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Chemical composition and sources of particle pollution in affluent and poor
neighborhoods of Accra, Ghana
Article  in  Environmental Research Letters · December 2013
DOI: 10.1088/1748-9326/8/4/044025
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IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 8 (2013) 044025 (9pp) doi:10.1088/1748-9326/8/4/044025
Chemical composition and sources of
particle pollution in affluent and poor
neighborhoods of Accra, Ghana
Zheng Zhou1,2, Kathie L Dionisio1,2, Thiago G Verissimo3,
Americo S Kerr3, Brent Coull2,4, Raphael E Arku2, Petros Koutrakis2,
John D Spengler2, Allison F Hughes5, Jose Vallarino2,
Samuel Agyei-Mensah6 and Majid Ezzati7
1 Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA
2 Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
3 Institute of Physics, University of Sao Paulo, Sao Paulo, Brazil
4 Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
5 Department of Physics, University of Ghana, Legon, Ghana
6 Department of Geography and Resource Development, University of Ghana, Legon, Ghana
7 Medical Research Council–Public Health England Centre for Environment and Health, Department of
Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
E-mail: majid.ezzati@imperial.ac.uk
Received 7 September 2013
Accepted for publication 10 October 2013
Published 30 October 2013
Online at stacks.iop.org/ERL/8/044025
Abstract
The highest levels of air pollution in the world now occur in developing country cities, where
air pollution sources differ from high-income countries. We analyzed particulate matter (PM)
chemical composition and estimated the contributions of various sources to particle pollution
in poor and affluent neighborhoods of Accra, Ghana. Elements from earth’s crust were most
abundant during the seasonal Harmattan period between late December and late January when
Saharan dust is carried to coastal West Africa. During Harmattan, crustal particles accounted
for 55 µg m−3 (37%) of fine particle (PM2.5) mass and 128 µg m−3 (42%) of PM10 mass.
Outside Harmattan, biomass combustion, which was associated with higher black carbon,
potassium, and sulfur, accounted for between 10.6 and 21.3 µg m−3 of fine particle mass in
different neighborhoods, with its contribution largest in the poorest neighborhood. Other
sources were sea salt, vehicle emissions, tire and brake wear, road dust, and solid waste
burning. Reducing air pollution in African cities requires policies related to energy,
transportation and urban planning, and forestry and agriculture, with explicit attention to
impacts of each strategy in poor communities. Such cross-sectoral integration requires
emphasis on urban environment and urban poverty in the post-2015 Development Agenda.
Keywords: air pollution, particulate matter, source apportionment, urbanization, sustainable
development, Africa
S Online supplementary data available from stacks.iop.org/ERL/8/044025/mmedia
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work
must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
1748-9326/13/044025+09$33.00 1 ©c 2013 IOP Publishing Ltd Printed in the UK
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
1. Introduction the samples were quantified by energy dispersive x-ray
fluorescence (ED-XRF) at the Institute of Astronomy,
The highest levels of air pollution in the world now occur in Geophysics and Atmospheric Science, University of Sao
cities in Asia, Middle East, and Africa [1]. In these settings, Paulo, Brazil. Measured elements in our study included
the concentrations of fine particles (less than 2.5 µm in sodium (Na), magnesium (Mg), aluminum (Al), silicon
aerodynamic diameter; PM2.5), which are associated with (Si), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca),
hazardous effects on health, approach or reach 100 µg m−3, titanium (Ti), vanadium (V), chromium (Cr), nickel (Ni),
compared to less than 20 µg m−3 in most European and manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), bromine
North American cities [1]. The sources of air pollution in (Br), and lead (Pb). We converted the major crustal elements
developing country cities include those that are common to their most abundant oxide forms as most of earth’s crust
in high-income nations, e.g., vehicle emissions, as well as consists of mineral oxides. We also converted sulfur (S) to
biomass and coal combustion for household and commercial sulfate (SO2−4 ), the most common form in the atmosphere.
