distribution profile between some specific sites, such as KSI and BIN and AT, PL and FAC, for instance. Moreover, the levels of concentration increase considerably from the westward to the eastward of the Abidjan district. The spatial variability in ambient measurements was already pointed out by recent studies performed in Abidjan, (Bahino et al., 2018; Djossou et al., 2018). Not only a similar spatial gradient was observed in ozone concentrations (Bahino et al., 2018) but also maximum aerosols concentrations were reported near domestic fires (KSI, Figure 1) and landfill sites (AD, Figure 1), indicating that open-air burning activity could be a large contributor to air pollution, by a factor of 3 and 5, respectively. The commonalities in spatial distribution seems to be related to the predominant wind direction frequency from the south-western direction (Figure 2a). This wind pattern was already observed in the West Africa region, when the urban plumes of the main coastal cities were analysed. The dispersion and transport of the urban plumes from Abidjan were predominantly directed into north- to eastward directions, reaching a distance up to 200km from the city over the course of the 24 hours model simulations (Knippertz et al., 2017).

The mean ambient concentrations observed in Abidjan were compared with those reported in previous studies, performed in other cities worldwide (Figure 4). Globally, a good agreement is observed with the mean VOC concentrations in most of the cities evaluated showing the same distribution, except for Karachi where larger VOC levels were reported (Barletta et al., 2005). Toluene, 124-TMB and o-xylene indicate lower mean concentrations in relation with those reported in other cities. However, toluene and xylenes also presented an appreciable variability considering all the sampling sites. Main VOC sources in Abidjan encompass road transportation activities and biomass burning processes. It is important to highlight that VOC from road transport emissions accompanying the fuel uses, vehicular technologies and age of the fleet and all these factors can affect the urban atmospheric composition. Furthermore, the coupling of regional-specific emissions together with the photochemistry and atmospheric dynamics need to be further investigated to better understand the ageing and processing of VOC over SWA region).

3.2 Ambient measurements vs emission source profiles

A comparative approach was carried out between ambient and sources measurement composition with the purpose to identify analogous VOC profiles and to evaluate the effect of emission sources. This analysis provided a general overview on the VOC distribution by assessing a limited number of fifteen species that were commonly measured in all the campaigns. Afterwards, an evaluation integrating the extensive VOC dataset is addressed here, by comparing the emission ratios profiles (in molar mass basis) for all the studied sources.

Figure 5 reports the relative mass contribution of VOC profiles observed at the nine urban sites together with those obtained from the emission sources. Toluene, xylenes and 124-TMB presented the highest variability in concentration among anthropogenic VOCs and limonene among biogenic VOC. Trimethylbenzenes (124-TMB, 135-TMB and 123-TMB), mainly observed in road transportation emissions, display a dissimilar profile showing higher fractions from sources than those observed in ambient sites (Figure 5). These discrepancies should be related to the short lifetime of these compounds (around 4 hours), with a reaction rate within 1.8 to 8.8 (x10^-15 cm³ molecule^-1 s^-1, (Atkinson and Arey, 2003a)). This condition implies a faster a reaction in the atmosphere and the losses of these species from emission to receptor. On the other hand, ambient profiles showed. in most of the cases, higher contributions of alkanes, terpenes and isoprene. It is noteworthy the presence of terpenes and isoprene in the profile of all the emission sources, mainly in those associated to burning processes, such us charcoal burning, waste burning and fuel wood burning (Figure 5). The terpenes emissions from biomass burning were already pointed out in several studies, as common species emitted by the combustion processes (Gilman et al., 2015; Simpson et al., 2011). Additional studies performed on African biomass emissions also reported concentrations of limonene and α-pinene higher than isoprene (Jaars et al., 2016; Saxton et al., 2007). This pattern is similarly observed in our study where considerable limonene and pinenes concentrations were measured.

Globally, aromatic compounds represent the higher fraction from ambient and sources profiles, contributing from 60 to 98 per cent (Figure 5). Comparing emission versus ambient profiles, we found a similarity with the two wheelers and domestic fires profiles like FW and CH sources. Notwithstanding that, the VOC ambient profiles obtained from the nine sites did not show a contrasted difference despite the dissimilarities in the activities conducted nearby.

3.2.1 VOC molar mass contribution from Abidjan sources.

Emission ratios (ER) were computed for each VOC identified related to CO concentrations measured on the emission sources (Table SM1). Since CO is considered an anthropogenic tracer and its emission sources are relatively well characterized, VOC to CO ERs were computed to facilitate the integration of this new dataset into models and emission studies. To evaluate the composition and intensity of SWA emissions we analysed here their contribution regarding molar mass related to VOC emission ratios. For this analysis, an extensive VOC database of 56 species was considered, including 12 species of terpenes, VOCs of intermediate volatility (IVOCs from C11–C16 n-alkanes), ketones and carbonyl compounds for the sources under study (Table 1 and Table SM1). Species groups were classified in agreement with GEIA groups (Huang et al., 2017) respecting the chemical function of each VOC family (Table SM2). In this way, ER were also grouped by VOC family from individual values obtained (Table SM1). Figure 6 shows the relative molar mass profiles for each source measured in Abidjan. Aromatic molar masses ruled the distribution for most of the sources, which jointly range from 26 % to 98 % of the total ER molar mass. The control of these compounds is predominantly observed in gasoline-fuelled vehicles, like LDGV and TW sources and diesel light duty vehicles (LDDV).

Alkanes (VOC5+VOC6) comprise also noticeable molar mass fraction, dominating in TW2T, HDDV and charcoal related sources (by a 40, 47 and 53% respectively). Regarding the VOCs of intermediate volatility (IVOCs), they show a considerable contribution in the emission of HDDV sources, where the VOC6 fraction is dominated by IVOCs by a 30% (of 47% for the total VOC6 fraction).

Interestingly, terpenes reported a 11%, 13% and 22% contribution in FW, HDDV and WB sources, respectively (Figure 7b-c). The presence of terpenes in biomass burning sources was already pointed out as the most important compounds together with furans and aromatics, in chamber experiments (Koss et al., 2018). Nevertheless, to the extent of our knowledge, their presence in road transport or open waste burning in SWA emissions remains unexplored. Regarding OVOCs, in general they are emitted in a smaller fraction (less than 7%) apart from HDDV which provide the 11% of the total molar mass. Previous studies have been reported the OVOCs as the main contribution fraction in biomass burning emissions (Akagi et al., 2011; Gilman et al., 2015; Yokelson et al., 2013). A dissimilar pattern is observed in this study, likely related to the limitation of VOCs species measurements by the analytical method deployed, which allows the quantification of a limited number aldehydes (>C6). Sekimoto and co-workers also analysed the VOC emission profiles depending on the pyrolysis temperature, showing an enrichment of terpenes and non-aromatic oxygenates under high temperature conditions and an increase in oxygenated aromatics under low temperature fires (Sekimoto et al., 2018).

To analyse absolute contribution and potential impacts related to African emissions, four sources were selected (TW2T, HDDV, WB and CH) representing the main sectors in the region (road transportation, waste burning and charcoal emissions). Figure 7 (a-d)shows the relative composition and the total molar mass of the measured VOC (µg m⁻³) emitted per ppm_v CO. TW2T sources disclosed the highest molar mass emissions (4680 ± 512 µg m⁻³ ppm_v CO⁻¹, Figure 7a-d), 10 to 200 times higher than any other source here analysed.

While aromatics seems to dominate the molar mass fraction of ERs for most of the sources, their contributions are dissimilar, govern by benzene and toluene in burning-related sources and by xylenes and trimethylbenzenes in traffic-related ones. Despite the non-controlled sampling and the restricted number of VOC species attained by our study, they represent a meaningful dataset for the first estimation of ER in SWA region.

