Results from a pilot-scale air quality study in Addis

ARTICLE IN PRESS

Atmospheric Environment 39 (2005) 7849–7860 www.elsevier.com/locate/atmosenv

Results from a pilot-scale air quality study in Addis Ababa, Ethiopia V. Etyemeziana,, M. Tesfayeb, A. Yimerc, J.C. Chowd, D. Mesfinb, T. Negab, G. Nikolicha, J.G. Watsond, M. Wondmagegnb a

Division of Atmospheric Sciences, Desert Research Institute, 755 E. Flamingo Rd., Las Vegas, NV 89119, USA b Environmental Protection Authority of Ethiopia, Addis Ababa, Ethiopia c Clark County Department of Air Quality Management, Las Vegas, NV, USA d Desert Research Institute, Division of Atmospheric Sciences, Reno, NV, USA Received 21 February 2005; accepted 30 August 2005

Abstract Twenty-one samples were collected during the dry season (26 January–28 February 2004) at 12 sites in and around Addis Ababa, Ethiopia and analyzed for particulate matter with aerodynamic diameter o10 mm (PM10) mass and composition. Teflon-membrane filters were analyzed for PM10 mass and concentrations of 40 elements. Quartz-fiber filters were analyzed for chloride, sulfate, nitrate, and ammonium ions as well as elemental carbon (EC) and organic carbon (OC) content. Measured 24-h PM10 mass concentrations were o100 and 40 mg m3 at urban and suburban sites, respectively. PM10 lead concentrations were o0.1 mg m3 for all samples collected, an important finding because the government of Ethiopia had stopped the distribution of leaded gasoline a few months prior to this study. Mass concentrations reconstructed from chemical composition indicated that 34–66% of the PM10 mass was due to geologically derived material, probably owing to the widespread presence of unpaved roads and road shoulders. At urban sites, EC and OC compounds contributed between 31% and 60% of the measured PM10 while at suburban sites carbon compounds contributed between 24% and 26%. Secondary sulfate aerosols were responsible for o10% of the reconstructed mass in urban areas but as much as 15% in suburban sites, where PM10 mass concentrations were lower. Non-volatile particulate nitrate, a lower limit for atmospheric nitrate, constituted o5% and 7% of PM10 at the urban and suburban sites, respectively. At seven of the 12 sites, real-time PM10 mass, real-time carbon monoxide (CO), and instantaneous ozone (O3) concentrations were measured with portable nephelometers, electrochemical analyzers, and indicator test sticks, respectively. Both PM10 and CO concentrations exhibited daily maxima around 7:00 and secondary peaks in the late afternoon and evening, suggesting that those pollutants were emitted during periods associated with motor-vehicle traffic, food preparation, and heating of homes. The morning concentration maxima were likely accentuated by stable atmospheric conditions associated with overnight surface temperature inversions. Ozone concentrations were measured near mid-day on filter sample collection days and were in all cases o45 parts per billion. r 2005 Elsevier Ltd. All rights reserved. Keywords: PM10; Africa; Lead; Carbon monoxide; Ethiopia

Corresponding author. Tel.: +1 702 862 5569; fax: +1 702 862 5507

E-mail address: [email protected] (V. Etyemezian). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.08.033

ARTICLE IN PRESS V. Etyemezian et al. / Atmospheric Environment 39 (2005) 7849–7860

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1. Introduction Located in the Horn of Africa and bordered by Eritrea, Somalia, Sudan, Djibouti and Kenya, Ethiopia is home to some 70 million people. It covers 1.1 million km2 with 7400 km2 covered by water (NFAC, 2004). The population density is greatest in the central portion of the country, which consists of an elevated plateau divided by the Great Rift Valley (Fig. 1a). Ethiopia’s capitol city of Addis Ababa has a population between 3 and 4 million and is located on the plateau at an elevation of 2400 m. With a life expectancy of 48 years (World

(a)

350 300

Rainfall (mm)

250 200 150 100 50

(b)

Dec

Oct

Nov

Sep

Aug

Jul

Jun

Apr

May

Mar

Feb

Jan

0

Month

Fig. 1. Characteristics of Ethiopia and Addis Ababa: (a) topographic map of Ethiopia and (b) 1964–1997 monthly rainfall in Addis Ababa. Vertical lines represent 10th and 90th percentile values.

