Source apportionment and seasonal variation of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in a Sub-Saharan African city: Nairobi, Kenya

Gaita, Samuel Mwaniki; Boman, Johan; Gatari, Michael; Pettersson, Jan B. C.; Janhäll, Sara

doi:10.5194/acp-14-9977-2014

Cited by 56 publications

(63 citation statements)

References 48 publications

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“…Note that typical PM 2.5 concentrations vary between 10-15 µg/m 3 for this site in keeping with the studies done earlier by (Gaita et al, 2014). Here, as at St Scholastica, particulate matter concentrations tend to peak in the morning and evening from Monday to Saturday, corresponding to the flow of traffic of people coming to work in the morning, and leaving in the evening, implying that emissions from vehicles is a major source of pollution at this site.…”

Section: St Scholastica Schoolsupporting

confidence: 87%

“…Scientists at the University of Nairobi, African Population and Health Research Center and their international collaborators (Gaita et al, 2014;Gatari et al, 2009;Gatari and Boman, 2003;Kinney et al, 2011;Muindi et al, 2014;Ngo et al, 2015;Vliet and Kinney, 2007) have taken a number of measurements in Nairobi. These are, however, short-term observations at limited points around the city (background, industrial, roadways, and households in informal settlements) and limited numbers of pollutants, mostly PM 2.5.…”

Section: Background To the Nairobi Case Studymentioning

confidence: 99%

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A Nairobi experiment in using low cost air quality monitors

deSouza¹,

Nthusi

Klopp

et al. 2017

Clean Air J.

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Many African cities have growing air quality problems, but few have air quality monitoring systems in place. Low cost air quality sensors have the potential to bridge this data gap. This study describes the experimental deployment of six low cost air quality monitors consisting of an optical particle counter Alphasense OPC-N2 for measuring PM 1 , PM 2.5 and PM 10 , and Alphasense A-series electrochemical (amperometric) gas sensors: NO2-A43F, SO2-A4, NO-A4 for measuring NO 2 , NO and SO 2 in four schools, the United Nations Environment Program (UNEP) headquarters and a community center in Nairobi. The monitors were deployed on May 1 2016 and are still logging data. This paper analyses the data from May 1 2016 to Jan 11 2017. By examining the data produced by these sensors, we illustrate the strengths, as well as the technical limitations of using low cost sensors for monitoring air quality. We show that despite technical limitations, sensors can provide indicative measurements of air quality that are valuable to local communities. It was also found that such a sensor network can play an important role in engaging citizens by raising awareness about the importance of addressing poor air quality. We conclude that these sensors are clearly a potentially important complement but not a substitute for high quality and reliable air quality monitoring systems as problems of calibration, certification, quality control and reporting remain to be solved

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Section: St Scholastica Schoolsupporting

confidence: 87%

Section: Background To the Nairobi Case Studymentioning

confidence: 99%

A Nairobi experiment in using low cost air quality monitors

deSouza¹,

Nthusi

Klopp

et al. 2017

Clean Air J.

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“…The concentrations of most TEs in Beijing are much higher than those measured in cities of the developed countries, such as London, United Kingdom (Visser et al, 2015), Toronto, Canada (Sofowote et al, 2014), Barcelona, Spain (Dall'Osto et al, 2013;Salameh et al, 2015), Athens, Greece (Grivas et al, 2018), Genoa and Venice, Italy (Salameh et al, 2015), Marseille, France (Salameh et al, 2015), Busan (Jeong et al, 2017), Chuncheon, Yeongwol (Han et al, 2015), and Gwangju City in South Korea (Park et al, 2014), as well as Denver and Greeley in the USA (Clements et al, 2014), and also in cities of developing countries like Tehran, Iran (Taghvaee et al, 2018), Nairobi, Kenya (Gaita et al, 2014), Sao Paulo, Brazil (de Miranda et al, 2018, and Makkah, Saudi Arabia (Shaltout et al, 2017). However, our TEs concentrations are somewhat lower than those in Algiers, Algeria (Talbi et al, 2018).…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