3.3 Implications on atmospheric reactivity

3.3.1 VOC-OH reactivity

It is relevant to evaluate the significance of VOCs in terms of atmospheric reactivity, since these species are important precursors in the photochemical formation of tropospheric O₃ and SOA. We analyse here the OH reactivity to evaluate the contribution of each VOC measured in the emission sources, to therefore identify key VOC reactive species that should contribute to the photochemical processing over SWA. The estimation was based on the calculated OH reactivity by using the reaction rate coefficient (Atkinson and Arey, 2003b; Manion et al., 2015) of each VOC species, as detailed in Equation 3, and then aggregated into VOC families. Estimations for all the sources were performed, however, only the results for the selected sources (TW2T, HDDV, WB and CH) are discussed here. Figure 7(e-h) shows the fractional contributions and total VOC-OH reactivity per ppm_v of CO for the sources analysed. The largely highest total reactivity is observed from the emissions of TW2T (488 ± 43 s^-1 ppm_v CO^-1), outpacing other sources within a factor of 7 to 170. This disclosed difference is related to the high ER observed for the more reactive species, like terpenes (VOC11) and C8- and C9-aromatics (VOC15 and VOC16 respectively). Terpenes and aromatics altogether suggest being the dominant sink of OH, contributing with 47 to 87% of the total calculated OH reactivity. Individually, terpenes ruled the OH-reactivity in open waste burning emissions (76%) and heavy-duty diesel vehicles (60%) (Figure 7f-g). In charcoal burning emissions it may be noted the fractional contribution of aldehydes (VOC22, 13%) and higher alkanes (VOC6, 28%). The modest presence of alkenes in the VOC-OH fractional analysis, well-known for their high reactivity’s effects, is related to the limitation of the sampling method which restricts the collection of light alkenes species.

3.3.2 Ozone formation potential

The oxidation of VOCs is often initiated by reaction with the hydroxyl radical (^.OH), which in the presence of NO_x (NO+NO₂) leads to the photochemical formation of O₃. The estimation of ozone formation was evaluated by using the POCP scale, developed by Derwent (Derwent et al., 2010a) as expressed in the equation 4. Overall, the fractional ozone formation distribution is dominated by aromatics (VOC13 to VOC17) in all the sources, by 38 to 63 per cent. Alkanes represent an important contribution in charcoal burning, HDDV and TW2T, accounting for 45, 28 and 26 per cent, respectively. It is important to note the terpenes contribution, not only from burning sources but also from road transportation sector (Figure 7i-l). Aldehydes are well-known due to their high reactivity in the atmosphere (Atkinson and Arey, 2003a; Sommariva et al., 2011), and some of these species have been shown to have a large impact on ozone formation and chemistry. In our estimation, we can observe the contribution of these compounds mainly from diesel (HDDV) and charcoal burning sources (CH).

The total potential ozone was computed for each source, showing a largely dominant contribution from TW-2T (80343 POCP ppm_v CO^-1), 13, 24 and 150 times higher than the potential impact in ozone formation derivate from the HDDV, WB and CH emissions, respectively.

3.3.3 SOA formation potential

Figure 7 (m-p) shows the composition and mean SOA formation potentials of VOC families emitted by each selected source. As it can be expected charcoal burning reports the lowest SOAP (335 SOAP per ppm_v CO^-1), compared to TW2T, HDDV and WB sources whose SOAPs values are 147, 10 and 9 times greater, respectively. It may be noted that, globally, aromatics governed the SOA formation in our estimations, by 98 to 72 per cent. Interestingly, terpenes represented a minor contribution in the SOA formation, presenting SOAP index lower than those for aromatics species, representing approximately 20% of the SOAP for toluene. Despite the well-known role of biogenic VOCs as SOA precursors (Ait-Helal et al., 2014), the method used here is not able to correctly quantify their contributions in SOA formation. The differences between SOAP values and measured aerosols yield were already pointed out by Gilman and co-authors, who performed some sensitivity tests in order to bring in line SOAP and aerosols yields (Gilman et al., 2015). We performed here the same analysis, by adjusting the SOAPs terpenes values 10% higher. The results in total SOAP per ppm_v of CO, did not show considerable increases in any of the sources, expanding the total SOAP up to 1%. Similar results were observed for fractional distribution, where the changes in terpenes SOAPs (VOC11) did not show any substantial change in the VOC contribution for SOA formation. These findings are in agreement with those identified in the study of Gilman et al (2015), suggesting an underestimation in the factional contribution of terpenes in the potential formation of organic aerosols over SWA region.

3.4 Quantification of VOC emissions

The VOC emissions were estimated from our measurements, considering the extensive dataset of fifty-six compounds. For that, VOC emissions were estimated by bringing the emission factors obtained in near-source measurements together with the statistical IEA activity data (International Energy Agency (IEA)) available for the different sources (Keita et al., 2018). To compute the emission factors the equation 1 was used, considering all the VOC species measured and including the mass fraction of each fuel (fc) obtained from the literature. Additionally, the differences in motorizations and the fleet age have been considered, as well as the fleet distribution by calculating the equivalent vehicular fleet as described in Keita et al. (2018). Afterwards, the mean road transportation and residential profiles for Cote d’Ivoire were computed and compared with two referenced global inventories, EDGAR and MACCity.

A first insight on these estimations was provided in the work of Keita and co-authors, by analysing discrepancies and commonalities with the emissions estimated by the EDGAR v4.3.2 global inventory (http://edgar.jrc.ec.europa.eu) In this previous study, the selected VOC database disclosed a large disagreement for the road transport sector by a factor of 50 (Keita et al., 2018), only considering fifteen VOC species. Here, we report the mean road transport profile by considering the complete VOC dataset including the 56 compounds analysed, incorporating aldehydes, IVOCs and terpenes species. Mean residential emissions are also integrated and compared with those from Edgar inventory. Additionally, we incorporate the residential and road transport profiles provided by MACCity inventory (Granier et al., 2011) available in the ECCAD-GEIA database (http://eccad.aeris-data.fr). The main differences between both global inventories are related to the speciation level for VOCs families. MACCity considered all the aromatics in the same VOC group, thus we provide here the sum of VOC13 to VOC17 families (Table SM2) to compare it whit aromatics group from this inventory.

Figure 8 exhibits the profiles obtained for Côte d’Ivoire along with those provided by emission inventories. Globally, the discrepancies already highlighted in the previous analysis are exacerbated by introducing the complete VOC database. Residential profiles showed a disparity in total VOC emissions by a factor of 14 with Edgar v4.3.2 and by a factor of 43 compared with MACCity (Figure 8a). In terms of composition, the main differences observed in residential profiles are related to VOC22 group (aldehydes), which discloses a greater contribution in the EDGAR inventory, accounting for 64% of the total emission, 5 times higher than our estimation. It should be noted that there is also a disparity in the contribution from aromatics (sum of VOC13 to VOC17) and alkenes (VOC12) which reveal a larger influence in the MACCity profile (58 and 22 per cent, respectively) (Figure 8a). This disparity could be related to the few VOC species that were analysed for the VOC12 group in our study. Nevertheless, aromatics dominate the fractional contribution on the updated emission inventory (39%), especially by toluene (VOC14) and C8-aromatics (VOC15) (11 and 10 %, respectively). Alkanes (>VOC6 alkanes) disclose a larger contribution in the residential profile, in which IVOCs contribute by 20% of the total alkanes estimated.