Health Organization (WHO), 2004), a high infant mortality rate (WHO, 2004), high occurrences of malaria (Bremen et al., 2004; Das, 2003), and HIV/ AIDS (Sanders et al., 2003; Abebe et al., 2003), air quality has not been a high priority for the Ethiopian government. To our knowledge, this study provides the only published data on air pollution levels in Addis Ababa. An important goal of this study was to estimate the concentration of air pollutants in Addis Ababa and compare them with Ethiopian ambient air quality guidelines (Ethiopian EPA, 2003). Although termed a plateau, the central portion of Ethiopia is mountainous, and Addis Ababa is surrounded by peaks with elevations up to 3300 m. This suggests that pollutants may become trapped in the valley under conditions of atmospheric stagnation. Ethiopia has a monsoonal climate with rainfall distributions that vary with elevation. Precipitation in Addis Ababa is greatest in August and least in November and December (see Fig. 1b). Based on this seasonality, air pollution is likely worst between October and February, when precipitation is minimal and high pressure systems can cause stagnation episodes. However, it is also possible that high particulate matter with aerodynamic diameter o10 mm (PM10) concentrations occur between March and June due to seasonal high winds which can result in the suspension of dust from semi-arid lands around Addis Ababa as well as arid areas that are hundreds of kilometers upwind. At present there are no data to estimate the potential contributions of different classes of sources to air pollution in Addis Ababa. Although not comprehensive, the list of air pollution sources includes light and heavy-duty motor-vehicles, industry, home heating and cooking, as well as fugitive sources such as biogenic emissions and dust. Cooking and heating are accomplished by the widespread use of eucalyptus (Lemenih and Bekele, 2004) which results in high levels of organic particles when combusted (Schauer et al., 2001; Oanh et al., 1999), as well as coal to a lesser extent. Light- and heavy-duty diesel vehicles contribute directly to ambient concentrations of PM (non-size specific), carbon monoxide (CO), oxides of nitrogen (NOx), sulfur dioxide (SO2), as well as organic compounds that are toxic, precursors to O3 formation, and can eventually transform from the gas phase to the particle phase. Gasoline-powered vehicles result in similar emissions to diesel vehicles, with gasoline


engines emitting more CO and diesel vehicles emitting more SO2. Ethiopia phased out the import of leaded gasoline in July 2003, and the Ethiopian government declared that leaded gasoline reserves in Ethiopia would be largely depleted by January 2004—shortly prior to the study described here. Industrial source emissions vary widely by the type of industry, fuels and solvents used, and the level of pollution controls that are in place. For example, coal combustion for power generation and metal smelting results in PM, SO2, NOx, and organic compound emissions. Depending on the nature of the coal used or the type of metal processed, toxic compounds such as lead, mercury, arsenic, and aromatics can also be emitted in significant quantities. Chemical plants may emit volatile organic compounds that are carcinogens or ozone (O3) precursors. Food and livestock-related operations result in primary and secondary PM, odor-causing agents, and O3 precursors. Coal-fired power plants provide a small fraction of the electricity used in Ethiopia due to the availability of hydroelectric power. Unlike some other African cities such as Cairo, where industrial sources are responsible for a large portion of the PM and lead pollution (Abu-Allaban et al., 2002), Addis Ababa is far less industrial and air pollution due to industrial sources is likely to be comparatively much smaller. 2. Methods The pilot study was conducted between 26 January and 28 February 2004. Due to the limited availability of air monitoring infrastructure in Ethiopia, equipment and supplies were shipped from Las Vegas, NV, USA, to Addis Ababa, Ethiopia. Differences in electric power ratings and availability at sampling sites necessitated that all equipment be operated off of batteries that required charging after each use. These and other logistical constraints imposed limits on the amount of equipment available for use and the frequency of sample procurement. Equipment available for this study included six portable filter samplers, two portable PM10 dust monitors, two portable CO monitors, one optical particle counter (OPC), and passive sampling devices for SO2, O3, and CO. 2.1. Filter samples Teflon-membrane filters (Pall Corp., 47 mm, Ann Arbor, MI) were used to collect particles for

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gravimetric and elemental analysis, and quartz-fiber filters (Pall Corp., 47 mm, Ann Arbor, MI) were used for carbon and ion analyses. Filter samples were collected using portable, battery-operated samplers (Airmetrics, MiniVol, Eugene, OR). The MiniVol is equipped with an air pump, internal flow regulator, and a timer/controller. A pneumatic attachment at the top of the unit was used to connect a filterpack equipped with a PM10 sizeselective inlet to the MiniVol. Proper functioning of the size-selective inlet requires an air flow rate of 5 (710%) liters per minute (lpm). Using an adapter that fits over the filterpack, a rotameter which was calibrated and corrected for barometric pressure and average daily temperature for Addis Ababa conditions was used to set flow rates prior to sampling and recheck them at the end of the sampling period. In all cases, the flow rate at the end of the sampling period was within 4% of the 5 lpm set value. Though the MiniVol is not a US Environmental Protection Authority (EPA) federal reference method (FRM) or federal equivalent method for PM10 compliance measurements, comparisons performed in Kansas between multiple MiniVol units and other PM10 measurement methods, including a dichotomous sampler (r2 ¼ 0:83) and a Tapered Element Oscillating Microbalance (r2 ¼ 0:90), indicated good statistical agreement (Baldauf et al., 2001). Filters were pre-weighed and post-weighed at DRI after 48-h equilibration at 2171.5 1C and 3575% relative humidity prior to use in this study. Samples were then submitted to chemical analysis at DRI. Weighing was performed on an MT5 (MettlerToledo, Columbus, OH) electromicrobalance with 71 mg sensitivity. After gravimetric analysis, samples collected on the Teflon-membrane filters were analyzed by energy dispersive X-ray fluorescence (Epsilon 5, PanAnalytical, The Netherlands) for 40 elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, Au, Hg, Tl, Pb, La, U) based on the method reported by Watson et al. (1999). Water-soluble chloride (Cl), nitrate (NO 3 ), + sulfate (SO2 4 ), and ammonium (NH4 ) were determined from the deposit on quartz-fiber filters. Each quartz-fiber filter was cut in half, and one filter half was extracted in 15 ml of distilled and de-ionized water, sonicated for 60 min, shaken for 60 min, then aged overnight to assure complete extraction of 2 the deposited material. Cl, NO 3 , and SO4 were