purposes and resuspended dust from unpaved roads. In coastal Other elements, whose forms in the atmosphere vary by
West Africa, sea salt and long-range-transported Saharan dust source, are reported in their elemental form. Gallium (Ga),
may also be sources of particles in some seasons [2–4]. selenium (Se), rubidium (Rb), yttrium (Y), niobium (Nb),
Yet little is known about the relative contributions of these barium (Ba), and hafnium (Hf) were excluded from the
sources to pollution levels in African cities, where the urban analysis because a relatively large number of the measured
population is growing faster than any other region; neither is concentrations were below the limit of detection.
it known how the source diversity affects particle chemistry, Black carbon (BC) concentrations were estimated
which may in turn affect both health hazards and impacts on primarily using data on reflectance coefficients. For 52
anthropogenic climate change. site-days, we also collected co-located PM samples on
We conducted a study in Accra, Ghana, one of Africa’s quartz fiber filters, which were used to directly measure
fastest growing cities to understand the levels, chemical BC concentrations. We used site-days with both direct
properties, and sources of particle pollution. Three quarters measurement and data on reflectance coefficient to develop
of sub-Saharan Africa’s population use biomass fuels as a regression equation that related the natural logarithm (ln) of
their main source of energy, including over one half of BC concentration to the reflectance coefficient. The regression
urban households [5, 6]. In Accra, and other developing equation was then used to estimate the BC concentrations of
country cities, there are significant differences in road the remaining samples whose reflectance, but not BC, had
conditions, traffic patterns, and fuel use between the affluent been measured. We used the positive matrix factorization
neighborhoods and poor ‘slum’ areas [6–8]. For this reason, (PMF) of the elemental mass to identify and quantify the
we collected data at sites located in poor and affluent sources of PM at five sites [9].
neighborhoods. Details on methods for particle sample collection,
measurement of total and elemental mass concentrations, and
2. Materials and methods the PMF model are described in the supplementary material
(available at stacks.iop.org/ERL/8/044025/mmedia).
We collected particulate matter (PM) samples between
September 2007 and August 2008 in four neighborhoods 3. Results
of varying degrees of poverty and affluence. The four
neighborhoods lie on a line from the coast to Accra’s northern 3.1. Particle chemical composition
boundaries: James Town/Usher Town (JT), Asylum Down
(AD), Nima (NM) and East Legon (EL) (figure 1). JT and Particle mass concentrations were reported elsewhere [10].
NM are densely populated low-income communities where Here, we analyzed and report the elemental composition
most residents use biomass for cooking at home and for of PM2.5 and PM10; PM10 includes both fine and coarse
street food. AD is a middle-class neighborhood and EL is fractions in the respirable range. Total particle mass, crustal
an upper-class, sparsely-populated residential neighborhood components like SiO2, and to a lesser extent K peaked
where most families live on large plots of land and in modern sharply in all neighborhoods between December and February
low-rise homes. Fewer people use biomass fuels in AD and (figure 2), the seasonal Harmattan period when northeast trade
EL than in JT and NM. winds blow from the Sahara at an altitude of about 1500 m,
Between September 2007 and August 2008, we simulta- carrying large amounts of Saharan dust and emissions from
neously operated five monitoring sites in four neighborhoods dry-season bushfires [2–4]. SO2−4 also had a strong seasonal
(JT, AD, NM-1, NM-2, and EL). Measurements were done for pattern, peaking in January and August and being lowest
one 48-h period every six days. We also used geo-coded data in May and October. The latter months coincide with rainy
from the Ghana Population and Housing Census on household seasons, when wet deposition helps remove the water-soluble
socioeconomic status and fuel use and data from the Ghana SO2−4 . In contrast to these components, chlorine concentration
Survey Department on road locations and types. changed little over time but had a noticeable spatial pattern,
PM mass concentrations were measured on a Mettler being highest in JT and lowest in EL; JT also had higher
Toledo MT5 microbalance at the Harvard School of sodium (Na) concentrations; see tables 1 and 2. JT is close to
Public Health Laboratory. The elemental concentrations of the ocean and hence has more particles from sea spray. Black
2
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Figure 1. Study neighborhoods and measurement sites. The polygons on the central panel show census enumeration areas (EAs). Each EA
has approximately the same population; hence the area of an EA is inversely related to population density. EAs are categorized according to
quintile in terms of per cent of household using biomass fuels. Each color represents a different quintile. The sites were at locations that
were typical of each neighborhood’s living environment, with the EL site being far from major traffic and the AD site being next to a road
with moderate traffic. In NM we also had a second site next to a road with heavy traffic. The distances of measurement sites to the ocean
coast were: JT 0.5 km, AD 3 km, NM 4.5 km, and EL 9 km.