Regarding road transportation sector, the comparison with global inventories depicted a factor 100 and 160 times lower in total emissions for Edgar and MACCity profiles, respectively (Figure 8a). A somewhat agreement is observed with reference to the fractional composition between emission inventories and measurements (Figure 8b). Maximal contributions from all profiles are governed by aromatics and alkanes in different proportions. Our estimations report the greater contributions in C8-aromatics (VOC15), C9-aromatics (VOC16) and toluene (VOC14), ranging for 25, 14 and 10 per cent, respectively (Figure 8c and Figure 9). Whereas Edgar v4.3.2 discloses a contribution of 9% for VOC15, 3.5 % for VOC16 and 13% for VOC14 (Figure 9). Road transport profiles also reproduce the anomalies in the VOC12 contribution observed in residential sector, presenting greater emissions in the global inventories.

A similar profile is observed for higher alkanes (VOC6) which present a quite analogous contribution between our estimation and Edgar emissions (34 and 37 per cent, respectively; Figure 8b). Nevertheless, the alkanes (VOC5+VOC6) contribution in MACCity profiles prevails on road transport emissions accounting for 62%.

Interestingly, terpenes and isoprene emissions can be denoted in the both profiles analysed in Côte d’Ivoire inventory (VOC11 and VOC10). Despite the reduce contribution of these species in both sectors (9 and 4 per cent in residential and road transport), the underestimation of them in the emission from anthropogenic sources could have consequences in the atmospheric chemistry. Since the reactivity is specific for each VOC, the inaccuracies in the speciation also have implications in the estimation of their impacts. Specifically, for terpenes, it can be noted the contribution in the k_OH reactivity, accounting for 42% in residential sector and 28% in road transport reactivity (Figure 8c).

Figure 9 also displays the residential and road transportation profiles obtained from Côte d’Ivoire compared with Edgar v4.3.2 profiles for Europe. We highlight here the substantial differences in total emissions, outpacing those estimated for Europe by a factor of 3 for road transport and by a factor of 6 for residential sector (433 and 198 Gg year^-1, respectively). These differences are meaningful due to the Cote d’Ivoire emissions are ate the same level to those estimated for the European continent.

The dearth of measurements and source profile data in Africa was previously pointed out in the development of the EDGAR inventory, which leads to considering the priority of this region for future inventory improvements (Huang et al., 2017). Our results emphasise the first insights obtained in the work of Keita et al (2018) where the large underestimation of anthropogenic VOCs emissions by emission inventories was revealed. Even thought, our VOC database is not extensive for all the species emitted by the sources analysed, the incorporation of new VOC species reinforces the usefulness of in situ measurements under real conditions to derive realistic emission factors and subsequent estimation of representative emission profiles.

3.5 Anthropogenic emissions of terpenes, IVOCs and aldehydes in SWA

As it was previously highlighted, the presence of terpenes commonly emitted by biogenic sources was observed in the emissions of anthropogenic sources. These emissions are completely neglected by global emission inventories; however, they could have considerable effects in the atmospheric chemical processing, by producing secondary pollutants in the atmosphere. Figure 10a reports the fractional distribution of terpenes in the several emission sources analysed. Main contributions are associated to the emissions from waste burning (WB, 47%), two wheelers (TW2T, 20%), wood burning (FW, 17%) and charcoal making (CHM, 14%) sources. The total annual emission estimated for these compounds, which represents 334 Gg. year^-1 and 11% of the total emissions, cannot be neglected. Evaluating the distribution by terpenes species among the emission sources, a dissimilar pattern can be noted (Figure 11). While terpenes emissions from road transport are mainly dominated by α-ocimene and α-terpinolene, limonene and isoprene are controlled by wood burning sources. The main wood burning species in Côte d’Ivoire are Hevea (Hevea brasiliensis) and Iroko(Milicia excelsa), which are widely use in urban domestic fires for cooking, heating and other services (Keita et al., 2018). In our study we only present the results obtained from Hevea, a tropical African hardwood, characterised as a monoterpenes emitter species (Bracho-Nunez et al., 2013; Wang et al., 2007). The main monoterpenes compounds naturally emitted by hevea species are sabinene, limonene and α-pinene (Bracho-Nunez et al., 2013). The isoprene emissions from non-isoprene emitting species were already observed in biomass burning studies, which indicates that isoprene is formed during combustion process (Hatch et al., 2015).

As it can be noted in Figure 11, isoprene emissions are also impacted by vehicles, mainly TW sources, and camphene and β-pinene emissions by HDDV sources. The anthropogenic sources of isoprene have been documented in urban areas, mainly associated to traffic emissions (Borbon et al., 2001; von Schneidemesser et al., 2011). However, to the best of our knowledge, no previous studies had ever been analysed the presence of monoterpenes from road transportation sources. Α-pinene and β-pinene emissions are ruled by charcoal burning fires, which also contribute in some fraction to the emissions of isoprene and limonene. In contrast, charcoal making emissions are dominated by γ-terpinene and isoprene. The results from biomass burning sources provided here were obtained from non-controlled experiments, which did not allow to evaluate the divergences between the emissions from each combustion phase (pyrolysis, flaming and smouldering). Further investigation is needed for a better understanding of these differences and for the characterization of all the combustion phases.

VOCs of intermediate volatility are suspected to be efficient precursors of secondary organic aerosol (SOA). However, as it was discussed in the section 3.3.3, our method was not able to resolve the differences between VOC families and most of the SOAP was assigned to aromatic compounds (up to 98%). Figure 11b reports the fractional contribution and total emissions of IVOCs. CHM, FW, HDDV and TW represent the main sources of these compounds, accounting for 58, 15, 12 and 11 per cent of the total, respectively. Despite their lower emissions compared to aromatics or terpenes, IVOCs are estimated to account for 80Gg/year emissions in Côte d’Ivoire. A recent study observed that fine particles in Abidjan are three times higher than the concentrations recommended by the World Health Organization (Djossou et al., 2018). Hence, a better understanding on the aerosol precursors and formation processes is substantial for the later reduction of their concentrations in the urban atmosphere.

Oxygenated compounds were previously indicated as important species in the emissions from burning sources (Gilman et al., 2015; Hatch et al., 2015; Koss et al., 2018; Wiedinmyer et al., 2014). In addition, oxygenated compounds like non-aromatics oxygenated, were the dominant fraction in burning emission sources including a range of functional groups, of which alcohols and carbonyls were the most abundant (Koss et al., 2018; Stockwell et al., 2015).  Figure 11c shows that aldehydes emissions are mainly governed by charcoal fabrication (CHM), two wheelers (TW) and wood burning sources (Figure 11c). In our study. the quantified aldehydes represent only 5.5% of the total emissions of the country (170 Gg/year). However, they can be important compounds in terms of reactivity and ozone formation. Hence, a further analysis of oxygenated compounds together with furans and other nitro-oxygenated compounds need to be address in future campaigns, to improve not only the quantification of these compounds but also a better identification of the African tracers from biomass burning processes.

 

4. Summary and conclusions

The present study reports for the first time a chemically detailed range of VOCs including C5-C16 alkanes, monoterpenes, alkenes, aromatics and carbonyls compounds by using sorbent tubes during an intensive field campaign in Abidjan, SWA. We present here an original dataset integrating main emission sources and ambient measurements from nine representative sites and covering the urban spatial distribution of VOCs in Abidjan. The overview of urban air composition resulted in a VOC profile which is mostly affected by biomass burning and traffic emissions. The highest concentrations were observed near domestic fires, landfill fires and traffic sites, in agreement with the results reported in previous studies, where gas-phase and aerosols pollutants were measured (Bahino et al., 2018; Djossou et al., 2018).