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measured by ion chromatography (Dionex, 500 , Sunnyvale, CA). An automated colorimetry system (Astoria–Pacific, Astoria 2, Clackamas, OR) was used to measure NH+ concentration by the 4 indolphenol method. Each sample was mixed with reagents and subjected to appropriate reaction periods before submission to the colorimeter. A portion of the remaining quartz-fiber filter was used to analyze the organic carbon (OC) and elemental carbon (EC) content of the aerosol using the IMPROVE thermal/optical reflectance (TOR) method specified by Chow et al. (1993, 2001, 2004). This method divides the OC into four fractions (OC1–OC4) based on the temperature of volatilization and the EC into three fractions (EC1–EC3). OC is separated from EC by monitoring changes in reflectance of a laser beam from the filter surface to account for OC charring. Carbon that evolves after reflectance returns to its original value is classified as EC, with the remainder classified as OC. Filter samples were stored under sporadic refrigeration during the study. Volatile inorganic aerosols, especially ammonium nitrate (NH4NO3), are known to evaporate during sampling and storage (Appel et al., 1979; Hering and Cass, 1999; Chow et al., 2005). For example, Schaap et al. (2004) report that evaporation of NH4NO3 from quartz-fiber filters during a recent study in Leipzig, Germany was negligible when storage temperatures were o20 1C, but was nearly complete for temperatures 425 1C. While these findings are dependent on the meteorological conditions during sample collection, it is worth noting that daytime high temperatures in Addis Ababa during the study period ranged from about 20 to 25 1C. Filters may have been exposed to even higher temperatures during transport between Addis Ababa and Reno, NV. NO 3 measured in this study represents the non-volatile portion, similar to that which is acquired on FRM samples used to determine PM10 compliance with standards in the USA. Non-volatile NO 3 is still of use to provide a lower limit for atmospheric NO 3 and to account for the PM10 measured on the Teflon filter.

ing to infer PM concentrations. With a set flow rate of 1.7 lpm, the instrument was equipped with a nominal PM10 inlet provided by the manufacturer. Several authors have reported that DustTrak measurements correlate reasonably well with filterbased measurements (Moosmu¨ller et al., 2001; Chung et al., 2001; Niu et al., 2002). However, the DustTrak utilizes an internal calibration coefficient based on Arizona Road Dust to relate light scattering to PM concentrations. The calibration coefficient is dependent on both particle composition and particle size distribution. Thus, when used in an urban environment where both of those properties may change substantially over the course of a day, the DustTrak might not accurately reflect PM10 concentrations. In the work described here, DustTraks were used to obtain approximate temporal information on PM10 concentrations over the diurnal cycle. The instruments were set to collect, average, and store data over 10-s sampling intervals. The GRIMM OPC (GRIMM Tech. Inc., Model 1.108, Atlanta, GA) was used to measure particle concentrations in 15 size bins that span between 0.3 and 20 mm. Because the ‘‘optical’’ diameter of a particle as measured by the GRIMM is different from the aerodynamic diameter—the quantity that determines whether or not a particle will pass through a size-selective inlet and be collected unto a PM10 filter sample—data from the OPC were used only to support temporal trends observed with the DustTrak monitors. Two portable CO monitors (Dra¨ger Safety, Pac III, Pittsburgh, PA) that utilize an electrochemical method to measure ambient CO concentration and a built-in datalogger were available for this study. These instruments have a detection limit between 2 and 4 ppm depending on the individual sensor and a resolution of 1 ppm. Collocated tests performed at CO concentrations above the detection limit indicated an average inter-instrument precision of 0.174.0% with 95% confidence limits of 8.0%. The portable CO monitors used in this study were zeroed and spanned using a 10 ppm CO standard prior to shipment from Las Vegas to Addis Ababa.