carbon, K, and SO2−4 were also higher in JT, the neighborhood in the fine fraction was below 40% for crustal oxides (MgO,
with the highest biomass use density (figure 1). K and SO2− Al2O3, SiO2, CaO, TiO2, MnO, and Fe4 2O3) and for elements
are linked to biomass burning and BC to emissions from in sea salt (Na and Cl), indicating that they were mostly in the
combustion sources including biomass smoke. Potassium in form of coarse particles. In contrast, 60% or more of SO
2−
4 ,
biomass smoke is emitted mostly as potassium salts such as K, Ni, Zn, Br, Pb and BC mass was in the fine fraction,
which can penetrate deeper into lungs. These elements are
KCl and K2SO4; with aging, most KCl particles are converted mainly associated with biomass and solid waste burning,
to K2SO4 [11, 12]. motor vehicle emissions, and industrial sources.
Between 49% and 59% of PM10 mass was in the fine There was strong correlation among the concentrations
fraction at different sites (table 3). The proportion of mass of crustal elements (tables S1 and S2, available at
3
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Figure 2. Concentrations of total particle mass and of selected elements for (a) PM2.5 and (b) PM10 over the study period at the five
measurement sites. Changes along the horizontal direction show variations over time and the spread along the vertical direction shows
variation across measurement sites. For comparison, the World Health Organization (WHO) guideline for annual mean PM2.5 concentration
is 10 µg m−3.
stacks.iop.org/ERL/8/044025/mmedia), especially during non-Harmattan months, when a sea breeze is more frequent.
Harmattan (correlation coefficients ≥ 0.9), when the Saharan Similarly, there was moderate correlation among Br, Pb, and
dust falls on the city. During Harmattan, crustal elements Zn, which are all present in vehicle emissions, tire and brake
were also highly correlated with total particle mass. Sea salt wear, road dust, and in smoke generated from burning solid
elements (Na and Cl) were also correlated with one another in waste [13, 14].
4
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Table 1. Average concentrations of total PM2.5 mass and its chemical components at five monitoring sites during the non-Harmattan
months.
Chemical
species AD (n = 35) EL (n = 37) JT (n = 36) NM-1 (n = 39) NM-2 (n = 52)
Total massa 27 ± 8 23 ± 8 50 ± 12 28 ± 8 34 ± 9
MgO 103 ± 128 (0.4%)b 141 ± 187 (0.6%) 179 ± 192 (0.4%) 125 ± 155 (0.5%) 132 ± 153 (0.4%)
Al2O3 909 ± 936 (3.4%) 1212 ± 1245 (5.4%) 1335 ± 1549 (2.6%) 1103 ± 1140 (4.0%) 1514 ± 1102 (4.4%)
SiO2 2334 ± 2603 (8.6%) 3115 ± 3399 (13.8%) 3540 ± 4343 (7.0%) 2814 ± 3165 (10.2%) 3664 ± 2977 (10.7%)
CaO 319 ± 223 (1.2%) 351 ± 291 (1.6%) 543 ± 424 (1.1%) 404 ± 264 (1.5%) 635 ± 311 (1.9%)
TiO2 53 ± 50 (0.2%) 68 ± 65 (0.3%) 75 ± 83 (0.1%) 64 ± 59 (0.2%) 93 ± 60 (0.3%)
MnO 10 ± 6 (<0.1%) 9 ± 7 (<0.1%) 11 ± 9 (<0.1%) 9 ± 7 (<0.1%) 12 ± 6 (<0.1%)
Fe2O3 495 ± 372 (1.8%) 604 ± 470 (2.7%) 594 ± 584 (1.2%) 534 ± 420 (1.9%) 771 ± 427 (2.3%)
SO2−4 1985 ± 778 (7.3%) 1815 ± 753 (8.1%) 2538 ± 829 (5.0%) 1894 ± 696 (6.8%) 2028 ± 726 (5.9%)