The establishment of emission ratios are important metrics to constraint the estimations provide by global emission inventories. Emission ratios from regional-specific emission sources were established here and later used for the analysis of fractional molar mass contribution and for the estimation of potential VOC-OH reactivity, ozone and secondary organic aerosol formation. The distribution of VOC emissions (magnitude and composition) was different for each source evaluated. Two wheelers and heavy-duty sources presented the largest total molar mass emissions, while charcoal burning the lowest. The sources related to burning processes, such as waste and wood burning, also presented important contribution in VOCs emissions. These sources represent commonly activities developed in Abidjan, suspected to contribute with a large quantity of VOC emissions over the SWA region. Regarding VOC speciation, molar mass contributions were mostly dominated by aromatics and alkanes compounds. Globally, aromatics ruled the ozone formation potential and the SOA formation potential. However, the SOA metrics applied here was not able to accurately analyse the contribution from other important SOA precursors, such as monoterpenes. Notwithstanding that, monoterpenes governed the contribution in the VOC-OH reactivity in most of the sources analysed.

In order to compare the magnitude of emissions in Côte d’Ivoire, emissions factors were obtained for the exhaustive VOC database and then emissions were estimated. Road transportation and residential profiles were obtained and compared whit those reported in global emission inventories, such as MACCity and Edgar. Our results showed a discrepancy up to a factor of 43 and 160 for residential and transport profiles when compared with both referenced inventories. The emissions obtained for Côte d’Ivoire y showed relevant levels, outpacing European emissions up to a factor of 6. Interestingly, monoterpenes emissions were observed in anthropogenic sources emissions from biomass burning to road transportation sources contributing with up to 340 Gg/year in the annual emissions. These compounds, as well as other VOC species, are generally missing in the global emission profiles affecting the estimation of air quality impacts considering their role in the VOC-OH reactivity. Therefore, the quantification of emissions from local observations need to be addressed to provide not only reliable profiles but also realistic magnitudes.

This study in the framework of the DACCIWA project, allowed us to identify and quantify several VOCs together in ambient sites and emission sources for the first time in Abidjan, Côte d’Ivoire. Our results provide important constraints for the estimation of air quality impacts and atmospheric composition over SWA region, as well as for the development of more realistic regional emission inventories. Nevertheless, we also highlight the importance of continuing ambient and emissions measurements in Cote d’Ivoire and other West Africa countries. Future studies should also integrate continuous sampling techniques to ensure the analysis of a wide range of VOCs, as well as their temporal profiles.

 

Acknowledgements

This work has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 603502 (EU project DACCIWA: Dynamics-aerosol-chemistry-cloud interactions in West Africa). P Dominutti acknowledges the Postdoctoral Fellowship support from the Université Clermont Auvergne and thanks the grant received from the CAPES program from the Ministry of Education of Brazil, during 2015-2016. Thierry Leonardis and Stéphane Sauvage are thanked for the contribution in the analysis of VOC sorbent tubes, graciously performed at the SAGE Department at IMT Lille Douai (France).

Data availability. After the DACCIWA embargo period, the data used in this study will be available on the SEDOO database

Competing interests. The authors declare that they have no conflict of interest.

 

References

AIRPARIF: Surveillance de la qualite de l’air en Ile-de-France., [online] Available from: http://www.airparif.asso.fr/ (Accessed 1 March 2018), 2013.

Ait-Helal, W., Borbon, A., Sauvage, S., De Gouw, J. A., Colomb, A., Gros, V., Freutel, F., Crippa, M., Afif, C., Baltensperger, U., Beekmann, M., Doussin, J. F., Durand-Jolibois, R., Fronval, I., Grand, N., Leonardis, T., Lopez, M., Michoud, V., Miet, K., Perrier, S., Prévôt, A. S. H., Schneider, J., Siour, G., Zapf, P. and Locoge, N.: Volatile and intermediate volatility organic compounds in suburban Paris: Variability, origin and importance for SOA formation, Atmos. Chem. Phys., 14(19), 10439–10464, doi:10.5194/acp-14-10439-2014, 2014.

Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D. and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11(9), 4039–4072, doi:10.5194/acp-11-4039-2011, 2011.

Assamoi, E.-M. and Liousse, C.: A new inventory for two-wheel vehicle emissions in West Africa for 2002, Atmos. Environ., 44(32), 3985–3996, doi:10.1016/j.atmosenv.2010.06.048, 2010.

Atkinson, R. and Arey, J.: Atmospheric Degradation of Volatile Organic Compounds, Chem. Rev., 103(12), 4605–4638, doi:10.1021/cr0206420, 2003a.

Atkinson, R. and Arey, J.: Gas-phase tropospheric chemistry of biogenic volatile organic compounds: a review, Atmos. Environ., 37(2), 197–219, doi:10.1016/S1352-2310(03)00391-1, 2003b.

Bahino, J., Yoboué, V., Galy-Lacaux, C., Adon, M., Akpo, A., Keita, S., Liousse, C., Gardrat, E., Chiron, C., Ossohou, M., Gnamien, S. and Djossou, J.: A pilot study of gaseous pollutants’ measurement (NO2, SO2, NH3, HNO3 and O3) in Abidjan, Côte d’Ivoire: contribution to an overview of gaseous pollution in African cities, Atmos. Chem. Phys., 18(7), 5173–5198, doi:10.5194/acp-18-5173-2018, 2018.

Baker, A. K., Beyersdorf, A. J., Doezema, L. a., Katzenstein, A., Meinardi, S., Simpson, I. J., Blake, D. R. and Sherwood, F. R.: Measurements of nonmethane hydrocarbons in 28 United States cities, Atmos. Environ., 42(1), 170–182, doi:10.1016/j.atmosenv.2007.09.007, 2008.

Barletta, B., Meinardi, S., Simpson, I. J., Khwaja, H. a, Blake, D. R. and Rowland, F. S.: Mixing ratios of volatile organic compounds (VOCs) in the atmosphere of Karachi, Pakistan, Atmos. Environ., 36(21), 3429–3443, doi:10.1016/S1352-2310(02)00302-3, 2002.

Barletta, B., Meinardi, S., Sherwood, F. R., Chan, C.-Y., Wang, X., Zou, S., Yin Chan, L. and Blake, D. R.: Volatile organic compounds in 43 Chinese cities, Atmos. Environ., 39(32), 5979–5990, doi:10.1016/j.atmosenv.2005.06.029, 2005.

Bechara, J., Borbon, A., Jambert, C., Colomb, A. and Perros, P. E.: Evidence of the impact of deep convection on reactive Volatile Organic Compounds in the upper tropical troposphere during the AMMA experiment in West Africa, Atmos. Chem. Phys., 10(21), 10321–10334, doi:10.5194/acp-10-10321-2010, 2010.

Bon, D. M., Ulbrich, I. M., de Gouw, J. a., Warneke, C., Kuster, W. C., Alexander, M. L., Baker, A., Beyersdorf,  a. J., Blake, D., Fall, R., Jimenez, J. L., Herndon, S. C., Huey, L. G., Knighton, W. B., Ortega, J., Springston, S. and Vargas, O.: Measurements of volatile organic compounds at a suburban ground site (T1) in Mexico City during the MILAGRO 2006 campaign: measurement comparison, emission ratios, and source attribution, Atmos. Chem. Phys., 11(6), 2399–2421, doi:10.5194/acp-11-2399-2011, 2011.

Borbon, A., Fontaine, H., Veillerot, M., Locoge, N., Galloo, J. C. and Guillermo, R.: An investigation into the traffic-related fraction of isoprene at an urban location, Atmos. Environ., 35(22), 3749–3760, doi:10.1016/S1352-2310(01)00170-4, 2001.