2.2. Continuous PM and CO monitors 2.3. Passive sampling devices Information on temporal changes in PM10 concentrations over the course of the 24-h filter sampling periods and particle size distribution was obtained using two types of real-time instruments. The DustTrak (TSI, Shoreview, MN) is a rugged portable instrument that uses particle light scatter-

Diffusion tubes for passive sampling of SO2 and CO were exposed during filter sampling intervals. The SO2 diffusion tubes (SKC West Inc., Fullerton, CA, 810-5DH) were rated for exposures ranging from 50 to 4800 ppm h. The CO diffusion tubes


(SKC West Inc., 810-1DL) were rated for exposures ranging from 10 to 200 ppm h. In both cases, the manufacturer’s suggested exposure period was 10 h or less. This nominal limit was frequently exceeded during the Addis Ababa study with exposures generally approaching 24 h. O3 test sticks (SKC West Inc., 526-300) were used to colorimetrically estimate ambient O3 concentrations. With a specified exposure time of 10 min and a detection limit of 45 ppb, the test sticks provided a rapid method to estimate O3 concentrations at the beginning and end of filter sampling periods. 2.4. Monitoring sites Sites used during the field study are shown in Fig. 2. Twelve distinct sites (see Table 1) were used for sampling of ambient air. The sites Ethiopian Seed Enterprise (ESE), Kera (KER/Yohannes Church), Ministry of Revenue (MOR), Science and Technology Faculty Building (SNT), and Evangelical Theological College (THE) were chosen to represent the urban core and were generally located between 50 and 100 m from arterial roads with heavy motor-vehicle and pedestrian traffic. These sites were intended to represent ambient

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conditions at street level. During each 24-h sample period, one set of measurements was obtained at SNT, while another set was obtained at one or more other locations. Thus, the SNT site provided information on day-to-day variations in pollutant concentrations while the other sites were used to assess variations in space. Residential Address 1 (RA1) was located in a residential neighborhood surrounded by urban activity while Residential Address 2 (RA2) and Residential Address 3 (RA3) were in neighborhoods in a comparatively less congested area in the northwestern part of the city, though in close proximity to the teeming market (Merkato). RA2 was also near small-scale metal workers who use rubber for fuel. Bus Depot (BUS) is the bus station located within Merkato where idling diesel buses spew visible noxious plumes into the air. The National Meteorological Service Agency (MET) site was in close proximity to Bole International Airport along Ring Road. Saris–Orimaya Disaster Preparedness (SAR) was also along Ring Road, across the street from a number of industrial sources including tire and paint-producing plants. The MET and SAR sites and the EPA of Ethiopia (EPA) site, located along the eastern periphery of Addis Ababa, were chosen to represent air quality levels outside the immediate urban area. Table 1 shows sample dates and locations. 3. Results

Fig. 2. Monitoring sites during field study. The three-letter mnemonics are shown atop a 2002 map produced by the Ethiopian Mapping Authority. The thick black line corresponds to Ring Road, an arterial that encircles the city.

Hourly averaged PM10 and CO data are shown in Fig. 3. As stated previously, PM10 data from the DustTrak may not be quantitative, because the DustTrak uses particle light scattering to infer mass concentrations and the optical properties of an aerosol depend on the size distribution as well as the composition. However, data from the DustTrak do provide relative values for comparison of concentration levels over the diurnal cycle and among sampling sites. Minimum, maximum, and average values are shown for multiple days of measurements at the SNT site of PM10 and CO in Fig. 3a and c, respectively. Hourly PM10 concentrations at SNT are highest from 5:00 to 8:00 local standard time. This peak is a result of vehicle emissions during morning commute times combined with stable atmospheric conditions (sunrise was around 7:00). Smaller peaks in PM10 concentrations can also be seen in the late afternoon around 18:00 and also late at night usually beginning at mid-night and tapering off at 3:00 but occasionally starting as early as



Table 1 Sample dates and times Site

Sample ID

Start time

End time

Duration (h)

DustTrak

Ethiopian EPA Office

EPA1 EPA2 EPA3

26/1/2004 15:00 27/1/2004 15:15 30/1/2004 12:00

27/1/2004 12:00 28/1/2004 12:00 31/1/2004 12:00

21.0 20.8 24.0

X

SNT1 SNT2 SNT3 SNT4 SNT5 SNT6 SNT7

26/1/2004 15:30 27/1/2004 13:00 30/1/2004 12:00 4/2/2004 0:00 10/2/2004 0:00 16/2/2004 13:30 26/2/2004 12:00

27/1/2004 12:00 28/1/2004 12:00 31/1/2004 12:00 5/2/2004 0:00 11/2/2004 0:00 17/2/2004 13:00 27/2/2004 12:00

20.5 23.0 24.0 24.0 24.0 23.5 24.0

X X X

Kera Theological College Oromiya Disaster Preparedness (DPPC) National Meteorological Service Agency Ethiopian Seed Enterprise Ministry of Revenue

KER1 THE1 SAR1 MET1 ESE1 MOR1

27/1/2004 12:30 30/1/2004 12:15 4/2/2004 0:00 4/2/2004 0:00 10/2/2004 0:00 10/2/2004 0:00

