Environ. Sci. Technol. 2010, 44, 2270–2276
Air Pollution in Accra Neighborhoods: Spatial, Socioeconomic, and Temporal Patterns KATHIE L. DIONISIO,† R A P H A E L E . A R K U , §,| ALLISON F. HUGHES,⊥ JOSE VALLARINO,† HEATHER CARMICHAEL,‡ JOHN D. SPENGLER,† S A M U E L A G Y E I - M E N S A H , §,# A N D M A J I D E Z Z A T I * ,† Harvard School of Public Health, Boston, Massachusetts, Harvard College, Cambridge, Massachusetts, Department of Geography and Resource Development, University of Ghana, Legon, Ghana, Cyprus International Institute for the Environment and Public Health, Nicosia, Cyprus, Department of Physics, University of Ghana, Legon, Ghana, and Environmental Science Program, University of Ghana, Legon, Ghana
Received October 27, 2009. Revised manuscript received February 9, 2010. Accepted February 16, 2010.
This study examined the spatial, socioeconomic status (SES), and temporal patterns of ambient air pollution in Accra, Ghana. Over 22 months, integrated and continuous rooftop particulate matter (PM) monitors were placed at a total of 11 residential or roadside monitoring sites in four neighborhoods of varying SES and biomass fuel use. PM concentrations were highest in late December and January, due to dust blown from the Sahara. Excluding this period, annual PM2.5 ranged from 39 to 53 µg/m3 at roadside sites and 30 to 70 µg/m3 at residential sites; mean annual PM10 ranged from 80 to 108 µg/m3 at roadside sites and 57 to 106 µg/m3 at residential sites. The low-income and densely populated neighborhood of Jamestown/Ushertown had the single highest residential PM concentration. There was less difference across traffic sites. Daily PM increased at all sites at daybreak, followed by a mid-day peak at some sites, and a more spread-out evening peak at all sites. Average carbon monoxide concentrations at different sites and seasons ranged from 7 to 55 ppm, and were generally lower at residential sites than at traffic sites. The results show that PM in these four neighborhoods is substantially higher than the WHO Air Quality Guidelines and in some cases even higher than the WHO Interim Target 1, with the highest pollution in the poorest neighborhood.
* Corresponding author e-mail:
[email protected]; telephone: +1-617-432-5722; fax: +1-617-432-6733. † Harvard School of Public Health. ‡ Harvard College. § Department of Geography and Resource Development, University of Ghana. | Cyprus International Institute for the Environment and Public Health. ⊥ Department of Physics, University of Ghana. # Environmental Science Program, University of Ghana. 2270
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ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 7, 2010
Introduction Although more than 60% of sub-Saharan Africa’s (SSA) population is currently rural, Africa’s urban population is growing faster than that in any other world region (1). Despite this trend, there is limited data on air pollution in SSA cities, especially for particulate matter (PM) which is considered the best indicator of the health effects of pollutant mixtures. For example, a comprehensive review found that in 2000 annual PM data were available for only 3 of 212 cities with population g100,000 in SSA (2). In high-income countries, distances to major roads or large stationary sources are important predictors of PM pollution, and air pollution tends to be higher in lower socioeconomic status (SES) communities (3-9). Sources of urban air pollution in SSA and other developing regions include industrial emissions, transportation, household and commercial biomass use, and resuspended dust from unpaved roads. These cities often have large informal “slum” communities with poor environmental conditions (10). While the spatial and SES patterns of air pollution may be different from those in high-income countries, few studies have empirically examined such patterns (11-14). Even fewer or none have measured both fine (PM2.5) and coarse particles, or have considered PM pollution in relation to community SES. Our study aimed to address this important data gap through systematic collection and analysis of primary air pollution data in Accra, Ghana. The results advance our understanding of the levels and the spatial, temporal, and SES patterns of air pollution in a growing city in a low-income developing country. Study Location. Our study took place in four neighborhoods in Accra, the capital city of Ghana. Accra is located on the Gulf of Guinea and has an area of more than 250 km2. Land elevation ranges from 0 to 30 m above sea level. Accra