Borbon, A., Gilman, J. B., Kuster, W. C., Grand, N., Chevaillier, S., Colomb, A., Dolgorouky, C., Gros, V., Lopez, M., Sarda-Esteve, R., Holloway, J., Stutz, J., Petetin, H., McKeen, S., Beekmann, M., Warneke, C., Parrish, D. D. and de Gouw, J. A.: Emission ratios of anthropogenic volatile organic compounds in northern mid-latitude megacities: Observations versus emission inventories in Los Angeles and Paris, J. Geophys. Res. Atmos., 118(4), 2041–2057, doi:10.1002/jgrd.50059, 2013.

Borbon, A., Boynard, A., Salameh, T., Baudic, A., Gros, V., Gauduin, J., Perrussel, O. and Pallares, C.: Is Traffic Still an Important Emitter of Monoaromatic Organic Compounds in European Urban Areas?, Environ. Sci. Technol., 52(2), 513–521, doi:10.1021/acs.est.7b01408, 2018.

Boynard, A., Borbon, A., Leonardis, T., Barletta, B., Meinardi, S., Blake, D. R. and Locoge, N.: Spatial and seasonal variability of measured anthropogenic non-methane hydrocarbons in urban atmospheres: Implication on emission ratios, Atmos. Environ., 82, 258–267, doi:10.1016/j.atmosenv.2013.09.039, 2014.

Bracho-Nunez, A., Knothe, N. M., Welter, S., Staudt, M., Costa, W. R., Liberato, M. A. R., Piedade, M. T. F. and Kesselmeier, J.: Leaf level emissions of volatile organic compounds (VOC) from some Amazonian and Mediterranean plants, Biogeosciences, 10(9), 5855–5873, doi:10.5194/bg-10-5855-2013, 2013.

Brito, J., Wurm, F., Yáñez-Serrano, A. M., de Assunção, J. V., Godoy, J. M. and Artaxo, P.: Vehicular Emission Ratios of VOCs in a Megacity Impacted by Extensive Ethanol Use: Results of Ambient Measurements in São Paulo, Brazil, Environ. Sci. Technol., 49(19), 11381–11387, doi:10.1021/acs.est.5b03281, 2015.

Deroubaix, A., Menut, L., Flamant, C., Brito, J., Denjean, C., Dreiling, V., Fink, A., Jambert, C., Kalthoff, N., Knippertz, P., Ladkin, R., Mailler, S., Maranan, M., Pacifico, F., Piguet, B., Siour, G. and Turquety, S.: Diurnal cycle of coastal anthropogenic pollutant transport over southern West Africa during the DACCIWA campaign, Atmos. Chem. Phys. Discuss., (August), 1–44, doi:10.5194/acp-2018-766, 2018.

Derwent, R. G., Jenkin, M. E., Saunders, S. M. and Pilling, M. J.: Photochemical ozone creation potentials for organic compounds in northwest Europe calculated with a master chemical mechanism, Atmos. Environ., 32(14–15), 2429–2441, doi:10.1016/S1352-2310(98)00053-3, 1998.

Derwent, R. G., Jenkin, M. E., Passant, N. R. and Pilling, M. J.: Reactivity-based strategies for photochemical ozone control in Europe, Environ. Sci. Policy, 10(5), 445–453, doi:10.1016/j.envsci.2007.01.005, 2007.

Derwent, R. G., Jenkin, M. E., Pilling, M. J., Carter, W. P. L. and Kaduwela, A.: Reactivity scales as comparative tools for chemical mechanisms., J. Air Waste Manag. Assoc., 60(8), 914–924, doi:10.3155/1047-3289.60.8.914, 2010a.

Derwent, R. G., Jenkin, M. E., Utembe, S. R., Shallcross, D. E., Murrells, T. P. and Passant, N. R.: Secondary organic aerosol formation from a large number of reactive man-made organic compounds, Sci. Total Environ., 408(16), 3374–3381, doi:10.1016/j.scitotenv.2010.04.013, 2010b.

Detournay, A., Sauvage, S., Locoge, N., Gaudion, V., Leonardis, T., Fronval, I., Kaluzny, P. and Galloo, J.-C.: Development of a sampling method for the simultaneous monitoring of straight-chain alkanes, straight-chain saturated carbonyl compounds and monoterpenes in remote areas., J. Environ. Monit., 13(4), 983–90, doi:10.1039/c0em00354a, 2011.

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., N’Datchoh, E. T., Ossohou, M. and Awanou, C. N.: 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(9), 6275–6291, doi:10.5194/acp-18-6275-2018, 2018.

Dominutti, P. A., Nogueira, T., Borbon, A., Andrade, M. de F. and Fornaro, A.: One-year of NMHCs hourly observations in São Paulo megacity: meteorological and traffic emissions effects in a large ethanol burning context, Atmos. Environ., 142, 371–382, doi:10.1016/j.atmosenv.2016.08.008, 2016.

Gilman, J. B., Lerner, B. M., Kuster, W. C., Goldan, P. D., Warneke, C., Veres, P. R., Roberts, J. M., De Gouw, J. A., Burling, I. R. and Yokelson, R. J.: Biomass burning emissions and potential air quality impacts of volatile organic compounds and other trace gases from fuels common in the US, Atmos. Chem. Phys., 15(24), 13915–13938, doi:10.5194/acp-15-13915-2015, 2015.

de Gouw, J. A., Gilman, J. B., Kim, S. W., Lerner, B. M., Isaacman-VanWertz, G., McDonald, B. C., Warneke, C., Kuster, W. C., Lefer, B. L., Griffith, S. M., Dusanter, S., Stevens, P. S. and Stutz, J.: Chemistry of Volatile Organic Compounds in the Los Angeles basin: Nighttime Removal of Alkenes and Determination of Emission Ratios, J. Geophys. Res. Atmos., 122(21), 11,843-11,861, doi:10.1002/2017JD027459, 2017.

Granier, C., Bessagnet, B., Bond, T., D’Angiola, A., Denier van der Gon, H., Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., Klimont, Z., Kloster, S., Lamarque, J.-F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T., Raut, J.-C., Riahi, K., Schultz, M. G., Smith, S. J., Thompson, A., van Aardenne, J., van der Werf, G. R. and van Vuuren, D. P.: Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period, Clim. Change, 109(1–2), 163–190, doi:10.1007/s10584-011-0154-1, 2011.

Hatch, L. E., Luo, W., Pankow, J. F., Yokelson, R. J., Stockwell, C. E. and Barsanti, K. C.: Identification and quantification of gaseous organic compounds emitted from biomass burning using two-dimensional gas chromatography-time-of-flight mass spectrometry, Atmos. Chem. Phys., 15(4), 1865–1899, doi:10.5194/acp-15-1865-2015, 2015.

Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C., Dore, C., Guizzardi, D., Muntean, M., Schaaf, E. and Friedrich, R.: Speciation of anthropogenic emissions of non-methane volatile organic compounds: A global gridded data set for 1970-2012, Atmos. Chem. Phys., 17(12), 7683–7701, doi:10.5194/acp-17-7683-2017, 2017.

Jaars, K., Beukes, J. P., Van Zyl, P. G., Venter, A. D., Josipovic, M., Pienaar, J. J., Vakkari, V., Aaltonen, H., Laakso, H., Kulmala, M., Tiitta, P., Guenther, A., Hellén, H., Laakso, L. and Hakola, H.: Ambient aromatic hydrocarbon measurements at Welgegund, South Africa, Atmos. Chem. Phys., 14(13), 7075–7089, doi:10.5194/acp-14-7075-2014, 2014.