28/1/2004 12:00 31/1/2004 12:00 5/2/2004 0:00 5/2/2004 0:00 11/2/2004 0:00 11/2/2004 0:00

23.5 23.8 24.0 24.0 24.0 24.0

X X X X

Residential Address 1 Residential Address 2 Residential Address 3

RA11 RA21 RA22 RA31

16/2/2004 16/2/2004 21/2/2004 26/2/2004

17/2/2004 17/2/2004 22/2/2004 27/2/2004

12:00 12:00 0:00 12:00

24.0 23.5 24.0 24.0

Merkato Bus Station

BUS1

26/2/2004 12:00

27/2/2004 12:00

24.0

Field Blank

FB1 FB2 FB3

28/1/2004 13:45 28/1/2004 14:15 27/2/2004 15:00

29/1/2004 13:50 29/1/2004 14:20 28/2/2004 15:05

0.1 0.1 0.1

Science and Technology Building

12:00 12:30 0:00 12:00

OPC

CO

X X

X X X X X X

X X X X

X

X X X X

X X X X X X

An ‘‘X’’ in one of the three rightmost columns indicates that time resolved data were collected.

22:00. The SNT site is located at the bottom of a hill and it is unknown if this nighttime peak is due to local emissions or due to nighttime drainage flow from an upwind source area. Two possible local sources were noted near the SNT site: motor-vehicle traffic and a public garden where homeless persons appeared to spend the night on occasion and perhaps burn biomass to stay warm. The public garden is only 5 m from the SNT sampling site and burning of biomass there would have likely caused very large sporadic spikes in PM10 concentrations. Inspection of the 10-s DustTrak data during these nighttime events revealed smooth peaks with concentrations elevated for several hours at a time, suggesting that biomass burning in the public garden was not a factor. In Fig. 3b and d, hourly averages are shown for multiple sites for PM10 and CO, respectively. For clarity in the figure, data from sites exhibiting similar magnitudes in PM10 and CO concentrations have been averaged. Only 1 or 2 days of data are available for most sites other than SNT; and

although the dates for the PM10 data from the different sites do not overlap, some results regarding the spatial distribution of PM10 over the Addis Ababa area are clearly supported by the data in Fig. 3b. The site with the lowest peak PM10 concentration (EPA) is on the periphery of the city where urban sources are less likely to impact air quality. Like the SNT site, the 10-s DustTrak data for the KER and EPA sites exhibit peaks in concentration that are smooth and have durations of several hours, indicating relatively constant influence from a source or source area. In contrast, much of the data from THE, MET, SAR, and RA2 at 10-s resolution is characterized by a noisy time series with sharp and frequent high concentration peaks. This indicates the proximity of a local and sporadic or periodic source, such as a point source with a meandering plume or emissions due to infrequent motor-vehicle passage. At the predominantly residential RA2 site, such sources can include cars passing on local, unpaved roads or meandering plumes from adjacent, small-scale metal working

ARTICLE IN PRESS 1000 900

Minimum Maximum Average

800 700 600 500 400

DustTrak PM 10 (µg/m3)

DustTrak PM 10 (µg/m3)


300 200 100 0 0

4

8

(a)

12 16 Hour of Day

20

1600 1400 1200 1000 800 600 400 200 0 0

4

8

12 16 Hour of Day

20

24

8 EPA RA2-BUS-ESE RA3 MOR-SNT

7 6 CO (ppm)*

CO (ppm)*

10

RA2 SNT-KER-THE SAR-MET EPA

(b)

Minimum Maximum Average

12

2000 1800

24

14

7855

8 6 4

5 4 3 2

2

1 0 0 (c)

4

8

12 Hour of Day

16

20

0

24

0 (d)

4

8

12 Hour of Day

16

20

24

Fig. 3. Hourly averaged PM10 and carbon monoxide concentrations: (a) PM10 as measured by DustTrak for multiple sample periods at SNT; (b) hourly average PM10 as measured by DustTrak; (c) carbon monoxide for multiple sample periods at SNT; and (d) hourly average carbon monoxide.

plants, home heating fuel combustion, and cooking. Similarly, the THE site is located on a hillside near the bottom of a valley and adjacent to a low-income neighborhood where biomass burning for heat and food preparation is commonplace. The SAR and MET sites are on the periphery Ring Road where traffic is comparatively low for most of the day and the passage of individual vehicles may result in a corresponding momentary PM10 spike. At the SNT site (Fig. 3c), hourly CO concentrations basically follow the same trend as the PM10 concentrations with daily maxima occurring near 7:00 with additional, smaller peaks in the late afternoon (around 18:00) and also during the late night and early morning hours. The highest 1-h CO concentration at all sites during the field study was 12.3 ppm. At most sites peak hourly values occurred in the morning and were generally o7 ppm (Fig. 3d). These values compare quite favorably with the Ethiopian EPA’s 1-h (35 ppm) and 8-h (9 ppm) ambient air quality guidelines for CO,

