ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-445-2018Interactions of atmospheric gases and aerosols with the monsoon dynamics over the Sudano-Guinean region during AMMAInteractions of pollutants and monsoon dynamics during AMMADeroubaixAdrienadrien.deroubaix@lmd.polytechnique.frhttps://orcid.org/0000-0003-4464-7802FlamantCyrilleMenutLaurenthttps://orcid.org/0000-0001-9776-0812SiourGuillaumeMaillerSylvainTurquetySolèneBriantRégisKhvorostyanovDmitryCrumeyrolleSuzannehttps://orcid.org/0000-0002-1491-5653LMD/IPSL, École Polytechnique, Université Paris Saclay,
ENS, IPSL Research University, Sorbonne Universités, UPMC
Univ Paris 06, CNRS, Palaiseau, FranceLATMOS/IPSL, UPMC, Sorbonne
Universités, CNRS & UVSQ, Paris, FranceLISA/IPSL,
Universités Paris Est Créteil & Paris Diderot, Créteil, FranceLOA, Université Lille 1 Sciences et Technologies, Villeneuve
d'Ascq, FranceAdrien Deroubaix (adrien.deroubaix@lmd.polytechnique.fr)16January201818144546513June201710July201710November201726November2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/445/2018/acp-18-445-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/445/2018/acp-18-445-2018.pdf
Carbon monoxide, CO, and fine atmospheric particulate matter,
PM2.5, are analyzed over the Guinean Gulf coastal region using the
WRF-CHIMERE modeling system and observations during the beginning of the
monsoon 2006 (from May to July), corresponding to the Africa
Multidisciplinary Monsoon Analysis (AMMA) campaign period.
Along the Guinean Gulf coast, the contribution of long-range pollution
transport to CO or PM2.5 concentrations is important. The contribution
of desert dust PM2.5 concentration decreases from ∼ 38 % in
May to ∼ 5 % in July. The contribution of biomass burning
PM2.5 concentration from Central Africa increases from
∼ 10 % in May to ∼ 52 % in July. The anthropogenic
contribution is ∼ 30 % for CO and ∼ 10 % for
PM2.5 during the whole period.
When focusing only on anthropogenic pollution, frequent northward transport
events from the coast to the Sahel are associated with periods of low wind
and no precipitation. In June, anthropogenic PM2.5 and CO concentrations
are higher than in May or July over the Guinean coastal region. Air mass
dynamics concentrate pollutants emitted in the Sahel due to a meridional
atmospheric cell. Moreover, a part of the pollution emitted remotely at the
coast is transported and accumulated over the Sahel.
Focusing the analysis on the period 8–15 June, anthropogenic pollutants
emitted along the coastline are exported toward the north especially at the
beginning of the night (18:00 to 00:00 UTC) with the establishment of the
nocturnal low level jet. Plumes originating from different cities are mixed
for some hours at the coast, leading to high pollution concentration, because
of specific disturbed meteorological conditions.
Introduction
The interaction between air pollution and climate in major population centers is a
challenging field of research . In the countries of the
Guinean Gulf, the population has been growing rapidly during the last
decades, accompanied by economic development. Parallel to industrialization,
air pollution is increasing without any governmental control .
During the dry season (i.e., November–April), when the harmattan easterly
wind is weak, high ozone concentrations and smog are observed over large cities
such as Lagos or Cotonou . During the wet season
(i.e., May–October), the West African monsoon (WAM) wind carries the
pollutants northward, and local convective precipitations wash pollutants out
of the atmosphere. Two precipitation periods occur over the Guinean Gulf
coastal region in April–May and August–September. Between these two
periods, the wind coming from the south is predominant .
There are various aerosol and gas sources in the Guinean Gulf coastal region
during the WAM. Sea salt aerosols are transported in the marine boundary
layer, and mineral dust aerosols are transported in the Saharan Air Layer
(SAL) above the monsoon air . Biogenic components are
emitted by tropical forests and the urban air pollution
from large cities, leading to ozone production .
In addition, pollutants resulting from incomplete combustion such as carbon
monoxide and black carbon particles are coming from the Southern Hemisphere
due to biomass burning emissions, and are reaching the Guinean coast in June
. In situ biomass burning plumes observations
have measured high ozone (≥ 60 ppb at 700 hPa) and carbon monoxide
(≥ 200 ppb at 700 hPa) concentrations .
In Nigeria, listed the different sources of
particle loading: biomass burning (31.7 %), fugitive dust from roads
(29.1 %), fuel wood burning (21.3 %), harmattan dust (13.8 %),
solid waste incineration (2.1 %), stationary sources (1.6 %),
automobile exhaust lead (0.2 %) and gas flares (0.1 %). Since the
1990s, natural pollution from desert dust and biomass burning has remained
important . However, the economic growth over the
region drives up anthropogenic emissions. There are increases in industries,
including gas flaring ; local fuel-wood burning for
stoves; and traffic . Moreover,
the increase in two-wheel vehicles, using very poor quality fuel is suspected
to quickly worsen air quality .
All air quality monitoring studies have shown that the outdoor WHO air
quality guidelines (i.e., threshold concentrations) are largely exceeded.
These thresholds are 35 ppm for 1 h and 9 ppm for 8 h exposure for CO,
and 10 µg m-3 annual mean and
25 µg m-3 for 24 h mean for PM2.5. For instance, in April 1993,
have measured very high levels of CO (up to 10 ppm,
measured close to high traffic road) and total particulate matter (up to
200 µg m-3) based on half-hour averages in Lagos (Nigeria).
In Accra (Ghana), have measured PM2.5 up to
200 µg m-3 (based on 1 min averages) in streets polluted by
wood stoves, heavy traffic and trash burning. In Ouagadougou (Burkina Faso)
PM2.5 observations (based on daily averages) reach
164 µg m-3 and CO concentration measured
in traffic frequently exceed all World Health Organization (WHO) guidelines
.
The health impact of such air pollution is expected to be high and to
increase further without any specific emission regulation .
It is therefore important to gain a better understanding of pollutant
emissions and transport in West Africa. All these results have highlighted
the high level of pollution in large cities, also affecting remote places.
However, there is no continuous air quality monitoring in West Africa, so
existing studies are focused on local scales, short time periods, and few
pollutants.
Several observation campaigns have been dedicated to the WAM, notably the
Africa Multidisciplinary Monsoon Analysis (AMMA), which was the first
international program started to improve our knowledge on all aspects of the
WAM . WAM modeling has made progress; however, the
Guinean Gulf coastal region is challenging to model because of the complex
land–sea–atmosphere interactions.
Along the coastline, there are several atmospheric cells acting on different
scales. The diurnal cycle of the land–sea breeze occurs at a local scale (a
few kilometers). During the day, surface wind is linked with convection
within the boundary layer, while at night there is the formation of the
nocturnal low-level jet (NLLJ) in response to the daily deep convection
activity . At a regional scale (a few hundred kilometers),
the monsoon wind from the south meets the harmattan wind from the north,
forming the Intertropical Discontinuity at ground level
, and leading to a
complex vertical structure . Between these two
scales, an additional meridional atmospheric cell is suspected in the low
atmosphere, enhancing convergence at the coast .
This cell results from a gradient of wind speed due to the meridional
gradient of sea surface temperature . The recent
research program “Dynamics-aerosol-chemistry-cloud interactions in West
Africa program” (DACCIWA) has been dedicated to the study of
land–sea–atmosphere interactions in West Africa. It will contribute to
understanding changes in the atmospheric composition due to increasing in
emissions over a rapidly growing region, as well as to the development of the
next generation of accurate models to forecast weather and pollution in
southern West Africa .