Jaars, K., Van Zyl, P. G., Beukes, J. P., Hellén, H., Vakkari, V., Josipovic, M., Venter, A. D., Räsänen, M., Knoetze, L., Cilliers, D. P., Siebert, S. J., Kulmala, M., Rinne, J., Guenther, A., Laakso, L. and Hakola, H.: Measurements of biogenic volatile organic compounds at a grazed savannah grassland agricultural landscape in South Africa, Atmos. Chem. Phys., 16(24), 15665–15688, doi:10.5194/acp-16-15665-2016, 2016.

Jenkin, M. E., Derwent, R. G. and Wallington, T. J.: Photochemical ozone creation potentials for volatile organic compounds: Rationalization and estimation, Atmos. Environ., 163(x), 128–137, doi:10.1016/j.atmosenv.2017.05.024, 2017.

Keita, S., Liousse, C., Yoboué, V., Dominutti, P., Guinot, B., Assamoi, E.-M., Borbon, A., Haslett, S. L., Bouvier, L., Colomb, A., Coe, H., Akpo, A., Adon, J., Bahino, J., Doumbia, M., Djossou, J., Galy-Lacaux, C., Gardrat, E., Gnamien, S., Léon, J. F., Ossohou, M., N’Datchoh, E. T. and Roblou, L.: Particle and VOC emission factor measurements for anthropogenic sources in West Africa, Atmos. Chem. Phys., 18(10), 7691–7708, doi:10.5194/acp-18-7691-2018, 2018.

Knippertz, P., Evans, M. J., Field, P. R., Fink, A. H., Liousse, C. and Marsham, J. H.: The possible role of local air pollution in climate change in West Africa, Nat. Clim. Chang., 5(9), 815–822, doi:10.1038/nclimate2727, 2015.

Knippertz, P., Fink, A. H., Deroubaix, A., Morris, E., Tocquer, F., Evans, M. J., Flamant, C., Gaetani, M., Lavaysse, C., Mari, C., Marsham, J. H., Meynadier, R., Affo-Dogo, A., Bahaga, T., Brosse, F., Deetz, K., Guebsi, R., Latifou, I., Maranan, M., Rosenberg, P. D. and Schlueter, A.: A meteorological and chemical overview of the DACCIWA field campaign in West Africa in June-July 2016, Atmos. Chem. Phys., 17(17), 10893–10918, doi:10.5194/acp-17-10893-2017, 2017.

Koss, A. R., Sekimoto, K., Gilman, J. B., Selimovic, V., Coggon, M. M., Zarzana, K. J., Yuan, B., Lerner, B. M., Brown, S. S., Jimenez, J. L., Krechmer, J., Roberts, J. M., Warneke, C., Yokelson, R. J. and De Gouw, J.: Non-methane organic gas emissions from biomass burning: Identification, quantification, and emission factors from PTR-ToF during the FIREX 2016 laboratory experiment, Atmos. Chem. Phys., 18(5), 3299–3319, doi:10.5194/acp-18-3299-2018, 2018.

Kumar, A., Singh, D., Kumar, K., Singh, B. B. and Jain, V. K.: Distribution of VOCs in urban and rural atmospheres of subtropical India: Temporal variation, source attribution, ratios, OFP and risk assessment, Sci. Total Environ., 613–614, 492–501, doi:10.1016/j.scitotenv.2017.09.096, 2018.

Liousse, C., Assamoi, E., Criqui, P., Granier, C. and Rosset, R.: Explosive growth in African combustion emissions from 2005 to 2030, Environ. Res. Lett., 9(3), 035003, doi:10.1088/1748-9326/9/3/035003, 2014.

Manion, J. A., Huie, R. E., Levin, R. D., Jr., D. R. B., Orkin, V. L., Tsang, W., McGivern, W. S., Hudgens, J. W., Knyazev, V. D., Atkinson, D. B., Chai, E., Tereza, A. M., Lin, C.-Y., Allison, T. C., Mallard, W. G., Westley, F., Herron, J. T., R. F. Hampson, A. and Frizzell, D. H.: NIST Chemical Kinetics Database, Gaithersburg, Maryland. [online] Available from: http://kinetics.nist.gov/ (Accessed 18 April 2018), 2015.

Mari, C. H., Reeves, C. E., Law, K. S., Ancellet, G., Andrés-Hernández, M. D., Barret, B., Bechara, J., Borbon, A., Bouarar, I., Cairo, F., Commane, R., Delon, C., Evans, M. J., Fierli, F., Floquet, C., Galy-Lacaux, C., Heard, D. E., Homan, C. D., Ingham, T., Larsen, N., Lewis, A. C., Liousse, C., Murphy, J. G., Orlandi, E., Oram, D. E., Saunois, M., Serça, D., Stewart, D. J., Stone, D., Thouret, V., Velthoven, P. van and Williams, J. E.: Atmospheric composition of West Africa: Highlights from the AMMA international program, Atmos. Sci. Lett., 12(1), 13–18, doi:10.1002/asl.289, 2011.

Salameh, T., Afif, C., Sauvage, S., Borbon, A. and Locoge, N.: Speciation of non-methane hydrocarbons (NMHCs) from anthropogenic sources in Beirut, Lebanon, Environ. Sci. Pollut. Res., 21(18), 10867–10877, doi:10.1007/s11356-014-2978-5, 2014.

Salameh, T., Sauvage, S., Afif, C., Borbon, A., Leónardis, T., Brioude, J., Waked, A. and Locoge, N.: Exploring the seasonal NMHC distribution in an urban area of the Middle East during ECOCEM campaigns: Very high loadings dominated by local emissions and dynamics, Environ. Chem., 12(3), 316–328, doi:10.1071/EN14154, 2015.

Salameh, T., Borbon, A., Afif, C., Sauvage, S., Leonardis, T., Gaimoz, C. and Locoge, N.: Composition of gaseous organic carbon during ECOCEM in Beirut, Lebanon: new observational constraints for VOC anthropogenic emission evaluation in the Middle East, Atmos. Chem. Phys. Discuss., (August), 1–32, doi:10.5194/acp-2016-543, 2016a.

Salameh, T., Sauvage, S., Afif, C., Borbon, A. and Locoge, N.: Source apportionment vs. emission inventories of non-methane hydrocarbons (NMHC) in an urban area of the Middle East: local and global perspectives, Atmos. Chem. Phys., 16(5), 3595–3607, doi:10.5194/acp-16-3595-2016, 2016b.

Saxton, J. E., Lewis,  a. C., Kettlewell, J. H., Ozel, M. Z., Gogus, F., Boni, Y., Korogone, S. O. U. and Serça, D.: Isoprene and monoterpene emissions from secondary forest in northern Benin, Atmos. Chem. Phys. Discuss., 7(2), 4981–5012, doi:10.5194/acpd-7-4981-2007, 2007.

von Schneidemesser, E., Monks, P. S., Gros, V., Gauduin, J. and Sanchez, O.: How important is biogenic isoprene in an urban environment? A study in London and Paris, Geophys. Res. Lett., 38(19), doi:10.1029/2011GL048647, 2011.

Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics. From Air Pollution to Climate Change, Second edi., John Wiley & Sons., 2006.

Sekimoto, K., Koss, A. R., Gilman, J. B., Selimovic, V., Coggon, M. M., Zarzana, K. J., Yuan, B., Lerner, B. M., Brown, S. S., Warneke, C., Yokelson, R. J., Roberts, J. M. and de Gouw, J.: High- and low-temperature pyrolysis profiles describe volatile organic compound emissions from western US wildfire fuels, Atmos. Chem. Phys. Discuss., (February), 1–39, doi:10.5194/acp-2018-52, 2018.