which are similar to US EPA’s National Ambient Air Quality Standards. Due to the comparatively low concentrations, the CO passive sampling tubes, which were exposed simultaneously with the filter samples, always indicated values below the detection limits of the device. Likewise, the diffusion tubes used for SO2 sampling yielded non-detect results at all sites where they were used. The manufacturer of the SO2 diffusion tubes states a nominal lower detection limit of 50 ppm h. Therefore, non-detect values obtained after a 24-h exposure suggest average SO2 concentrations that are less than 2 ppm. This value is far greater than the annual Ethiopian EPA standard of 0.03 ppm and the 24-h standard of 0.14 ppm. Therefore, it is not possible to use these SO2 data to determine if Addis Ababa air quality is more or less in compliance with the Ethiopian guidelines for SO2. O3 test sticks with a nominal detection limit of 45 ppb were also exposed at each sample location at the time of filter loading and


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100 mg m3 at several urban sites during the short study period suggests that violation of the 24-h standard is possible. Furthermore, if these data are representative of other times of the year, it is likely that the annual average PM10 guideline of 50 mg m3 would be violated at several of the urban sites. We note, however, that during the long rainy season (March–September), geological material contribution to PM10 would be significantly lower than during the January/February study period due to prolonged wetting of the ground. This may help keep annual average PM10 o50 mg m3, though the absence of long-term data makes this difficult to ascertain. Results from chemical analysis of filter samples were used to estimate the fractions of PM10 that were associated with SO2 4 , geological material, EC,

unloading. This time varied from one sample to the next, but was usually between 11:00 and 16:00. As with the CO and SO2, all O3 test sticks gave nondetect values suggesting that ambient O3 concentrations in the Addis Ababa metropolitan area during peak sunlight hours were o45 ppb. This is perhaps due to the abundance of NOx, emitted from vehicles operating without catalytic converters, that is available to quench any ozone that is formed. NOx concentrations were not measured during this study to ascertain the validity of this hypothesis. Fig. 4a shows filter-based measured PM10 concentrations for individual sample days, as well as for site averages. For all samples obtained during the study period, the PM10 measured is below the 24-h Ethiopian EPA guideline of 150 mg m3. However, the fact that PM10 concentrations approach

PM10 mass( µg/m3)

120 100 80 60 40 20 RA3-Average

BUS-Average

RA2-Average

RA1-Average

ESE-Average

MOR-Average

MET-Average

THE-Average

SAR-Average

SNT-Average

KER-Average

EPA-Average

RA31-02/26/04

BUS1-02/26/04

RA22-02/21/04

RA21-02/16/04

RA11-02/16/04

ESE1-02/10/04

MOR1-02/10/04

MET1-02/04/04

THE1-01/30/04

SAR1-02/04/04

SNT7-02/26/04

KER1-01/27/04

SNT6-02/16/04

SNT5-02/10/04

SNT4-02/04/04

SNT3-01/30/04

SNT2-01/27/04

SNT1-01/26/04

EPA3-01/30/04

EPA2-01/27/04

EPA1-01/26/04

0

(a)

Percent of PM10 mass.

120% Unknown/Over Predicted

100%

OC(3+4+P)

80%

OC(1+2) 60% Geologic 40%

Elemental Carbon

20%

Nitrate (non-volatile)

0% BUS

RA3

RA2

RA1

ESE

MET

SAR

THE

KER

SNT

MOR

(b)

EPA

-20%

Sulfate

Fig. 4. MiniVol PM10 filter sample chemical analysis results. (a) PM10 filter mass for individual samples and site averages. For individual samples, vertical bars represent analytical uncertainty, whereas for multi-sample site averages, vertical bars represent standard deviations among samples. (b) PM10 reconstructed mass as percent of measured PM10 mass. Values shown are averages for each site during study the period. The white bar shows the difference between the reconstructed PM10 mass and the measured mass. Values exceeding 100% indicate that the sum of the reconstructed components exceeds the measured PM10 concentration.