Characteristics and Sources of Hourly Trace Elements in Airborne Fine Particles in Urban Beijing, China

Cui

Chen

et al. 2019

JGR Atmospheres

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To better investigate the characteristics and sources of trace elements (TEs) in PM2.5 in urban Beijing, a 1‐year hourly observation was continuously made using an online multi‐element analyzer from 1 June 2016 to 31 May 2017. The average concentrations of 14 individual TEs ranged from 1.1 (V) to 900 ng/m3 (K). The occurrence levels of most TEs of interest in Beijing were lower than those in most domestic cities, but higher than those in most foreign countries. The formation of sulfate increased with the concentrations of all studied TEs during autumn and winter. Dust, industry, biomass burning and waste incineration, vehicle emissions, coal combustion, and oil combustion were identified by the positive matrix factorization (PMF) model, which accounted for 36.3%, 10.7%, 27.1%, 13.7%, 7.6%, and 4.6%, respectively, of the total elements. All factors exhibited higher concentrations on weekends than on weekdays. Local vehicular emissions and industry contributed to the loading of TEs, but dust, biomass burning and waste incineration, coal combustion, and oil combustion from neighboring areas appeared to be dominant sources of TEs. Except for dust and industry, the four other sources of TEs were mainly located in the south and southeast areas of the sampling site. The analysis by conditional probability function and potential source contribution function showed that the distribution of the PMF sources of TEs roughly agreed with the location of the main point sources. Overall, this work provides more detailed information on the characteristics of the TEs for the scientific community and modelling work.

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“…In Nairobi, there have been numerous short-term measurements of PM over the last decade (Brauer et al, 2012;Kinney et al, 2011;Ngo et al, 2015;Egondi et al, 2016;Gaita et al, 2016) with only one long-term continuous measurement (Gaita et al, 2014). To date, most measurements have used gravimetric measurement methodologies to record PM mass concentration in the PM 2.5 and PM 10 size fractions.…”

Section: Previous Pm Measurements In Nairobimentioning

confidence: 99%

Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors

et al. 2018

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Abstract. East African countries face an increasing threat from poor air quality stemming from rapid urbanization, population growth, and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low-cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an AlphaSense OPC-N2 ran with a Raspberry Pi low-cost microcomputer, packaged in a weather-proof box. Measurements were conducted over a 2-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting 2 weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8±2.0 % in the fine particle fraction (PM2.5). The low-cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi.The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1 and 8.8 µg m−3, respectively. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8 and 13.0 µg m−3, respectively. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0 and 19.5 µg m−3, respectively. The urban measurements in Nairobi showed that PM concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow-type approach we can estimate the urban and roadside increments that are applicable to Nairobi (Lenschow et al., 2001). The median urban increment is 33.1 µg m−3 and the median roadside increment is 43.3 µg m−3 for PM2.5. For PM1, the median urban increment is 4.7 µg m−3 and the median roadside increment is 12.6 µg m−3. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi.A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high roadside increment indicating that vehicular traffic is a dominant source of PM in the city, accounting for approximately 48.1 %, 47.5 % and 57.2 % of the total PM loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low-cost sensors for PM measurement without gravimetric calibration available at all sites are discussed.This study shows that calibrated low-cost sensors can be successfully used to measure air pollution in cities like Nairobi. It demonstrates that low-cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.

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Source apportionment and seasonal variation of PM<sub>2.5</sub> in a Sub-Saharan African city: Nairobi, Kenya

Cited by 56 publications

References 48 publications

A Nairobi experiment in using low cost air quality monitors

A Nairobi experiment in using low cost air quality monitors

Characteristics and Sources of Hourly Trace Elements in Airborne Fine Particles in Urban Beijing, China

Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors

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