Simpson, I. J., Akagi, S. K., Barletta, B., Blake, N. J., Choi, Y., Diskin, G. S., Fried, A., Fuelberg, H. E., Meinardi, S., Rowland, F. S., Vay, S. A., Weinheimer, A. J., Wennberg, P. O., Wiebring, P., Wisthaler, A., Yang, M., Yokelson, R. J. and Blake, D. R.: Boreal forest fire emissions in fresh Canadian smoke plumes: C1-C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN, Atmos. Chem. Phys., 11(13), 6445–6463, doi:10.5194/acp-11-6445-2011, 2011.

Sommariva, R., De Gouw, J. A., Trainer, M., Atlas, E., Goldan, P. D., Kuster, W. C., Warneke, C. and Fehsenfeld, F. C.: Emissions and photochemistry of oxygenated VOCs in urban plumes in the Northeastern United States, Atmos. Chem. Phys., 11(14), 7081–7096, doi:10.5194/acp-11-7081-2011, 2011.

Sommers, W. T., Loehman, R. A. and Hardy, C. C.: Wildland fire emissions, carbon, and climate: Science overview and knowledge needs, For. Ecol. Manage., 317, 1–8, doi:10.1016/j.foreco.2013.12.014, 2014.

Stockwell, C. E., Veres, P. R., Williams, J. and Yokelson, R. J.: Characterization of biomass burning emissions from cooking fires, peat, crop residue, and other fuels with high-resolution proton-transfer-reaction time-of-flight mass spectrometry, Atmos. Chem. Phys., 15(2), 845–865, doi:10.5194/acp-15-845-2015, 2015.

United Nations: World Population Prospects: The 2017 Revision, Dep. Econ. Soc. Aff. – Popul. Div., 2017.

Wang, M., Shao, M., Chen, W., Yuan, B., Lu, S., Zhang, Q., Zeng, L. and Wang, Q.: A temporally and spatially resolved validation of emission inventories by measurements of ambient volatile organic compounds in Beijing, China, Atmos. Chem. Phys., 14(12), 5871–5891, doi:10.5194/acp-14-5871-2014, 2014.

Wang, Y. F., Owen, S. M., Li, Q. J. and Peñuelas, J.: Monoterpene emissions from rubber trees (Hevea brasiliensis) in a changing landscape and climate: Chemical speciation and environmental control, Glob. Chang. Biol., 13(11), 2270–2282, doi:10.1111/j.1365-2486.2007.01441.x, 2007.

Warneke, C., Trainer, M., de Gouw, J. A., Parrish, D. D., Fahey, D. W., Ravishankara, A. R., Middlebrook, A. M., Brock, C. A., Roberts, J. M., Brown, S. S., Neuman, J. A., Lerner, B. M., Lack, D., Law, D., Hübler, G., Pollack, I., Sjostedt, S., Ryerson, T. B., Gilman, J. B., Liao, J., Holloway, J., Peischl, J., Nowak, J. B., Aikin, K. C., Min, K.-E., Washenfelder, R. A., Graus, M. G., Richardson, M., Markovic, M. Z., Wagner, N. L., Welti, A., Veres, P. R., Edwards, P., Schwarz, J. P., Gordon, T., Dube, W. P., McKeen, S. A., Brioude, J., Ahmadov, R., Bougiatioti, A., Lin, J. J., Nenes, A., Wolfe, G. M., Hanisco, T. F., Lee, B. H., Lopez-Hilfiker, F. D., Thornton, J. A., Keutsch, F. N., Kaiser, J., Mao, J. and Hatch, C. D.: Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of the Southeast Atmosphere Study 2013, Atmos. Meas. Tech., 9(7), 3063–3093, doi:10.5194/amt-9-3063-2016, 2016.

Wiedinmyer, C., Yokelson, R. J. and Gullett, B. K.: Global Emissions of Trace Gases, Particulate Matter, and Hazardous Air Pollutants from Open Burning of Domestic Waste, Environ. Sci. Technol., 48(16), 9523–9530, doi:10.1021/es502250z, 2014.

Yokelson, R. J., Burling, I. R., Gilman, J. B., Warneke, C., Stockwell, C. E., De Gouw, J., Akagi, S. K., Urbanski, S. P., Veres, P., Roberts, J. M., Kuster, W. C., Reardon, J., Griffith, D. W. T., Johnson, T. J., Hosseini, S., Miller, J. W., Cocker, D. R., Jung, H. and Weise, D. R.: Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires, Atmos. Chem. Phys., 13(1), 89–116, doi:10.5194/acp-13-89-2013, 2013.

Zhang, Q., Yuan, B., Shao, M., Wang, X., Lu, S., Lu, K., Wang, M., Chen, L., Chang, C. C. and Liu, S. C.: Variations of ground-level O3and its precursors in Beijing in summertime between 2005 and 2011, Atmos. Chem. Phys., 14(12), 6089–6101, doi:10.5194/acp-14-6089-2014, 2014.

List of Figures

Figure 1. Geographical location of Abidjan, Côte d’Ivoire and spatial distribution of ambient VOC measurements. Red stars indicate the VOC measurement sites and blue point represents the meteorology site. More information about the ambient site is detailed in the Table 2.

Figure 2. Meteorological data from Abidjan, Côte d’Ivoire. The figure represents a) the weekly accumulated precipitation and weekly air temperatures means and b) the wind speed and direction observed, during field campaigns.  Data was downloaded from the National Centers for environmental information site (NCDC), NOAA and recorded at Abidjan International Airport (see location in Figure1).

Figure 3. Spatial distribution of BTEX measured in ambient sites in Abidjan size-coded by the sum of VOCs (in µg/m3) and colour-coded by the relative contribution of BTEX compounds, namely benzene (Benz), toluene (Tol), ethylbenzene (EthylB), m+p-xylene (m+p-xyl) and o-xylene (o-xyl).

Figure 4.VOC concentrations (µg.m-3) in Abidjan ambient sites. The middle line in each box plot indicates the median (50th percentile), the lower and upper box limits represent the 25th and 75th quartiles and the whiskers the 99% coverage if the data have a normal distribution. Black markers represent the mean concentrations reported in other cities worldwide, such as Abidjan (this study), Paris (AIRPARIF, 2013), São Paulo (Dominutti et al., 2016), Beirut (Salameh et al., 2014), Karachi (Barletta et al., 2002) and South Africa (Jaars et al., 2014).

Figure 5. Relative concentration comparison between ambient measurements and emission sources profiles of VOCs measured in Abidjan, Côte d’Ivoire. Orange-yellow based-colours represent alkanes, blue based-colours aromatics and green-based colours terpenes and isoprene contributions.

Figure 6. Distribution of relative emission ratios in molar mass basis from the emission sources analysed in Abidjan, aggregated in VOC families (table SM2). The emission sources under analysis are heavy-duty diesel vehicles (HDDV), two-wheelers two strokes (TW-2T), two-wheelers four strokes (TW-2T), light-duty diesel vehicles (LDDV), Light-duty gasoline vehicles (LDGV), charcoal burning (CH), fuel wood burning (FW), charcoal making (CHM) and landfill waste burning (WB). Absolute totals for each source are shown below each pie chart in the respective unity.

Figure 7. VOC emission ratios contributions to (a)–(d) the measured molar mass, (e)–(h) OH reactivity, (i)– (l) relative ozone formation potential POCP and (m)-(p) relative SOA formation potential, aggregated in VOC families

Figure 8. Comparison of VOC emission profiles for Côte d’Ivoire between the emissions estimated from the measurements of this study (Keita et al., 2018), Edgar v4.3.2 (Huang et al; 2017) and MACCity inventory (Granier et al., 2011).The profile analysis integrates road transportation and residential sectors based on the sector activity for 2012. a) Absolute emissions in Tg/year, b) relative mass contribution, and c) relative mass reactivity considering 100 Tg of emissions weighted by the kOH reaction rate calculated for each VOC family.