and OC. Using linear regression, a statistically significant correlation was found between SO2 4 and 1 2 Ca (slope ¼ 0.1770.06 g SO2 g Ca, R ¼ 0:32, 4 n ¼ 22, P ¼ 0:007). This correlation with calcium, which is predominantly found in soil-derived aerosols, suggests that a portion of the SO2 4 (ranging between 4% and 35% of the total) was associated with geological material, probably in the form of CaSO4 (gypsum). The remainder of the SO2 was 4 assumed to be associated with ammonium (NH+ 4 ) as NH4(HSO4) or as (NH4)2SO4, depending on the stoichiometric availability of NH+ 4 . In all cases, NH+ was available in sufficient amounts to 4 completely account for the non-soil SO2 using a 4 combination of these two salts, with the assumption that NH4(HSO4) formed preferentially and (NH4)2SO4 formed only when additional NH+ 4 was available. For seven out of the 21 filter samples, NH+ 4 was present in quantities greater than needed to completely neutralize SO2 as (NH4)2SO4. We 4 note here that all samples also contained the NO 3 ion in quantities above detection limits and that perhaps a portion of the NO 3 may have combined with NH+ 4 to form NH4NO3, especially on the 7 days when NH+ concentrations exceeded SO2 4 4 concentration by more than a factor of two on a molar basis. However, owing to variable temperature and relative humidity conditions experienced by filters after exposure, during storage and transport, NH4NO3 is likely to have dissociated into ammonia (NH3) and nitric acid (HNO3), and evaporated from quartz filters before analysis at least partially and perhaps to a great extent (e.g. Schaap et al., 2004). This, combined with the potential for NO 3 to be derived from soil or to appear as a positive artifact of HNO3 (g) reacting with alkaline soil particles on filter samples (Milford and Davidson, 1987), precludes the ability to report secondary nitrate aerosol concentrations with confidence based on NO 3 measured during this study. Thus, NO 3 reported here refers to primarily nonvolatile NO 3 and represents a lower limit for total + NO 3 aerosol. NH4 concentrations are not reported separately here, because most of the NH+ 4 has already been accounted for in the NH4(HSO4) and (NH4)2SO4 and any remaining NH+ 4 constituted o1% of the PM10 mass. EC compounds were assumed to consist of the sum of the three EC fractions obtained by TOR minus the pyrolysis correction (OP) so that [EC] ¼ [EC1]+[EC2]+[EC3][OP]. OC compounds were assumed to consist of the four OC fractions plus the

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pyrolysis correction obtained by TOR modified with a multiplier to account for oxygen and hydrogen atoms associated with relatively fresh urban emissions (Watson et al., 1988; Turpin and Lim, 2001), so that [organic material, OM] ¼ 1.4 ([OC1]+[OC2]+ [OC3]+[OC4]+[OP]). The airborne concentration of geological material was estimated by assuming that Al, Si, Ca, Fe, and Ti existed only as Al2O3, SiO2, CaO, Fe2O3, and TiO, and were entirely associated with soil matter so that [geological material] was set equal to 1.89 [Al]+2.14 [Si]+1.4 [Ca]+1.43 [Fe]+ 1.33 [Ti]. It would have been preferable to use metal oxide compositions that were specific to the soils in the vicinity of Addis Ababa, as was done for the western US by Sisler et al. (1996), Cahill et al. (1981), and Pitchford et al. (1981). However, in the absence of information on local mineralogy and relative abundances of metal oxides in soils, this equation provided a rough estimate of the portion of PM10 that could be attributed to geologic material. Site averages of reconstructed component mass as a fraction of the measured PM10 appear in Fig. 4b. An ‘‘unknown’’ component has been added to cases when the reconstructed mass falls short of the measured PM10. For a number of samples— indicated by a negative correction in Fig. 4b—the reconstructed mass slightly exceeds the measured PM10 (4100% of the measured mass is explained), indicating that one or more of the reconstructed components is overestimated in those cases. One possible reason for this is that organic gases adsorbing onto quartz-fiber filters can result in positive artifacts in the OC1 and OC2 carbon fractions (Watson, 2002). The OM associated with OC1 and OC2 is delineated from the OM associated with OC3, OC4, and OCP fractions in Fig. 4b. Secondary SO2 4 mass generally constituted o10% of the measured PM10 except at two of the periphery sites, EPA (15%) and MET (11%), where PM10 concentrations were the lowest in the study area. Nonvolatile NO 3 was responsible for o7% of the PM10 at all sites and o4% at the urban sites. The sum of EC and OC, on average, contributed from as little as 24–26% of the measured PM10 at the three periphery sites to as much as 60% at the RA2 site. The carbon compound contribution at the non-residential, urban sites ranged between 31% at the ESE site and 57% at the KER site. EC and OM are caused by sources within the boundaries of the city, including diesel and gasoline vehicles and biomass burning. Particles originating from combustion sources are generally in the PM2.5 size fraction. If all of the carbonaceous



material is in the PM2.5 size fraction, then the Ethiopian EPA’s guideline for annual average PM2.5 (15 mg m3) would probably be violated at a number of sites. Geological material content was high at all sites with contributions to PM10 ranging between 34% at KER and 66% at RA1. Although empty unpaved lots and construction are common in Addis Ababa, the absence of high winds during the study period combined with the prevalence of unpaved roads, as well as paved roads with unpaved shoulders, suggests that most of the geological material found in PM10 is due to emission of road dust. Concentrations of airborne PM10 lead never exceeded 0.1 mg m3 for any of the samples collected, which is well below the EPA guideline of 0.5 mg m3, averaged on an annual basis. This finding is especially timely since Ethiopia stopped the distribution of leaded gasoline 6 months prior to the date of this study.