has grown substantially in recent decades with the population of Accra Metropolitan Area (AMA) increasing from 600,000 in 1970 to 1.7 million in 2000. The four study neighborhoods lie on a line from the coast to the northern boundaries of the AMA: Jamestown/Ushertown (JT), Asylum Down (AD), Nima (NM), and East Legon (EL) (Figure S1 and Table S1). JT is one of the oldest neighborhoods in Accra and lies between the coast and Accra Business Center; AD and NM are located approximately 3 km inland, separated by Ring Road Central; EL is 10 km inland and lies north of Accra International Airport. JT and NM are poor, densely populated communities where biomass is the predominant household fuel. Biomass is also used for smallscale commercial purposes, such as cooking street food. Along the JT coast, fish are smoked over wood fires and goats are roasted over burning tires doused with kerosene (15). A large busy road with a central bus station runs through NM. Both JT and NM experience much activity throughout the day, including markets and small vendors. AD is a middle class, mostly residential neighborhood, bordered by the Ring Road Central, one of the largest and busiest roads in Accra. Fewer people use biomass fuels and street food vendors are less common in AD than in JT and NM. EL is an upper-class, sparsely populated, and residential neighborhood with most families living on large plots of land. Most homes have modern indoor kitchens and use liquefied petroleum gas. The streets are quiet during the day. The main road in EL has heavier traffic primarily during the morning and evening commute periods. 10.1021/es903276s
2010 American Chemical Society
Published on Web 03/05/2010
Study Design. We evaluated the spatiotemporal variability of ambient air pollution in these four neighborhoods using a combination of integrated and continuous rooftop monitors. The study took place between November 2006 and August 2008, following a pilot study in July 2006 (16). We designed our study to examine the seasonal and daily patterns of air pollution, as well as its variation between neighborhoods and to a lesser extent within each neighborhood; within neighborhood analyses compared measurements near major roads and those in residential parts of the neighborhood. Monitors were placed on the rooftops of a total of 11 homes and businesses in the four study neighborhoods (Figure S1). To achieve the above aims, we selected monitoring sites such that one site in each neighborhood was on a road with medium-heavy traffic for the whole day or parts of the day. One or two other sites in each neighborhood were selected in areas judged to be typical of residential parts of the neighborhood. These residential (-R1 and -R2) sites were located on secondary roads or alleys with no or significantly less traffic than the neighborhood traffic site (-T), though the residential sites still may have pollution sources nearby, especially biomass fuels in JT and NM. The other criteria for selection of sites were equipment safety, access to electricity, and uninterrupted access to our equipment for operation and maintenance. Characteristics of measurement sites are provided in Table S2. Because we had a limited number of integrated monitors and because operating and maintaining monitors in distant neighborhoods was time-intensive, we restricted simultaneous operation to five sites in two neighborhoods. To achieve the above study aims, we used the following design: Throughout the first year of the study (November 2006 to August 2007), we operated monitors at a residential and a traffic site in NM. We also operated three sites for 6-7 weeks in each of the other three neighborhoods with the detailed schedule shown in Figure S2. Therefore, in study year 1 NM was a reference neighborhood, relative to which all other neighborhoods were measured. A reference neighborhood was necessary because measurements in other neighborhoods were in different months. To examine whether the difference between other neighborhoods and NM varied by season, in the second year of the study (September 2007 to August 2008), we continued to operate NM-T and NM-R while placing the remaining three monitors at JT-R1, AD-R1, and EL-R1 for the entire study year (Figure S2). Year 1 measurements were conducted for contiguous 48-h periods, while year 2 measurements were conducted for one 48-h period every six days. From October to April, predominant winds in West Africa blow southwest from the Sahara, creating dry and dusty conditions, a phenomenon known as the Harmattan (17-19). Accra