This article focuses on transport of pollutants over the Guinean Gulf coastal
region, in particular on carbon monoxide and particulate matter with an
aerodynamic diameter Dp<2.5µm (CO and
PM2.5 hereafter), which both have a detrimental impact on health
. The scientific questions addressed in this work are
the following:
What is the relative contribution of long-range transported and locally
emitted pollutants to surface concentrations from the Guinean Gulf to the Sahel?
What is the impact of meridional atmospheric cells on the transport of
pollutants emitted from large coastal cities?
The pollution patterns are analyzed during the 2006 AMMA period using several
observational datasets in combination with numerical simulations of the
meteorology as well as of the aerosol–gas chemistry and transport presented
in Sect. 2. Section 3 presents the main spatial and temporal patterns over
the Sudano-Guinean region of the AMMA study case. Section 4 analyzes the
anthropogenic pollution from the coast to the Sahel. Section 5 focuses on the
analysis of the coastal dynamics and pollution transport. Section 6 focuses
on specific study cases. Conclusions and perspectives are given in Sect. 7.
Weather-pollution modeling configuration
The modeling analysis was performed using the Weather Research and
Forecasting (WRF) model for the meteorological fields, which drives the
CHIMERE model for the gaseous and particulate species concentrations. Two
nested geographical domains are defined: a continental one to take into
account remote sources and long-range transport from the Mediterranean sea to
the Tropic of Capricorn (27∘ S to 44∘ N, 38∘ W to
47∘ E) and a regional one, centered on the Guinean Gulf (1 to
20∘ N, 23∘ W to 17∘ E). The WRF and CHIMERE models
are run offline on the same horizontal grids for the continental and regional
domains. The simulated time period is April to July 2006, including a 1-month spin-up. The spin-up time aims at ensuring that all pollutants emitted
outside of the modeled domain are present in the domain (depending on the
wind speed and direction) since the first modeled hour.
Meteorological fields with the WRF model
The meteorological variables are modeled with the regional non-hydrostatic
WRF model (version 3.7.1) presented by . The continental
domain has a constant horizontal resolution of 60 km × 60 km, and
the regional domain has a constant horizontal resolution of
20 km × 20 km, both with 32 vertical levels from the surface to
50 hPa. We use a two-way nesting with the WRF model.
The global meteorological fields are taken from the US Global Forecast System
produced by the National Center for Environmental Prediction. It is read and
interpolated hourly by WRF using low-frequency spectral nudging above the
planetary boundary layer (PBL)
in order to enable the PBL variability to be resolved by WRF
. We followed the recommendations of
to configure the convection and planetary
boundary layer schemes with a setup optimized for the 2006 WAM, especially
for the meridional gradient of temperature and the low level circulation.
The Single Moment-6 class microphysics scheme (WSM6) is used allowing for
mixed-phase processes suitable for high resolution simulations
. have shown that WAM precipitation patterns
are very sensitive to the radiation scheme, and the most realistic patterns
were obtained with the Rapid Radiative Transfer Model for General Circulation
Models (RRTMG) with the Monte-Carlo Independent Column Approximation (McICA)
method of random cloud overlap from . The planetary
boundary layer physics are computed using the Yonsei University scheme
. The cumulus parametrization used is the ensemble
Grell–Dévényi scheme, as have shown that internal
variability is much larger with the Kain–Fritsch scheme than with the
Grell–Dévényi scheme at the seasonal, intra-seasonal, and daily time
scales, and from the regional to the local (grid point) spatial scales. The
surface layer scheme is based on Monin–Obukhov with a Carslon–Boland viscous
sub-layer. The surface physics are calculated using the “Noah” Land Surface
Model scheme with four soil temperatures and moisture layers .
Chemistry transport with the CHIMERE model
CHIMERE is a regional chemistry-transport model (version 2017), fully
described in and . The CHIMERE model has previously
been used over the AMMA observation period, but only dust aerosols were
modeled . In this study, all important gas
and aerosol sources are included (anthropogenic, biogenic, mineral dust, sea
salt and biomass burning). The 32 vertical levels of the WRF model are
projected on the 20 levels for CHIMERE from the surface to 200 hPa. We use a
one-way nesting with the CHIMERE model.
The anthropogenic emissions are estimated by the EDGAR Team using the HTAP v2
(Hemispheric Transport of Air Pollution) annual totals for the year 2010,
using inventories based on the MICS-Asia, EPA-US/Canada and TNO databases
(available at http://edgar.jrc.ec.europa.eu/htap_v2).
Figure presents the anthropogenic PM and CO emissions over
the regional domain and the Cotonou–Niamey meridional transect used for the
analysis in the next sections, defined in longitude λ=2 to
3∘ E and in latitude ϕ=1 to 19∘ N.
Anthropogenic carbon monoxide (a) and primary particulate
matter (b) surface emission fluxes in kg km-2 day-1
averaged over the 3-month period. The gray dots are the major cities and
the three green dots are the locations studied from the south to the north:
Cotonou (Benin), Djougou (Benin), Niamey (Niger). The blue box represents the
Cotonou (Benin)–Niamey (Niger) meridional transect studied (longitude
λ=2∘to 3∘ E, latitude ϕ=1 to 19∘ N).
Biomass burning emissions from Central Africa are of primary importance to
simulate West African pollution . This is achieved using
the APIFLAME model , which estimates aerosols and
chemical species emissions produced by biomass burning. Since the incomplete
combustion is both included in anthropogenic inventories (local urban
burning) and forests biomass burning inventories, the simulation was designed
to split these two parts.
Biogenic emissions are calculated using the MEGAN emissions scheme
. The mineral dust sources are obtained using the GARLAP
(Global Aeolian Roughness Lengths from ASCAT and PARASOL) new global soil and
surface dataset made from satellite-derived aeolian roughness lengths with a
6 km spatial resolution, as detailed in .
The top and lateral boundary conditions are driven by LMDZ-INCA for aerosols
and chemical species . The time step is set to 10 min
for the physical processes and 5 min for the chemistry, which could change
depending on the Courant–Friedrichs–Lewy condition. It is also possible to
release gaseous or particulate atmospheric tracers, which is a powerful tool
to analyze the pollution patterns.
described the calculation of gaseous species in the
MELCHIOR-2 (reduced) scheme and the aerosol scheme, which takes into account
species such as sulphate, nitrate, ammonium, primary organic matter (POM) and
elemental carbon (EC), secondary organic aerosols (SOA), sea salt, dust and
water. All aerosols are represented using 10 bins, from 40 nm to
40 µm in diameter. Their life cycle is fully represented with
emission, transport, chemistry and deposition (wet and dry).
have detailed and analyzed aerosol speciation and size
distribution in the CHIMERE model during the summer 2013 over Europe and
Africa using the AERONET network for aerosol optical depth (AOD) and EMEP network for PM
concentrations. For the AOD calculation, the aerosol optical scheme in the
CHIMERE model considers mixed aerosols following the “core–shell”
hypothesis detailed in and evaluated in .
In order to quantify the PM2.5 source apportionment, we assume that it
is possible to split aerosols in different families depending on the sources
because their chemical compositions are different: mineral, biogenic, salt
and anthropogenic. Given that anthropogenic and biomass burning aerosols have
similar compositions, we have done two simulations with and without biomass
burning emissions to split their contributions. The gas-phase chemical scheme
for SOA formation explained in takes into account three
anthropogenic and three biogenic hydrophilic species, three hydrophobic
species with different saturations, and two surrogate compounds for the
isoprene oxidation products.
The source apportionment has been determined for CO considering three main
contributors (anthropogenic sources, biomass burning sources and long-range
transport). Consequently, three simulations have been done: one without any
emission source in the domain for the background concentration, one with the
anthropogenic emission only, and a last one with the anthropogenic and
biomass burning emissions. The oxidation of volatile organic carbon gases
(VOCs), which leads to CO formation is also taken into account in the
anthropogenic sources. However, the amount of VOCs is low; for instance, in
BAe-146 measurements VOC concentration are lower than 10 ppb (e.g.