Na 295 ± 217 (1.1%) 239 ± 174 (1.1%) 502 ± 336 (1.0%) 284 ± 266 (1.0%) 226 ± 143 (0.7%)
K 585 ± 233 (2.2%) 633 ± 230 (2.8%) 1873 ± 414 (3.7%) 858 ± 248 (3.1%) 936 ± 232 (2.7%)
Cl 239 ± 269 (0.9%) 152 ± 119 (0.7%) 1450 ± 817 (2.9%) 346 ± 258 (1.2%) 366 ± 209 (1.1%)
V 1 ± 1 (<0.1%) 1 ± 1 (<0.1%) 1 ± 1 (<0.1%) 1 ± 1 (<0.1%) 1 ± 1 (<0.1%)
Cr 1 ± 1 (<0.1%) 1 ± 1 (<0.1%) 2 ± 1 (<0.1%) 1 ± 1 (<0.1%) 2 ± 1 (<0.1%)
Ni 4 ± 1 (<0.1%) 3 ± 1 (<0.1%) 4 ± 1 (<0.1%) 3 ± 2 (<0.1%) 3 ± 1 (<0.1%)
Cu 6 ± 5 (<0.1%) 2 ± 2 (<0.1%) 5 ± 4 (<0.1%) 5 ± 4 (<0.1%) 6 ± 6 (<0.1%)
Zn 38 ± 15 (0.1%) 15 ± 8 (0.1%) 45 ± 16 (0.1%) 29 ± 13 (0.1%) 32 ± 11 (0.1%)
Br 29 ± 22 (0.1%) 10 ± 4 (<0.1%) 36 ± 23 (0.1%) 23 ± 21 (0.1%) 23 ± 19 (0.1%)
Sr 2 ± 1 (<0.1%) 3 ± 2 (<0.1%) 4 ± 3 (<0.1%) 3 ± 2 (<0.1%) 4 ± 2 (<0.1%)
Zr 1 ± 1 (<0.1%) 2 ± 1 (<0.1%) 2 ± 2 (<0.1%) 1 ± 1 (<0.1%) 2 ± 1 (<0.1%)
Pb 14 ± 5 (0.1%) 6 ± 2 (<0.1%) 31 ± 15 (0.1%) 13 ± 4 (<0.1%) 14 ± 4 (<0.1%)
BCc 6603 ± 1642 3676 ± 1774 (16.1%) 8613 ± 1509 5176 ± 1584 (18.5%) 6200 ± 1268 (18.2%)
(24.4%) (17.2%)
% of total mass 52% 52% 43% 49% 49%
accounted
a Units are in ng m−3 except for total mass, which is in µg m−3.
b Mean ± standard deviation. Numbers in parenthesis show the per cent of total mass.
c The 52 site-days with direct BC measurements using PM samples on quartz fiber filters also provided data on organic carbon (OC),
measured following NIOSH Protocol 5040. For those 52 samples, OC accounted for 11–44% (mean 26%) of total PM mass. Mean OC/BC
ratio in these samples was 1.7 (SD 0.6). In these samples, the correlation between OC and BC was around 0.6.
3.2. Source contributions 10.6 µg m−3 in AD and 11.2 µg m−3 in EL. Road dust and
traffic aerosols had a more important role in the two sites near
We identified 5–6 potential sources for fine particles and traffic routes, one of NM’s sites by a busy road and the AD
4–5 for PM10 at different sites during non-Harmattan months site. A last source, which is likely to be burning of solid waste,
(figure 3). The estimated contributions of sea salt to total was identified for fine particles in all neighborhoods except
PM2.5 mass ranged between around 2 µg m−3 in NM-2 and affluent EL, being largest in JT where old tires and other solid
EL (4.5 and 9 km from the coast) to 13.9 µg m−3 (25% waste are commonly burned.
of total PM2.5 mass) in JT (500 m from the coast) during There was about 10 times as much particle mass from
non-Harmattan months. Similarly, sea salt contribution to crustal sources during Harmattan as there had been in other
PM10 was smallest in EL and largest in JT, accounting for
3 3 months (figure 3). During Harmattan, there was also an6.4 µg m− (14%) and 27.5 µg m− (31%) of total PM10 increase in particle pollution from resuspended road dust and
mass, respectively. In contrast to sea salt, crustal sources from biomass burning. The absolute amount of particle mass
made up a larger share of total particle mass in EL, the from other sources changed little, leading to a reduction in
northernmost neighborhood. For example 38% of PM2.5 and their share of total mass.