residents have observed that the winds are strongest and the dust highest during the last few days of December and through the month of January, also confirmed by our data. We operated monitors at JT-R1, JT-R2, JT-T, NM-R1, and NM-T during these 3-5 weeks of highest dust concentrations in measurement year 1, and at JT-R1, AD-R1, EL-R1, NM-R1, and NM-T in measurement year 2. In this manuscript, we use “Harmattan” to refer to this shorter, more dusty period. Specific dates that denote the Harmattan period in our measurements and analysis are provided in Figure S2. At each monitoring site, we measured integrated PM2.5 and PM10 using a gravimetric method. A total of 568 PM2.5 and 571 PM10 samples were collected during year 1 of the study and 311 PM2.5 and 314 PM10 samples during year 2 of the study (see Table S3 for details by site). Of these, 58 were PM2.5 duplicates (i.e., side-by-side measurements) and 64 were PM10 duplicates. A total of 56 PM2.5 and 56 PM10 field blanks were collected over these two years. We also measured PM2.5 and PM10 continuously at as many sites as we had
equipment available. The numbers of site-days of continuous measurements were 960 for PM2.5 and 554 for PM10 in year 1, and 656 (PM2.5) and 404 (PM10) in year 2 (see Table S3 for details by site). We measured integrated carbon monoxide (CO) using passive monitors. All pollutant measurements were colocated. We did not measure SO2 and NO2 because an earlier pilot study showed that their concentrations were relatively low and there was little spatial variation (16). Electricity outages were particularly common and lengthy in study year 1 because droughts had reduced electricity generation. We used 12-V 100-Ah batteries connected to chargers and inverters as a power backup system at each monitoring site so that the regular unannounced or planned electricity outages in Accra had limited effect on our measurements.
Materials and Methods Pollutant Measurement and Analytical Methods. Integrated PM. See Text S1 for description of measurement methods. Measured concentrations were used only if the pumps operated for g90% of the 48-h measurement period and if the average flow rate was within 10% of the intended rate. This excluded 169 PM2.5 and 123 PM10 measurements in year 1 and 33 PM2.5 and 37 PM10 measurements in year 2. The 21-30% year 1 exclusion was greater than the 12-13% exclusion in year 2 because power outages were more common and substantially longer in year 1 and on some days affected measurements despite our power backup system. Eight additional measurements in year 1 and 10 in year 2 were excluded for miscellaneous quality control reasons, including incorrect labeling, damage to the filter, and broken connections in the air flow system. All PM concentrations were blank corrected. Where duplicate measurements were taken, the two measurements were averaged to use all available data. See Text S1 for quantitative information on blanks and duplicates. Continuous PM. We measured continuous PM2.5 and PM10 using DustTrak Model 8520 and SidePak Model AM510 monitors (TSI Inc.). See Text S1 for details. PM concentrations measured using light scattering are subject to error, because factory calibrations use specific aerosols whose characteristics (e.g., shape, size, density, and refractive index) may differ from those in field studies, and because factors such as relative humidity (RH) affect measurements (20-22). For the same reason, measurement error can vary across days. We adjusted measured continuous PM in a two-step process: In the first step, we standardized the minute-by-minute records for the effects of RH, using relationships from previous studies (20). See Text S1 for RH data sources and methods. In the second step, we corrected all minute-by-minute PM records in each 48-h measurement period using a correction factor (CF) so that the average of continuous PM measurements was equal to the integrated gravimetric PM level over the same period and at the same location. In the above approach, the first step removes the effect of RH variation on measured PM within a single day and the second step ensures that the measurements are corrected against the gravimetric measurement which has substantially less error than nephelometers. We calculated unique CFs for PM2.5 and PM10, for each 48-h period, and at each site. The median (interquartile range) of CFs were 0.71 (0.56-1.13) for PM2.5 and 1.07 (0.91-1.34) for PM10. When the integrated sample was excluded for a site-period (see above), or when the duration of continuous data was