; ). Thus CO produced by VOCs oxidation is low (a
few ppb).
Temporal variability from May to July 2006
In this section, we analyze the temporal variability of precipitation, gas
and aerosols during the whole AMMA-SOP1 period .
First, the precipitation regimes are identified using Hovmöller diagrams.
Second, AERONET surface stations data are used to quantify the 3-month
variability of the AOD. Finally, the relative
contribution to gases and aerosols from several sources is quantified using
both airborne observations and modeling. The two last points focus on three
locations: Cotonou (Benin), Djougou (Benin) and Niamey (Niger), which are
representative of locations under the combined influence of mineral dust,
anthropogenic pollution, biogenic and biomass burning components.
Precipitation patterns
During this period, the precipitation location and rate will play a crucial
role for the modeled surface PM2.5 concentrations. As a validation for
this variable, the methodology of is used: precipitation
rates are averaged between 8.5∘ W and 8.5∘ E. Day-to-day
variability is smoothed by applying a moving average of ±2 days.
Figure is directly comparable to the
study using the same period and averaged region. In May and June, observed
and modeled precipitations occur mainly over the ocean (below 5∘ N).
From late June on, the main precipitation areas move over the continent
(above 5∘ N) and reach the Sahel (at about 13∘ N).
Figure shows that the modeled precipitation spatial patterns
are in good agreement with the two satellite observations (TRMM and GPCP)
presented in their study (see Fig. 3 of ).
Time–latitude average (Hovmöller) of precipitation
(mm day-1). Precipitation is averaged between 8.5∘ W and
8.5∘ E in longitude. Day-to-day variability is eliminated by
applying a moving average of ±2 days. Due to the longitudinal average,
the coastline is between 5 and 6∘ N. The two gray lines show the
latitudinal extend of the regional model domain. The three dash gray lines
represent the latitudes of the three locations studied (Cotonou, Djougou and
Niamey).
The WAM is driven by the sea surface temperature decreases over the Gulf of
Guinea and over the Sahara, a low thermal pressure system appears called the
Saharan heat low . The temperature gradient between the sea
and the Sahara allows the monsoon system to progress inland reaching the
Sahara in July . The monsoon progression to the
north is not linear due to the complex sea–land–atmosphere interactions
(Fig. ). Two jumps are modeled at the end of May and at the
beginning of June. Precipitation is associated with large-scale squall lines,
creating mesoscale convective systems (MCSs) moving westward .
The meteorological simulation reproduces the two changes of the main
precipitation area that have been previously identified from climatological
averages: the “pre-onset” (i.e., end of May), when the main precipitation
area associated with the Intertropical Convergence Zone (ITCZ) located at the
Equator moves close to the coast ,
and the “onset” (i.e., at the beginning of July), when the main
precipitation area reaches the Sahel . For these dates,
simulated precipitation matches very well with AMMA observations. In 2006,
the monsoon onset occurred on the 10 July with a 10-day delay compared to its
climatological date, which is 24 June with a standard deviation of 8 days
over the period 1968–2005 according to . Thus, three
periods could be defined: before “pre-onset” (in May), between
“pre-onset” and onset (in June), and after onset (in July).
Meridional aerosol content
In our studied region, surface aerosol concentrations in the cities are
affected by several contributions. In addition to local emission, cities may
be strongly impacted by biomass burning transported from Central Africa
, or by mineral dust transported from Sahara
.
The modeled daytime AOD and Ångström exponent are
compared to observations from the AERONET network ,
available at https://aeronet.gsfc.nasa.gov. From the daily AERONET
level-2 measurements of AERONET-AOD (at 440 nm) and the Ångström exponent
(440–870 nm), AERONET-AOD is calculated (at 550 nm) based on the
Ångström law. A spatial bilinear interpolation of the model outputs is
performed at the station location.
Two AERONET stations are located close to the meridional transect studied
(Fig. ): Banizoumbou (13.5∘ N, 2.1∘ E) in
the suburb of Niamey in Niger, and Djougou in Benin (9.7∘ N,
1.6∘ E) north of the heavily urbanized areas around Cotonou. We
compare a simulation made with and without biomass burning emission
(with/without BB), presented in Fig. .
Observed daily averages of AERONET level 2 AOD and Ångström
exponent (black dots) at Djougou (Benin) and Banizoumbou compared to the
modeled time series with a splitting to extract the relative contribution
between without biomass burning emissions (including anthropogenic, biogenic,
sea salt and mineral dust; all four in blue) and with biomass burning
emissions (in red).
There are two important events of coarse particles recorded at both sites,
associated with a low Ångström exponent (i.e., Ångström exponent lower
than 0.5 as in ) and AOD greater than 1, between 13 and 14
May and between 10 and 13 June. The model captures the magnitude of these large-scale dust events. During the studied period, the events of coarse particles
are well reproduced (high or moderate AOD is generally associated with a low
Ångström exponent). There is an increase in the Ångström exponent, i.e.,
fine particles over the period, which is well reproduced by the model.
Frequent fine-aerosol events (high Ångström exponent) have been monitored
corresponding to low or moderate AOD, which are partially captured by the
model.
The addition of the biomass burning emissions lead to an important plume of
gas and aerosols reaching the Guinean Gulf in June. Modeled AOD with biomass
burning emissions are well in the range of the observations but the
variability is not captured. In May, the aerosol content is mostly composed
of coarse particles (Ångström exponent about 0.2), in June of a fine–coarse
mixture of particles (about 0.5), and in July of finer particles, especially at
Djougou (Ångström exponent about 0.8). The model is able to reproduce this
increase in the Ångström exponent, which suggests an aerosol origin
transition, from a period dominated by desert dust to a period of fine
particles which could be local urban and/or biomass burning pollution from
the south.
Meridional aerosol and gas concentrations
In this section, the modeled CO and PM2.5 concentrations are compared to
aircraft observations collected during the AMMA campaign (Sect. 3.3.1),
available on the AMMA database (http://baobab.sedoo.fr/AMMA/)(although
other datasets exist). Then, the different contributions of the pollution
sources are analyzed from the modeled concentrations at the three studied
sites (Sect. 3.3.2).
Airborne observations
We are studying two flights conducted along a meridional transect between
Cotonou and Niamey on 13 and 14 June 2006 as part of a “north–south
land–atmosphere–ocean interaction” survey mission. During these 2 days, the
WAM dynamics over the area were perturbed by the presence of a MCS. It
developed over the Jos Plateau (Nigeria) around 16:00 UTC, reaching the
Benin–Nigeria border at 20:00 UTC and moving southwestward across Benin
overnight and into central Ghana, as already described in
and . The model reproduces the location of this MCS
but earlier than in the observations, i.e., reaching the Benin–Nigeria border
at 10:00 UTC (Fig. ). The MCS interacts with the dust
layer coming from the Sahara (especially from the Bodélé depression),
changing the dust load and vertical distribution over Benin and Niger.
Associated with subsidence in the wake of the MCS, there is a lowering of the
dust layer height .
(a) EUMETSAT visible image of the Cotonou area of 13
June 2006 at 20:00 UTC (from NAScube
http://nascube.univ-lille1.fr); (b) map of the Cotonou area for
13 June 2006 at 12:00 UTC with wind vectors at 10 m (orange arrows) and
precipitation (blue shading). The two flight trajectories are displayed with
the red line for 13 June and with the green line for 14 June.