46% of PM10 mass in EL were from crustal sources, compared
to only 17% and 16% in JT. Despite having a larger share
from crustal sources, lower total particle mass in EL meant 4. Strengths and limitations
that the absolute contribution of crustal aerosols was only
slightly higher than other neighborhoods, e.g., 10.5 µg m−3 To our best knowledge, this paper uses one of the richest data
in PM2.5 compared to 9.5 µg m−3 in JT. This higher absolute sets on particle air pollution levels, chemical composition,
contribution may have been because more spread-out low- and sources in a developing country city. The unique data
rise homes do not block the windblown Saharan or local dust. come from our own measurements in poor and affluent
Biomass smoke contributed more particle pollution in neighborhoods and in different seasons, combined with
NM and JT, where the density of households who use data from the Population and Housing Census and the
biomass fuels is substantially higher [6], than in AD and EL; Survey Department, all with detailed geospatial information.
biomass-related particles accounted for 15.7–21.3 µg m−3 of The consistent and comparable data in poor and affluent
PM2.5 mass in the former two neighborhoods, compared to neighborhoods alone allow us to examine, for the first time,
5
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Table 2. Average concentrations of total PM10 mass and its chemical components at five monitoring sites during the non-Harmattan months.
Chemical
species AD (n = 33) EL (n = 37) JT (n = 33) NM-1 (n = 40) NM-2 (n = 54)
Total massa 55 ± 18 46 ± 19 81 ± 22 51 ± 20 70 ± 21
MgO 454 ± 289 (0.8%)b 446 ± 342 (1%) 741 ± 346 (0.9%) 430 ± 335 (0.8%) 515 ± 329 (0.7%)
Al2O3 3026 ± 2220 (5.5%) 3646 ± 2516 (7.9%) 3088 ± 2626 (3.8%) 3239 ± 2535 (6.3%) 4882 ± 2780 (7.0%)
SiO2 8484 ± 6441 9749 ± 7245 8764 ± 7635 8744 ± 7126 (17.0%) 12 270 ± 7374
(15.5%) (21.1%) (10.8%) (17.6%)
CaO 1520 ± 788 (2.8%) 1340 ± 804 (2.9%) 2260 ± 1023 (2.8%) 1682 ± 958 (3.3%) 2653 ± 1127 (3.8%)
TiO2 212 ± 140 (0.4%) 233 ± 151 (0.5%) 215 ± 159 (0.3%) 218 ± 154 (0.4%) 343 ± 182 (0.5%)
MnO 26 ± 15 (<0.1%) 24 ± 17 (0.1%) 27 ± 18 (<0.1%) 25 ± 16 (<0.1%) 35 ± 18 (0.1%)
Fe2O3 2096 ± 1060 (3.8%) 2171 ± 1110 (4.7%) 1783 ± 1169 (2.2%) 1956 ± 1210 (3.8%) 2895 ± 1261 (4.1%)
SO2−4 2601 ± 847 (4.7%) 2138 ± 682 (4.6%) 3301 ± 879 (4.1%) 2276 ± 737 (4.4%) 2625 ± 758 (3.8%)
Na 1033 ± 624 (1.9%) 616 ± 463 (1.3%) 2021 ± 838 (2.5%) 739 ± 505 (1.4%) 739 ± 505 (1.4%)
K 880 ± 332 (1.6%) 874 ± 328 (1.9%) 2188 ± 490 (2.7%) 1123 ± 354 (2.2%) 1324 ± 356 (1.9%)
Cl 2023 ± 930 (3.7%) 1203 ± 570 (2.6%) 4871 ± 1362 (6.0%) 1711 ± 658 (3.3%) 2087 ± 733 (3%)
V 3 ± 1 (<0.1%) 3 ± 1 (<0.1%) 2 ± 1 (<0.1%) 2 ± 2 (<0.1%) 3 ± 2 (<0.1%)
Cr 4 ± 1 (<0.1%) 4 ± 1 (<0.1%) 4 ± 2 (<0.1%) 3 ± 2 (<0.1%) 5 ± 2 (<0.1%)
Ni 3 ± 1 (<0.1%) 3 ± 1 (<0.1%) 3 ± 1 (<0.1%) 3 ± 1 (<0.1%) 3 ± 1 (<0.1%)
Cu 10 ± 5 (<0.1%) 4 ± 2 (<0.1%) 8 ± 4 (<0.1%) 8 ± 3 (<0.1%) 11 ± 5 (<0.1%)
Zn 55 ± 18 (0.1%) 26 ± 13 (0.1%) 71 ± 27 (0.1%) 41 ± 18 (0.1%) 55 ± 16 (0.1%)
Br 35 ± 24 (0.1%) 12 ± 5 (<0.1%) 44 ± 21 (0.1%) 28 ± 23 (0.1%) 32 ± 24 (<0.1%)
Sr 8 ± 4 (<0.1%) 8 ± 5 (<0.1%) 14 ± 6 (<0.1%) 10 ± 5 (<0.1%) 13 ± 6 (<0.1%)
Zr 5 ± 4 (<0.1%) 6 ± 4 (<0.1%) 5 ± 4 (<0.1%) 5 ± 4 (<0.1%) 8 ± 4 (<0.1%)
Pb 22 ± 6 (<0.1%) 9 ± 3 (<0.1%) 36 ± 14 (<0.1%) 17 ± 5 (<0.1%) 21 ± 5 (<0.1%)
BC 7407 ± 1664 3617 ± 1450 (7.8%) 9587 ± 1463 5358 ± 1884 (10.5%) 6283 ± 1476 (9.0%)