Figure 9.  VOC emission profiles considering all the VOC families calculated from the measurements of this study (Keita et al., 2018) and compared with the global Edgar v4.3.2 inventory (Huang et al; 2017). The comparison integrates road transportation and residential sectors for Côte d’Ivoire and Europe in the year 2012. a) Absolute emissions in Gg/year and b) mass reactivity of 100 Gg emissions weighted by kOH reaction rate for VOC groups.

Figure 10. Total estimated emissions and relative distributions in the anthropogenic sources measured in Abidjan, Côte d’Ivoire. Each pie chart represents a VOC family, like a) Terpenes, b) IVOCs and c) Aldehydes.

Figure 11. Distribution of monoterpenes and isoprene in the emission sources measured in Abidjan. The values represents a the percentage of each biogenic VOCs over the total emission estimated for these species.

Figure 1. Geographical location of Abidjan, Côte d’Ivoire and spatial distribution of ambient VOC measurements. Red stars indicate the VOC measurement sites and blue point represents the meteorology site. More information about the ambient site is detailed in the Table 2.

Figure 2. Meteorological data from Abidjan, Côte d’Ivoire. The figure represents a) the weekly accumulated precipitation and weekly air temperatures means and b) the wind speed and direction observed, during field campaigns.  Data was downloaded from the National Centers for environmental information site (NCDC), NOAA and recorded at Abidjan International Airport (see location in Figure1).

Figure 3. Spatial distribution of BTEX measured in ambient sites in Abidjan size-coded by the sum of VOCs (in µg/m³) and colour-coded by the relative contribution of BTEX compounds, namely benzene (Benz), toluene (Tol), ethylbenzene (EthylB), m+p-xylene (m+p-xyl) and o-xylene (o-xyl).

Figure 4.VOC concentrations (µg.m^-3) in Abidjan ambient sites. The middle line in each box plot indicates the median (50th percentile), the lower and upper box limits represent the 25th and 75th quartiles and the whiskers the 99% coverage if the data have a normal distribution. Black markers represent the mean concentrations reported in other cities worldwide, such as Abidjan (this study), Paris (AIRPARIF, 2013), São Paulo (Dominutti et al., 2016), Beirut (Salameh et al., 2014), Karachi (Barletta et al., 2002) and South Africa (Jaars et al., 2014).

Figure 5. Relative concentration comparison between ambient measurements and emission sources profiles of VOCs measured in Abidjan, Côte d’Ivoire. Orange-yellow based-colours represent alkanes, blue based-colours aromatics and green-based colours terpenes and isoprene contributions.

Figure 6. Distribution of relative emission ratios in molar mass basis from the emission sources analysed in Abidjan, aggregated in VOC families (table SM2). The emission sources under analysis are heavy-duty diesel vehicles (HDDV), two-wheelers two strokes (TW-2T), two-wheelers four strokes (TW-2T), light-duty diesel vehicles (LDDV), Light-duty gasoline vehicles (LDGV), charcoal burning (CH), fuel wood burning (FW), charcoal making (CHM) and landfill waste burning (WB). Absolute totals for each source are shown below each pie chart in the respective unity.

 

Figure 7. VOC emission ratios contributions to (a)–(d) the measured molar mass, (e)–(h) OH reactivity, (i)– (l) relative ozone formation potential POCP and (m)-(p) relative SOA formation potential, aggregated in VOC families

Figure 8. Comparison of VOC emission profiles for Côte d’Ivoire between the emissions estimated from the measurements of this study (Keita et al., 2018), Edgar v4.3.2 (Huang et al; 2017) and MACCity inventory (Granier et al., 2011).The profile analysis integrates road transportation and residential sectors based on the sector activity for 2012. a) Absolute emissions in Tg/year, b) relative mass contribution, and c) relative mass reactivity considering 100 Tg of emissions weighted by the kOH reaction rate calculated for each VOC family.

Figure 9.  VOC emission profiles considering all the VOC families calculated from the measurements of this study (Keita et al., 2018) and compared with the global Edgar v4.3.2 inventory (Huang et al; 2017). The comparison integrates road transportation and residential sectors for Côte d’Ivoire and Europe in the year 2012. a) Absolute emissions in Gg/year and b) mass reactivity of 100 Gg emissions weighted by kOH reaction rate for VOC groups.

 

Figure 10. Total estimated emissions and relative distributions in the anthropogenic sources measured in Abidjan, Côte d’Ivoire. Each pie chart represents a VOC family, like a) Terpenes, b) IVOCs and c) Aldehydes.

Figure 11. Distribution of monoterpenes and isoprene in the emission sources measured in Abidjan. The values represent the percentage of each biogenic VOCs over the total emission estimated for these species.

List of Tables

Table 1. Description of the emission sources measured and evaluated in Abidjan, Côte d’Ivoire.

Table 2. Geographical location and characteristics of ambient measurement sites in Abidjan, Côte d’Ivoire

 

Table 1. Description of the emission sources measured and evaluated in Abidjan, Côte d’Ivoire.

Reference	Sub-group	Description	source	type
HDDV		Heavy-duty diesel vehicles	Diesel emissions	Road Transport
	HDDV-T	Diesel trucks	Diesel emissions	Road Transport
	HDDV-B	Diesel buses	Diesel emissions	Road Transport
LDDV		Light-duty diesel vehicles	Diesel emissions	Road Transport
LDGV		Light-duty gasoline vehicles	Gasoline emissions	Road Transport
TW	TW-2T	Two wheelers two strokes	mixture of smuggled oil and gasoline	Road Transport
	TW-4T	Two wheelers four strokes	mixture of smuggled oil and gasoline	Road Transport
CH		Charcoal	Charcoal burning	Residential
FW		Fuel wood burning	Hevea brasiliensis	Residential
CHM		Charcoal making	Charcoal fabrication	Residential
WB		Waste burning	Domestic landfill burning	Waste burning

 

Table 2. Geographical location and characteristics of ambient measurement sites in Abidjan, Côte d’Ivoire

ID	Site location	Longitude	Latitude	Activity
AT	Adjame	04°01’04”W	05°21’14”N	Traffic site  Site near a transport station; regular traffic jams, ancient public transport vehicles
AD	Akouédo	03°56’16″W	05°21’12″N	Landfill- waste burning  Uncontrolled landfill, continuous waste burning
FAC	Cocody	03°59’27”W	05°20’42”N	Residential  University residence
BIN	Bingerville	03°54’07”W	05°21’30″N	Urban Background  Far from traffic, near to Ebrié Lagoon
CRE	Treichville	04°00’10″W	05°18’41 “N	Green urban area  Near to Ebrié Lagoon; a lot of wind
ABO	Abobo	04°04’10″W	05°26,0’8,0″N	Traffic + residential  Town hall, near to the big market of Abobo. Old communal taxis and minibuses in a crowded crossroad, human activities
ZI YOP	Yopougon	04°04’52″W	05°22’12″N	Industrial area  All type industries (cement, agro-industries, plastic and iron processing, pharmaceutical and cosmetics); heavy-duty vehicles and traffic jams
KSI	Koumassi	03°57’20″W	05°17’52″N	Domestic fires + traffic  Residential site mainly influenced by domestic activities, fire-wood and charcoal; old vehicles
PL	Plateau	04°01’26”W	05°19’33″N	Traffic/ administrative  City centre, crossroad with traffic jams; Light-duty vehicles, near train station