Modeled CO and PM2.5 concentrations are compared to aircraft
measurements performed onboard the ATR-42 aircraft (with PCASP instrument for
PM), which have been averaged at a 2 min time step. The modeled values are
interpolated along the aircraft trajectories, in time between the two closest
modeled hourly outputs, and vertically between the two closest model vertical
levels and horizontally with a bilinear interpolation. For the two flights
(13 July in the morning from Niamey to Cotonou, and 14 July in the afternoon
from Cotonou to Niamey), Table presents modeled and observed
mean spatial values and ranges of CO and PM2.5 concentrations in the PBL
(altitude lower than 1000 m) over three regions: the coastal region, including
Cotonou (6.3–9.0∘ N); the Sudano-Guinean region, including Djougou
(9.0–11.0∘ N); and the Sudano-Sahelian region, including Niamey
(11.0–13.5∘ N).
Range of 2 min average modeled and observed concentrations of CO
(ppb) and PM2.5 (µg m-3) in the PBL (altitude lower than
1000 m) over three regions: coastal region (6.3–9.0∘ N),
Sudano-Guinean region (9.0–11.0∘ N), and Sudano-Sahelian region
(11.0–13.5∘ N).
Pollutants obs/modCoastal region Sudano-Guinean region Sudano-Sahelian region Aircraft observations of 13 June 2006 from 10:00 to 13:00 UTC MeanMinMaxMeanMinMaxMeanMinMaxCO CHIMERE (ppb)207.24174.90233.03231.52217.50244.54243.88212.25275.76CO aircraft (ppb)172.78146.78209.21172.51161.43182.14159.26148.70174.24PM2.5 CHIMERE (µg m-3)23.9519.2626.0128.9625.4633.8755.1535.1879.93PM2.5 Aircraft (µg m-3)15.677.5733.0249.7222.3377.8855.8134.2272.20Aircraft observations of 14 June 2006 from 13:00 to 16:00 UTC MeanMinMaxMeanMinMaxMeanMinMaxCO CHIMERE (ppb)212.71190.23239.83229.15205.49246.75244.74232.68257.97CO aircraft (ppb)200.21185.49222.34181.51153.11233.35168.66146.35200.66PM2.5 CHIMERE (µg m-3)42.7928.2364.3382.2564.4392.9384.0179.6591.46PM2.5 aircraft (µg m-3)39.4037.2642.3739.3623.6059.1292.0850.11138.86
For CO concentration, on 13 June, there is no clear gradient over the three
regions but rather a constant concentration of about 170 ppb. On 14 June, we
can noticed a gradient from the coast (200 ppb) to the Sahel (167 ppb). For
both days, the model predicts a gradient with the highest concentration over
the Sahel. Over the coastal region, the observed CO concentration range is
similar for the two flights (between 147 and 222 ppb), which is in good
agreement with the modeled range (between 175 and 240 ppb). Over the
Sudano-Guinean region, the observed range of variation is 161–182 ppb prior
to the MCS (13 June), and it increases to 153–233 ppb after the MCS (14
June). The model is able to capture the larger variability on 14 June
(205–247 ppb compared to 218–245 ppb on 13 June). Over the
Sudano-Sahelian region, the observed variability of CO concentration is also
larger on 14 June (146–200 ppb) than on 13 June (149–174 ppb). This
behavior is not reproduced in modeled concentrations. There is an
overestimation of the modeled CO concentration (positive bias of
∼ 20 ppb) for 13 and 14 June.
For PM2.5 concentrations, a south–north gradient is expected with the
highest concentrations close to the Sahara. There is a clear gradient in the
observed PM2.5 concentration mean on 13 June between the coastal region
(8 µg m-3) and the Sudano-Guinean region
(50 µg m-3), and almost the same concentration in the
Sudano-Guinean and the Sudano-Sahelian region (56 µg m-3).
After the MCS, there is no clear gradient but rather the same concentration
over the coastal and the Sudano-Guinean regions (39 µg m-3).
However, there is an important increase in the concentration moving to the
Sahel (up to 92 µg m-3). The variability is increased over
the three regions: 37–42 µg m-3 for the coastal region,
24–59 µg m-3 for the Sudano-Guinean region, and
50–139 µg m-3 for the Sudano-Sahelian region. The modeled
ranges match the observed ones for both days. The model reproduces a
south–north gradient on 13 June, which is well in agreement with the
observations. On 14 June, the model predicts the concentration gap between
the coastal and the Sudano-Guinean regions (from 43 to
82 µg m-3), while it was observed between the Sudano-Guinean
and the Sahelian regions.
These 2 days correspond to disturbed meteorological conditions, which may
not be representative of the typical average concentrations. The
model–observation comparison suggests that the MCS occurs later in the
observation, which could in turn induce a unrealistic modeled pollution plume
(for instance a biomass burning plume) over the Sudano-Sahelian region and
the Sahel. Nevertheless, the orders of magnitude of the CO and PM2.5
concentrations are good in agreement with observations.
Monthly modeled pollution source apportionment
In order to analyze the source apportionment, we consider that the CO mixing
ratio is due to three major contributors – long-range transport and anthropogenic and biomass
burning sources – and that the PM2.5 mass concentration comes from five
major types of pollution source: anthropogenic, biomass burning, mineral
dust, biogenic and sea salt (we assume that PM2.5 background
concentration is negligible). The relative percentage of each source is
presented for CO in Table and for PM2.5 Table
at the three studied locations – Cotonou, Djougou and Niamey – for the
whole period and for each month of the simulation.
CO (ppb) average and relative contributions (%) of each type of
pollution source (background, anthropogenic, biomass burning) at Cotonou
(Benin), Djougou (Benin) and Niamey (Niger). The time-averaged periods
correspond to each month (May, June, July) and to the whole period (from May
to July).
COMay–July May June July Cotonou (Benin) Average (ppb)221.01157.25239.11267.26Background (ppb and %)73.3033.1773.5846.7972.8230.4573.4827.50Anthropogenic (ppb and %)64.7529.3067.8343.1464.6527.0461.7723.11Biomass burning (ppb and %)82.9637.5415.8410.07101.6442.51132.0049.39Djougou (Benin) Average (ppb)226.78180.28240.65259.85Background (ppb and %)75.2733.1977.3842.9275.2331.2673.2128.17Anthropogenic (ppb and %)80.7735.6293.5251.8889.0136.9960.0423.11Biomass burning (ppb and %)70.7431.199.385.2176.4131.75126.6048.72Niamey (Niger) Average (ppb)212.44171.24229.26237.34Background (ppb and %)78.9537.1682.7248.3078.6934.3275.4231.78Anthropogenic (ppb and %)82.5438.8583.9249.0198.2442.8565.9627.79Biomass burning (ppb and %)50.9523.984.602.6952.3322.8295.9740.43
PM2.5 (µg m-3) average and relative
contributions (%) of type of pollution source (anthropogenic, biomass
burning, dust, biogenic, sea salt) at Cotonou (Benin), Djougou (Benin) and
Niamey (Niger). The time-averaged periods correspond to each month (May, June
July) and to the whole period (from May to July).