4. Conclusions A pilot-scale air quality study was conducted in Addis Ababa, Ethiopia between 26 January and 28 February 2004. The intent of the study was to obtain a snapshot of the air quality conditions in Addis Ababa, especially with respect to PM10, airborne lead, and carbon monoxide concentrations. The findings of the short study can be summarized as follows:

PM10 concentrations at urban sites ranged between 35 and 97 mg m3. This suggested that if PM10 concentrations measured over the sample period are representative for the year, the Ethiopian EPA annual standard of 50 mg m3 would probably be violated at a number of sites. If most of the carbonaceous material found on PM10 filters is associated with particles from combustion sources, then those particles would be in the fine size range and their relative abundance suggests that the PM2.5 annual standard (15 mg m3) would also be violated at the same sites. Based on analysis of the aerosol components, 35–65% of the PM10 was of geologic origin and probably due to paved and unpaved road dust, and 35–60% was due to OM and EC. Because Addis Ababa is not highly industrialized, the sources of carbon that are important on the urban scale are limited to gasoline and diesel vehicles, as well as biomass burning for home

heating and cooking. Factoring out soil-related + SO2 4 , estimates based on NH4 concentration 2 indicate that secondary SO4 species generally contributed o10% of the PM10, except at the suburban sites. For 14 of the 21 samples collected, the stoichiometric relationship between + SO2 4 and NH4 indicates that it is unlikely that significant amounts of secondary nitrate aerosol were present. For the remaining seven samples, some secondary NO 3 aerosol was likely to have been present, but sampling procedures used in this study precluded the determination of the concentration of those volatile species. Nonvolatile NO 3 represented o4% of the PM10 mass at most of the sites and o7% at all of the sites. Airborne lead concentrations were lower than the Ethiopian EPA (2003) standard of 0.5 mg m3 calculated on an annual average basis. This is an especially important finding because Ethiopia had recently discontinued the use of leaded gasoline. Test strips used during the late morning and early afternoon indicated that ozone concentrations at the sampling sites were generally o45 ppb. CO concentrations measured with portable instruments were comfortably within both the 1-h standard (35 ppm) and the 24-h standard (9 ppm). Using time-resolved instruments for PM10 and CO, the diurnal patterns for those two pollutants were found to be similar. Peak concentrations occurred between 5:00 and 8:00 with secondary peaks between 16:00 and 20:00. In some cases, sporadic spikes in concentration were also observed during the late night and early morning hours.

In summary, with respect to PM10, PM2.5, and lead, ambient air quality conditions in Addis Ababa are far better than in Cairo, Egypt (Abu-Allaban et al., 2002). Nevertheless, this pilot study suggests that concentrations, especially in urban and residential areas within the city, are close to, if not higher, than the EPA’s ambient standards for PM10 and PM2.5. This may be especially true for the annual standards. While providing insight into air quality in Addis Ababa—including spatial and temporal distributions—when no information existed before, this study had several major shortcomings that ought to be addressed in future work. First, this study


provided a snapshot of the air quality in January/ February of 2004. This time period was chosen because it corresponded to comparatively dry months when pollution from combustion sources is likely to be exacerbated by the presence of dust. A multi-year study, or permanent monitoring station, would provide better assessment of the long-term temporal trends. Second, in developing countries, indoor air quality can be far worse and lead to a greater number of respiratory illnesses than ambient air quality (e.g. WHO, 2002; Dagoye et al., 2004; Tnorboo et al., 1991; Sanyal and Maduna, 2000; Azizi et al., 1995; Mishra, 2003), partly because of the use of biomass in poorly ventilated areas for home heating and cooking. From the standpoint of health, therefore, future efforts should aim to quantify the relative importance of indoor air pollution in Addis Ababa. Third, the relatively small amount of data collected for this study precluded source attribution analysis. Such analysis could be useful to the Ethiopian government for economical placement of controls on air pollution sources in the future. Acknowledgements This study would not have been possible without the logistical and technical assistance provided by Dr. Tewoldebrhan Egziabher (Environmental Protection Authority of Ethiopia); Dr. Desta Mebratu and Dr. Strike Mekandla (United Nations Environment Programme); Mr. Demelew Awoeke (National Meteorological Service Agency of Ethiopia); Ms. Cristina Mercurio, Ms. Marianne Bailey, Mr. Scott Hedges, Ms. Jane Metcalf, and Ms. Sara Terry (US Environmental Protection Agency); Dr. Hampden Kuhns, Dr. John Gillies, Dr. Oliver Chang, Dr. Eric Fujita, Dr. Alan Gertler, Dr. Barbara Zielinska, and Mr. Steven Kohl (Desert Research Institute); Mr. Solomon Teffera (South Coast Air Quality Management District); and Mr. Daniel Balzer (US Department of State). This work was funded by the Desert Research Institute, Las Vegas, NV, USA.

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