(13.5%) (11.8%)
% of total mass 54% 57% 48% 54% 53%
accounted
a Units are in ng m−3 except for total mass, which is in µg m−3.
b Mean ± standard deviation. Numbers in parenthesis show the per cent of total mass.
how air pollution levels, composition, and sources differ in for air pollution control in rapidly expanding developing
relation to socioeconomic status. country cities like Accra. First, the role of urban biomass
Similar to all field measurement studies, our work burning as a source of air pollution creates both policy
is affected by some limitations. Logistical difficulties and complexities and opportunities. Large-scale transitions to
time-intensive field work restricted our ability to run cleaner fuels such as liquefied petroleum gas (LPG) may
multiple measurement sites simultaneously in each study require targeted subsidies for fuel and/or financial assistance
neighborhood to assess within-neighborhood variation. It towards the initial cost of an LPG stove for poor households.
would have been desirable to conduct consecutive 48 h Perhaps more importantly, sustained use of clean fuels
measurements, but this was beyond our resources. requires improving the energy delivery and distribution
Limitations in the analysis include the reliance of infrastructure so that people can have regular trouble-free
source apportionment analysis on relatively stable source access to fuel purchase, currently not available in poor
profiles across samples. In reality, source profiles may neighborhoods [6]. On the other hand, addressing household
somewhat differ overtime, even at the same site. The fuel use in cities can take advantage of high population density
choice of the number of PM sources is based on the best and urban infrastructure versus in rural areas, where long
compromise between the goodness of model fit and the distances and poor roads and energy infrastructures make fuel
physical meaningfulness of the resolved factors; we tried the switching more challenging. In addition to urban biomass use,
source apportionment with different numbers of factors and long-range transport of particles from wild bushfires or from
compared the results before selecting the current number of land clearing/preparation for agriculture by burning may be
sources. In addition, labeling PM sources after PMF analysis responsible for urban particle pollution in West Africa [16].
also involves some subjectivity. All of these limitations should Reducing regional biomass pollution requires attention to land
inform the design of future research on the sources of urban use and agriculture policies and should be accompanied by
air pollution in cities in low- and middle-income countries local strategies for wildfire management [17].
where combustion and non-combustion sources are changing Second, while resuspended dust may seem outside the
relatively rapidly. realm of policy interventions, local and regional strategies
do exist: road and urban dust can be addressed through
5. Discussion and conclusions paving roads, traffic control, and, where affordable, regular
road cleaning. Regional dust pollution is intensified by
Our findings on the chemical composition and sources deforestation and desertification, and can be gradually re-
of air pollution provide a number of important directions duced through afforestation and grassland restoration [18–21].