PM2.5May–July May June July Cotonou (Benin) Average (µg m-3)29.6223.3128.8936.64Anthropogenic (µg m-3 and %)3.4511.643.4414.753.4411.903.479.46Biomass burning (µg m-3 and %)12.0240.582.5410.8914.3849.7919.2152.44Dust (µg m-3 and %)4.3514.688.8938.152.348.101.744.76Biogenic (µg m-3 and %)6.0420.385.8425.045.6819.656.5917.98Salt (µg m-3 and %)3.7712.722.6011.173.0510.565.6215.35Djougou (Benin) Average (µg m-3)38.2541.3137.7135.70Anthropogenic (µg m-3 and %)4.1010.724.3810.604.5912.163.359.38Biomass burning (µg m-3 and %)9.1823.991.343.239.3624.8216.8447.17Dust (µg m-3 and %)13.5535.4323.7357.4412.0531.954.8313.52Biogenic (µg m-3 and %)9.9526.0110.7626.0510.4427.688.6624.25Salt (µg m-3 and %)1.473.851.102.671.283.392.035.68Niamey (Niger) Average (µg m-3)53.7671.5952.5637.09Anthropogenic (µg m-3 and %)4.227.854.005.585.159.803.549.54Biomass burning (µg m-3 and %)5.7310.660.560.785.4710.4111.1530.06Dust (µg m-3 and %)36.8668.5761.0285.2434.3465.3515.1440.83Biogenic (µg m-3 and %)6.3111.755.587.797.0813.486.3117.01Salt (µg m-3 and %)0.631.180.440.610.510.960.952.56
For the three sites, the average concentrations of surface CO increase during
the whole period. The mean concentrations are very close for the three sites:
221 ppb in Cotonou, 227 ppb in Djougou, and 212 ppb in Niamey. There is a
clear increase in CO from May (157–180 ppb) to July (267–280 ppb). This
increase is due to the biomass burning sources from May (3–10 %) to July
(40–49 %), while the anthropogenic and background concentrations are
stable during the whole period and for all sites. It seems that the CO
overestimation noticed in the previous section is linked with an
overestimation of biomass burning emissions.
PM2.5 concentrations display a south–north gradient of concentrations
(30 µg m-3 in Cotonou, 38 µg m-3 in Djougou,
54 µg m-3 in Niamey) on average over the whole period,
consistent with the gradient of dust contribution (15 % in Cotonou, 35 %
in Djougou, 67 % in Niamey). From May to July and for all sites, the
mineral dust contribution is constantly decreasing. On the other hand, the
biomass burning contribution increases (3 to 19 µg m-3 in
Cotonou, 1 to 17 µg m-3 in Djougou, 0.6 to
11 µg m-3 in Niamey), which seems to be overestimated as for
CO concentration. Over the studied area, PM2.5 concentrations are
dominated by long-range transport of biomass burning and dust aerosols.
Anthropogenic PM2.5 concentrations range from 3 to
5 µg m-3, which is about 10 % for the whole period and for
the three sites.
In Cotonou, the average concentration of surface PM2.5 increases during
the whole period, from 23 to 37 µg m-3. This mainly
corresponds to the arrival of biomass burning emission products, transported
from Central Africa to the Guinean Gulf, with an increase from 11 to 52 %
from May to July. However, the mineral dust contribution decreases
during the period, from 38 to 5 %. The sea salt contribution increases
from 3 to 6 ppb. During the 3 months, the anthropogenic and biogenic
contributions remain stable at about 4 and 6 µg m-3, respectively.
In Djougou, the same behavior is observed but with some changes in the
absolute values. The relative contribution of mineral dust decreases from 57
to 14 %, while the biomass burning contribution increases from 3 to 47 %. The
anthropogenic contribution is slightly higher in June at about 5 µg m-3.
In Niamey, the dust contribution is important for the 3 months. It
decreases by a factor 4, from 61 to 15 µg m-3, consistent
with observation of PM10 in Banizoumbou in Niger
, which is probably due to the reduction of local
emission linked with the increase in vegetation cover. The relative
contribution of anthropogenic pollution is slightly higher in June at about 5 µg m-3.
For CO or PM2.5 concentrations, the anthropogenic emissions contribute
significantly to the total budget (∼ 30 % for CO and
∼ 10 % for PM2.5). It is therefore important to better
understand the daily variability of anthropogenic pollutant transport.
Focus on anthropogenic pollutants from Cotonou to Niamey
This section focuses on the horizontal variability and vertical structure of
anthropogenic pollution. Only the contribution of anthropogenic sources is
considered in PM2.5 and CO concentrations, from now on referred to as
anth-PM2.5 and anth-CO.
Time–latitude variability at the surfaceCO and PM2.5 concentrations
Time–latitude average (Hovmöller diagram) of (a) surface CO
concentration (ppb) and (b) PM2.5 (µg m-3) due to
anthropogenic emissions along a meridional transect from 2 to
19∘ N and averaged from 2 to 3∘ E including
Cotonou (Benin) and Niamey (Niger). Day-to-day variability is smoothed by
applying a moving average of ±2 days. White contours are precipitation of 10 mm day-1. Specific isocontours are highlighted:
4 µg m-3 (orange) and 5 µg m-3 (violet).
The black line is the ITD defined as RH isocontour of 20 %.
The Cotonou–Niamey meridional transect includes the two specific cities
extensively studied in the framework of the AMMA program: a large coastal city
(Cotonou in Benin) and a Sahelian city (Niamey in Niger). To highlight the
latitudinal regional transport, modeled concentrations are presented with the
same methodology as in the previous section with Hovmöller diagrams,
corresponding to time–latitude average of variables (data are smoothed with a
5-day moving average, i.e., ±2 days).
Results are presented in Fig. for anth-CO and
anth-PM2.5. For the two species, meteorological parameters are
superimposed on the concentrations. Precipitation rate contours are defined
for events with more than 10 mm day-1 over the Cotonou–Niamey transect. These
are similar but not equivalent patterns to those presented in Sect. averaged over the whole of West Africa.
The Intertropical Discontinuity (ITD) is the limit between the northward
monsoon wind and the southward harmattan wind
. The ITD could be defined as the isocontour
of relative humidity (RH) equal to 20 %. We can notice that the location
of the ITD associated with a sharp gradient in surface anthropogenic
concentrations, with a decrease going further north from 100 to 20 ppb for
CO, and from 5 to 2 µg m-3 for
PM2.5.
For anth-CO surface concentrations, high concentrations are continuously
modeled from the beginning of May to late June at the coast where Cotonou is
located, about 100 ppb around ϕ=6.3∘ N. Over the Guinean
Gulf, the concentration is low between 20 and 50 ppb. The second area with
high anth-CO values corresponds to the Sudano-Sahelian region, where
concentrations vary between 60 and 100 ppb around ϕ=12∘ N.
Whereas high concentrations of anth-CO at the coast are clearly related to
local emissions, the high concentrations over the Sahel could be due
to either transport or local emissions. Over the studied domain, anth-CO surface
concentrations evolve between 20 and 120 ppb. There are some high modeled
surface concentrations in May and in June when rain occurs, such as around 11 June. In July, the variability is mostly consistent with precipitation rates
after the onset, suggesting modifications of transport and deposition
patterns by the convection associated with large-scale precipitation.
Precipitation variability can thus explain only a part of the CO variability.
It is necessary to also investigate the large-scale wind speed and direction.
The same behavior is observed for the surface concentrations of
anth-PM2.5. The week-to-week variability is greater than for anth-CO,
which is probably due to the longer lifetime of CO compared with that of PM
(being less chemically active and less prone to settling). CO is more
homogeneously mixed than PM in a large latitudinal area spanning from the
coast to latitudes higher than 16∘. The temporal variability of
surface anth-PM2.5 exhibits a periodicity close to 2 weeks: during the
whole modeled period, five higher-concentration periods are observed from the
coast to ϕ≈ 16∘ N. In addition in these latitudinal
patterns, local minima are modeled, for instance 20 June. At ϕ=12∘ N, there is an area of high concentration which is present over
the whole period. This may be related to vertical transport and will be
quantified in the next sections. The results show similarities for both
two pollutants in terms of time–latitude variability; the next sections will
refine the analysis only for anth-PM2.5.
Synoptic wind and pollution
This section aims at analyzing the anth-PM2.5 concentration temporal
variability through the surface wind speed and direction. The previous
section has shown that low pollution concentration is not always associated
with precipitation events. Results are presented in the same way using Hovmöller
diagrams (Fig. ). For each figure, colored isocontours are
superimposed corresponding to surface anth-PM2.5 of 4 and 5 µg m-3.
Time–latitude average (Hovmöller diagram) of surface
wind: (a) speed and (b) direction, along a meridional transect from 2 to
19∘ N and averaged from 2 to 3∘ E including
Cotonou (Benin) and Niamey (Niger). Wind directions are presented with steps
of 45∘. Day-to-day variability is smoothed by applying a moving
average of ±2 days. Orange and violet contours are surface PM2.5
concentrations of 4 and 5 µg m-3.
For the surface wind speed, the lowest values are modeled along the coast
during the whole period. The wind speed variability is weak from the coast to
the Sahel. Periods of low wind speed are coincident with the highest values
of surface anth-PM2.5. At the end of the period, when precipitation
occurs inland and anth-PM2.5 is low, the meteorological situation
changes suddenly over the ocean, showing the cold tongue arrival located at
the Equator, which is associated with increased wind speed between the
Equator and the coast, as detailed by .
Regarding the precipitation occurrences discussed in the previous section,
the high surface anth-PM2.5 concentrations modeled around ϕ=12∘ N are due to a combination of low wind speed and low
precipitation rates. These meteorological conditions are representative of
stagnation, which accumulate pollutants in the lowest layers of the
troposphere.
For the surface wind direction, the main wind direction near the coast is the
southwest quarter during the whole period. There is no obvious link between
wind direction changes and PM2.5 highest values over the Cotonou–Niamey
meridional transect.
The large-scale variability of meteorological variables (precipitation and
wind speed) controls the period of high anthropogenic pollution from the
coast to the Sahel. However, it does not explain whether the high concentration
over the Sahel are linked with local emissions and/or with pollutants
transport from the coast.
Monthly mean vertical structureAnthropogenic pollution
We now focus on the vertical structure of the lower troposphere from the
surface to 4 km altitude in order to understand the causes of the high
anth-PM2.5 concentration over the Sahel. Monthly averages of
anth-PM2.5 concentration are analyzed together with wind circulation
(monthly averages correspond to consistent meteorological periods;
see Sect. ). In Fig. , the
modeled concentrations are spatially averaged over the Cotonou–Niamey
meridional transect. Three isocontours (3, 4 and 5 µg m-3)
are used to follow the anthropogenic pollution patterns. Results are
presented at 00:00 UTC, when the NLLJ is well established (during the day, the
pollution is more mixed in the PBL).
Vertical cross section of the meridional wind (shading in
m s-1) mean over (a) May, (b) June and (c) July, at 00:00 UTC along a
meridional transect from 2 to 19∘ N and averaged from
2 to 3∘ E including Cotonou (Benin) and Niamey (Niger).
Orange and violet shading represents anthropogenic PM2.5 concentrations
of = 4.0 µg m-3 and = 5.0 µg m-3. Vectors
represent the wind field in the plan of the transect (with an aspect ratio of
500 between the meridional and the vertical components). The green line is
the PBL height (m). The gray vertical dash line is the latitude of the
coast.
For the 3 months, the meridional wind is lower at the surface than in the
boundary layer from the coast to ∼ 9∘ N, highlighting the
well established NLLJ. Above the northward monsoon flux, there is the SAL
associated with southward winds. The highest southward wind speed in the core
of the SAL between 11 and 16∘ N in latitude and 2 to 4.5 km
altitude is the African easterly jet (AEJ).
Regarding the wind, two atmospheric cells are modeled during the 3 months
(Fig. ). There is a large cell going northward at the
surface within the monsoon flow, and going backward toward the south with the
SAL (or AEJ), located at ∼ 2 km altitude, between the coast and
∼ 16∘ N. There is also a small cell turning in the same
direction (counterclockwise) at ∼ 2 km altitude, with the downdraft
at 6∘ N and the updraft at 7∘ N. In May and June, the small
cell is included in the large cell, while in July they are disconnected.
Regarding anth-PM2.5 concentration, these two atmospheric cells seem to
interact with the anthropogenic pollution because the anth-PM2.5
isocontours shape appear to be driven by the wind patterns. The large
atmospheric cell induces a recirculation of the modeled anthropogenic plume,
ranging from ϕ=6 to 18∘ N in latitude and 0.5 to 3 km
altitude (anth-PM2.5 isocontour of 3 µg m-3). The center
of this cell is located at ϕ≈ 14∘ N during the 3 months studied. In May, an important part of the pollution from the coast is
transported at altitude within the NLLJ above the PBL (displayed by the
anth-PM2.5 isocontour of 4 µg m-3). In June, there is
high concentration at altitude over the Sahel (displayed by the
anth-PM2.5 isocontour of 5 µg m-3), which suggests that
the atmospheric cell concentrates pollutants. Note that in July, high
anth-PM2.5 concentrations are only modeled along the coast and close to
the surface.
Air masses transport anthropogenic pollutants from the coast to the Sahel.
High surface concentrations of anth-PM2.5 are modeled at the latitude of
the coastal urbanized areas (ϕ=6.3∘ N), leading to a plume to
the north within the NLLJ and concentrating pollutants over the Sahel.
Coastal versus Sahelian pollutant meridional transport
A tracer experiment has been set up to analyze whether the main contributors to
the Sahelian maximum are emitted locally or remotely at the coast. Gaseous
tracers are released at the two major cities of the meridional transect
(Cotonou in Benin and Niamey in Niger). The tracer experiment uses arbitrary
units (a.u.) and considers the same quantity of tracers emitted in each town. The
tracers are constantly released from the 1 to 30 June. The emission altitude
occurs in the PBL (0–200 m) from 1 June to 30 June. Results are
presented averaged during the 10 last days at 00:00 UTC as in the previous
section. We consider either only coastal emissions or only Sahelian emissions
in order to observe where air recirculation may concentrate the pollutants
(Fig. ).
Vertical cross section of the meridional wind (shading in
m s-1) along a meridional transect from 2 to
19∘ N, averaged from 2 to 3∘ E including Cotonou (Benin)
and Niamey (Niger) and averaged over 20 to 30 June at 00:00 UTC. Isocontours
represent gaseous tracer concentration continuously emitted (in arbitrary
units) from the 1 to 30 June at (a) Niamey (Niger)
and (b) Cotonou (Benin). Brown and yellow shading represents tracer
concentration of 1 a.u. and 10 % a.u., respectively. The green line is the PBL height
(m). Vectors represent the wind field in the plan of the transect (with an
aspect ratio of 500 between the meridional and the vertical components). The
white vertical dash line is the latitude of the coast.
Tracers emitted at the coast indicate that there is an important transport of
coastal pollutants toward the north in the PBL. On the other hand, there is
no significant transport of tracers emitted in the Sahel toward the coast. In
Niamey, Cotonou tracer concentration is about 9 % (of the 1 a.u.
isocontour presented in Fig. a), while in Cotonou, Niamey
tracer concentration is about 0.03% (of the 1 a.u. isocontour presented in
Fig. b). In the HTAP anthropogenic inventories (presented
in Fig. ), the anth-PM2.5 (anth-CO) is
∼ 103 (735) kg km-2 day-1 in Niamey and ∼ 438
(7707) kg km-2 day-1 in Cotonou. Therefore, an important part of
the pollution over the Sahel has been emitted at the coast and it contributes
to a maximum of anthropogenic pollution in June over the Sahel. In
conclusion, the high concentration over the Sahel is due to the existence of
a meridional atmospheric cell, which acts to accumulate pollutants emitted
locally and remotely at the coast.
Impact of coastal dynamics on anthropogenic pollution
In order to better characterize coastal pollution, anth-PM2.5 for the
period of 8 to 15 June 2006 is now described at hourly temporal resolution.
This week includes a large variability of low to high surface concentrations.
Surface hourly pollution variability
Time–latitude average (Hovmöller) of surface anthropogenic
PM2.5 concentration (µg m-3) averaged along a meridional
transect between 2 and 3∘ E from 1 to 17 June 2016. Black vertical
bars delimit the periods of the day (00:00, 06:00, 12:00, 18:00 UTC). White
isocontours present precipitation rate of 3.0 mm h-1. Orange
isocontour represents the surface anthropogenic PM2.5 concentration of
4 µg m-3.