6
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Figure 3. Contributions of pollution sources to particle mass during (a) non-Harmattan months, by neighborhood and (b) peak Harmattan
(25 December 2007 to 30 January 30 2008). Sea salt is characterized primarily by Na, Cl, and S (in aged sea salt, the Cl ion is replaced by
SO2−4 as a result of reaction with sulfuric acid; this chemical transformation is more pronounced in coastal areas than in inland
regions [15]). Crustal sources are characterized by Si, Al, Mg, Ti, Mn and Fe. Biomass smoke is primarily characterized by K, Cl, S, and
black carbon (BC) [12]. Road dust and traffic particles are characterized by Al, Si, Ca, Fe, and BC. Solid waste burning is characterized by
Br [14]. During Harmattan, data from the five sites were pooled to increase sample size, after confirming that source profiles (but not
contributions) were similar across sites.
Beyond ecological considerations, such strategies should be their health hazards, especially if the harms disproportionately
connected with programs related to population growth and affect poor urban communities relative to the benefits. Fourth,
mobility, and those that improve the economic conditions of there was a larger contribution from burning of solid waste
rural households who rely on land resources. The important in poor neighborhoods, where trash collection is less frequent
role of seasonal dust as a pollution source also demonstrates than in affluent neighborhoods. This demonstrates the need
the need for research on the acute and chronic health for equitable provision of urban services, now largely absent
effects of exposure to crustal particles [22, 23], and whether from poor slums.
air pollution regulations in developing countries should be Air pollution regulation and technological advances
based on total particle mass or specifically target combustion related to emissions from vehicles, power plants, and
sources. factories, have led to cleaner cities in high-income countries.
Third, while traffic sources accounted for a relatively Some cities have also benefited from reducing pollution
small share of particle pollution in Accra, their absolute from local or regional dust, for example through sweeping
contribution towards PM2.5 was over 10 µg m−3 in some and washing roads, stabilizing the road surface with dust
neighborhoods, larger than WHO guidelines for total PM2.5 suppressants, and planting trees. Lower particle pollution has
concentration. If African cities follow Asian and Middle in turn contributed to improved health [26]. The highest
Eastern megacities, where traffic management and pollution pollution levels, and about 90% of the global disease burden
is a major policy challenge, there will be even more from air pollution, now occur outside the Americas and
traffic-related pollution. Curbing and reducing traffic air Europe [1, 27]. Therefore, while efforts to further reduce air
pollution will inevitably require a sustainable pro-poor pollution in these regions continue, there is an urgent need
public transportation system, as implemented in cities like to tackle air pollution in cities in the developing world. The
Bogota [24]. African countries are a market for old cars diversity of sources demonstrates the need for integration
that do not meet emissions standards in Europe and North of policies and interventions across environment, energy,
America [25]. While the lower cost of used vehicles may help transportation, urban development and even forestry and
meet transportation needs in low-resource settings, there is agriculture sectors, while explicitly considering the benefits
a need to assess whether their economic benefits outweigh and harms of each strategy for poor communities. The
7
Environ. Res. Lett. 8 (2013) 044025 Z Zhou et al
Table 3. Average PM2.5-to-PM10 ratio of total PM mass and its References
chemical components at five monitoring sites during the
non-Harmattan season.
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Prempeh and Adam Abdul Fatah for field assistance; and the [15] McInnes L M, Covert D S, Quinn P K and Germani M S 1994
Measurements of chloride depletion and sulfur enrichment
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and Resource Development at the University of Ghana for marine boundary layer J. Geophys. Res. 99 8257–68
valuable help with logistical arrangements. Funding for field [16] Johnston F H, Henderson S B, Chen Y, Randerson J T,
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Grant 0527536, and laboratory support was provided by the Brauer M 2012 Estimated global mortality attributable tosmoke from landscape fires Environ. Health Perspect.
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