The hourly surface anth-PM2.5 concentrations are shown in
Fig. over the Cotonou meridional transect with
Hovmöller diagrams. The beginning of the week is associated with lower
concentrations, as already described in Fig. . For most
days, the highest concentrations are modeled between 18:00 and 00:00 UTC from
the coast to 8∘ N. This coincides with the lowest boundary layer
height, which concentrates urban emissions (i.e waste burning and traffic) in
a thin layer (not shown). At the coast, anth-PM2.5 concentration
decreases at night (between 00:00 and 06:00 UTC) and increases again in the
morning (around 06:00 UTC). This feature follows traffic emissions, which are
included in the anthropogenic emission inventories. It seems that urban
plumes start from the coastline at sunset (18:00 UTC) and are transported to
the north in the following hours.
There is a transition from low to high concentration of anthropogenic
pollution from 8 to 12 June. Anthropogenic pollution is modeled over the sea
from 10 to 11 June. It is interesting to note that precipitation occurs
inland on 11 June (between 18:00 and 00:00 UTC), and then high modeled
concentrations persist during the night of 11–12 June. This precipitation
event reflects a change in the wind patterns, which induces a change in the
transport of pollutants, leading to surface concentrations up to
8 µg m-3 in Cotonou.
Contribution of other cities at Cotonou
At the coast, the wind direction comes from the sector S-SW, but there are
diurnal variations which could affect the air pollution along the coastline.
In order to distinguish pollutant transport from the different large coastal
cities, a tracer experiment has been set up. The tracers are constantly
released in the lowest model layers ranging from surface to 500 m a.s.l.
(above sea level). This altitude corresponds to emission below or within the
NLLJ. Three point sources have been defined: Accra in Ghana (5.6∘ N,
0.2∘ W), Lome in Togo (6.2∘ N, 1.2∘ E) and Cotonou
in Benin (6.4∘ N, 2.4∘ E). The results are presented over
the Cotonou meridional transect (Fig. ). We aim at
evaluating the impact of the Cotonou local emissions versus emissions from
distant areas transported toward Cotonou. The emission (in arbitrary units)
has the same magnitude in each city without any daily variation.
Time–latitude average (Hovmöller) of gaseous tracer concentration
(a.u.) averaged along a meridional transect between 2 and 3∘ E
centered on Cotonou (Benin) from 8 to 15 June 2006. Emissions are set up with
a constant emission between 0 and 500 m altitude at (a) Cotonou
(Benin), (b) Lome (Togo) and (c) Accra (Ghana). Black
vertical bars delimit the periods of the day (00:00, 06:00, 12:00,
18:00 UTC).
As expected, the surface concentrations of Cotonou due to local emission are only the highest at the coast. The diurnal cycle of pollutant transport
appears clearly with the highest concentrations exported at the beginning of
the night (18:00 UTC), when the boundary layer height decreases quickly and
when the establishment of the NLLJ occurs. Up to 9∘ N, high tracer
concentrations are transported from Cotonou far from the point source. The
Cotonou plume always transports tracers up to 7∘ N; for some days,
these plumes may reach the latitude of 9∘ N during the night between
18:00 and 06:00 UTC.
At Cotonou, the modeled tracer concentrations released from Lome or Accra are
logically lower because the source points are not in the Cotonou meridional
transect. The same kind of northward transport is observed but pollutant
transport from Accra and Lome reaches Cotonou in the morning between 06:00
and 12:00 UTC for the Lome plume, and in the afternoon between 12:00 and
18:00 UTC for the Accra plume. This result is consistent with a transport speed
between 10 and 20 km h-1 (the Lome–Cotonou distance is about
150 km, and Accra–Cotonou about 300 km) and synoptic wind direction from
the sector SW. In arbitrary units and with the same emission at the three
cities, modeled concentrations between 7 and 8∘ N are
similar for the Lome and Cotonou plumes, and lower for the Accra plume.
The comparison between the three plumes shows a specific behavior during
10–12 June as the Cotonou pollution is not exported to the north:
On 10 June, the Lome plume is clearly exported over the sea and very
high concentrations are noticed over Cotonou. The same behavior is observed
for the Accra plume with lower concentration because this location is further
from Cotonou. Indeed, the Lome and Accra plumes reach Cotonou after being
transported over the sea, which suggests that pollutants at the three cities
have been transported eastward, leading to plumes mixing at the Cotonou location.
On 11 June, there are still high concentrations over Cotonou due to the
Accra plume probably driven by the same meteorological conditions, but it does
not affect the Lome plume. This suggests a perturbation affecting Accra in particular.
On 12 June, there is an important transport of Cotonou pollution to the
north. At the same time, this is the only day when Lome and Accra plumes do not
reach Cotonou, as they are shifted to the north at 7∘ N when crossing the
Cotonou meridional transect.
All these results suggest a fast change of the meteorological situation,
leading to air pollution. In the next section, the specificity of the
vertical wind structure during this period will be studied in detail.
Disturbed atmospheric dynamics and pollution transport
In this section the analysis is refined to two periods of 2 days on 8–9 and
11–12 June 2006 in the Cotonou area. The first corresponds to non-perturbed
monsoon flow situation leading to low anthropogenic pollution, while the second
corresponds to a perturbed situation leading to high anthropogenic pollution.
Vertical cross section of the meridional wind (shading in
m s-1) along a meridional transect from 5 to
10∘ N and averaged from 2 to 3∘ E including
Cotonou (Benin). The two orange isocontours are tracer concentrations
released in Cotonou and in Lome, respectively bold and dashed, with same
threshold values (in arbitrary units). Vectors represent the wind field in the
plan of the transect (with an aspect ratio of 500 between the meridional and
the vertical components). The green line is the PBL height (m). The white
vertical dash line is the latitude of the coast.
Evolution of the vertical structure
In order to focus on the day/night transport from the coast to the north
(described in Sect. ) and the changes in dynamical regimes
during this period (described in Sect. ), results are
presented as vertical slices in Fig. , averaged along the
Cotonou meridional transect. The tracer concentrations, emitted separately
at Lome and Cotonou, are presented as isocontours of threshold values: the
emissions being arbitrary, the modeled concentrations are also arbitrary. But
the same threshold is used for the two emission locations; thus,
concentration magnitudes are comparable. The wind vectors
(meridional/vertical components) are superimposed on the figures to highlight
the vertical cells. The meridional wind is also presented as color shading
for the NLLJ intensity.
The first period (8 June at 23:00 UTC and 9 June at 11:00 UTC) corresponds to a
classical monsoon case, often observed and described in the literature
. At night, surface pollutants are concentrated
in a shallow layer (less than 200 m), corresponding to nocturnal surface layer
and to the lowest part of the NLLJ (represented by the dark blue shaded
area in Fig. ). The Cotonou and Lome plumes are mixed.
During the day, the convection induces mixing in the boundary layer, which
reaches 1500 m at 11:00 UTC over the continent. The Lome plume does not reach
the coast, but it crosses the Cotonou meridional transect further to the
north (∼ 7∘ N).
On 8–9 June, an updraft–downdraft convective cell is clearly observed during
the day and at night, with ascendent wind at 7∘ N and subsident wind
at 6.3∘ N (the Cotonou site latitude). This circulation has already
been observed for the whole studied period in Sect. .
This is not a modeled land–sea breeze because it turns in the same direction
day and night. Land–sea breezes have not been explicitly modeled because of
the too coarse resolution (about 20 km).
The second period (11 June at 23:00 UTC and 12 June at 11:00 UTC) corresponds to
a disturbed case compared to what is usually observed in this region and for
this month. Indeed, for 11 June at 23:00 UTC, the NLLJ is not present near the
coast and the wind is weak from the coast to 8∘ N. The modeled
nocturnal PBL height is very low (less than 50 m). The Lome plume is not
present over Cotonou. On 12 June at 11:00 UTC, air subsidence is modeled from
7 to 10∘ N. The isocontours of concentrations due to
emissions in Lome and Cotonou are at the same latitude, corresponding to an
iso-latitudinal transport, along the coast. Compared to 8–9 June, there is no
coastal cell located over the emission region. A larger cell is modeled, with
high meridional wind speed (up to 4 m s-1). The subsidence, located at
ϕ> 7∘ N, imports upper air masses from the free troposphere
and blocks the northward transport of the coastal pollutants.
Specificity of 11–12 June 2006
EUMETSAT visible image of the Cotonou area of the 11 June 2006
at 19:00 UTC (from NAScube http://nascube.univ-lille1.fr). The red
ellipse is the convective cell location.
(a) Map of Cotonou area for the 11 June 2006 at 19:00 UTC with
wind vectors at 10 m (green arrows), precipitation (blue shading), and
anthropogenic PM2.5 concentration (red shading); (b) isocontours of
tracer concentration on 11 June at 19:00 UTC (solid line) and
on 12 June at 01:00 UTC (dashed line), released in Accra (Ghana) in green, Lome (Togo) in red,
Cotonou (Benin) in orange, and Lagos (Nigeria) in violet. Blue dots show
precipitation location each hour between 11 June at 19:00 UTC and 12 June at
01:00 UTC (the size of blue dots depends on precipitation amount).
In this last analysis, we focus on 11–12 June to understand which
meteorological conditions have led to an important modeled anthropogenic
PM2.5 event at Cotonou. have shown that a large
MCS has occurred over Ghana due to convective instabilities at the border of
Togo, Ghana and Burkina Faso. Some spots of convection (“popcorn”
convection) over a large region including Cotonou have been identified on 11 June.
An isolated convective cell lasting a few hours coming from southeast
and moving northwest has crossed the coastline over Cotonou at around
18:00 UTC (Fig. ), which is well in agreement with the
modeled location (Fig. ). When precipitation is inland
between 19:00 and 23:00 UTC, the wind speed is zero over the coast because
the monsoon flux is blocked (Fig. a). During these
specific meteorological conditions, the high anth-PM2.5 surface
concentrations are thus due to an accumulation of pollution over a few
hours (from 19:00 to 23:00 UTC).
The same tracer experiment as described in Sect. is
used to confirm the accumulation of pollutants and to distinguish plumes of
the different cities. Gaseous tracers are released with the same emission at
four cities: Accra (Ghana), Lome (Togo), Cotonou (Benin) and Lagos in Nigeria
have been added (6.5∘ N,3.4∘ E) .
We can notice that the pollution emitted at the different cities west from
Cotonou is mixed at ϕ=7∘ N on 11 June at 19:00 UTC
(Fig. b). Six hours later, only the Cotonou plumes is
responsible for the high anth-PM2.5, which confirms pollutants
accumulation because it has been blocked by the downdraft of the
precipitation system over 6 h.
This result demonstrates that during the monsoon period, specific
meteorological conditions could lead to high pollution in the large Guinean coast
cities, although most of the time pollution emitted along the coastline
is quickly transported to the north.
Conclusions
West African pollution was studied using both models and observations during
May, June and July 2006. This corresponds to the beginning of the West
African monsoon and includes the AMMA campaign observational period. The
focus was on urbanized areas located along the Guinean Gulf coast and known
as large gas and aerosol emitters. In addition to these anthropogenic
emissions, the coast is often under the influence of long-range transport of
mineral dust and biomass burning emissions. The analyses are performed for CO
and PM2.5 over a large domain to include all sources: Central Africa for
biomass burning, Sahel and Sahara for mineral dust, and a large part of the
Guinea Gulf for sea salt.
The first analysis was devoted to estimating the relative contribution of each
source during the 3 months in Cotonou (Benin), Djougou (Benin) and Niamey
(Niger). It was shown that the surface concentrations of PM2.5
constantly increase during the period. The mineral dust relative contribution
remains low close to the coast, showing that, on monthly average, pollution
during this period is not dominated by mineral dust transport events. On the
other hand, the biomass burning emissions increase from May to July. The
anthropogenic part is stable during the whole period for the three studied
sites at ∼ 50 % for CO and ∼ 15 % for PM2.5.
The second part of the study analyzed the anthropogenic contribution to CO
and PM2.5 along a Cotonou–Niamey meridional transect. These pollutants
are transported from the coast to the north as far as the Sahel
(13∘ N). The northward limit of the transport corresponds to the
Intertropical Discontinuity. It was also shown that there are alternating
periods of high and low concentration from Cotonou to Niamey with a weekly
frequency. To understand this variability, meteorological variables were
investigated. The highest surface pollutants concentrations occurred when
there is no precipitation and low wind speed.
In order to better understand the meridional transport and the occurrence of
high pollutant concentrations over the Sahel (∼ 13∘ N),
monthly averages of vertical wind structure were analyzed. From May to June,
a large atmospheric cell going from the coast to the Sahel remains present,
and it has been identified as responsible for pollutant accumulation over the
Sahel emitted locally and remotely at the coast.
Focus was put on coastal dynamics and pollution transport during a
restricted period, from 8 to 15 June 2006, which included high and low
concentrations of anthropogenic pollution. To isolate the coastal dynamics
impacts on several city plumes along the coastline, a tracer experiment was
designed with emissions at Accra (Ghana), Lome (Togo) and Cotonou (Benin).
The tracer concentrations confirm that, in Cotonou, the modeled
concentrations are due to both local and remote emissions. A meridional
transport of the anthropogenic pollution from the coast to the north has been
highlighted at night linked with the nocturnal low-level jet close to the
coast.
Finally, two contrasting anthropogenic pollution situations were analyzed in
detail. The first situation (8–9 June) corresponds to low anthropogenic
pollution during a “typical” case of monsoon dynamics, while the second
situation (11–12 June) corresponds to a disturbed meteorological situation
due to a convective system. During 11–12 June, air subsidence is modeled at
latitude 7∘ N, which imports upper air masses from the free
troposphere, limiting the northward transport of the coastal pollutants.
Concerning air quality and climate policy development, we showed that the
export of anthropogenic pollutant from the Guinean coast toward the north
could lead to cross boundary pollution plumes. This result will be confirmed
by comparison to the 2016 DACCIWA campaign observations in order to propose a
strategy to reduce atmospheric pollution in West Africa.
The data used in this article are available at
http://baobab.sedoo.fr/AMMA/ (Borbon, 2006).
The authors declare
that they have no conflict of interest.
This article is part of the special issue “Results of the
project `Dynamics-aerosol-chemistry-cloud interactions in West Africa'
(DACCIWA) (ACP/AMT inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
The research leading to these results has received funding from the European
Union 7th Framework Programme (FP7/2007-2013) under grant agreement no.
603502 (EU project DACCIWA: Dynamics-aerosol-chemistry-cloud interactions in
West Africa). This work has been supported by the African Monsoon
Multidisciplinary Analysis (AMMA) project. Based on a French initiative, AMMA
was created by an international scientific group and is currently funded by a
large number of agencies, especially from France, the UK, the USA and various African
countries. The authors wish to thank the SAFIRE (Service des Avions Francais
Instruments pour la Recherche en Environnement) for preparing and delivering
the research aircraft (ATR-42). We thank Philippe Goloub and Didier
Tanre for their effort in establishing and maintaining AERONET sites in
Djougou (Benin) and Banizoumbou (Niger).
Edited by: Mathew Evans
Reviewed by: two anonymous referees
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