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

Abstract

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.

Long-range and local air pollution: what can we learn from chemical speciation of particulate matter at paired sites?

“…Furthermore, the high summer insolation favours a faster oxidation of SO 2 and, accordingly, higher SO 2− 4 concentrations (i.e. Querol et al, 1999). During these summer conditions, the SIA concentrations were similar at the three Spanish sites, thus leading to high relative R+C SIA contributions in summer compared to winter in ES (see Figs.…”

“…For the Swiss and Dutch sites, the uncertainties were estimated using information about the minimum detection limit (MDL) of the techniques used for chemical analysis. In this approach, data below the MDL were replaced by half the MDL and the corresponding uncertainty was set to 5/6 times the MDL (Polissar et al, 1998;Kim et al, 2003;Kim and Hopke, 2008). For the German sites, the uncertainty matrix was constructed from three components: (i) uncertainty of the instrumental limit of detection (LOD), defined as 5/6 of the LOD; (ii) analytical uncertainty, obtained from relative standard deviations of signal intensities from repeated standard measurements; and (iii) uncertainty of the mean field blank concentration, defined as 3 times the standard deviation of the field blank.…”

“…rural and urban background) and has been widely used to discriminate the local and non-local increments (e.g. Pope et al, 2018;Petetin et al, 2014;Gianini et al, 2012, among others).…”

Effect of aerosol composition on the performance of low-cost optical particle counter correction factors

“…The measurements in Nairobi are previously reported in Pope et al (2018). These measurements are part of the 'A Systems Approach to Air Pollution' program (ASAP East Africa, www.asap-eastafrica.com).…”

“…However, there is still work needed if low-cost sensors are to be used for accurate exposure measurements, or in the future for compliance monitoring, which is of particular interest in 25 under-monitored low and middle-income countries (LMICs). LMICs typically have high urban air pollution yet the resources and infrastructure are sometimes lacking to support continuous classical reference air quality measurements (Pope et al, 2018). One of the challenges with using low-cost sensors in this setting is there is currently no agreed methodology for the evaluation of their accuracy and precision and their subsequent calibration (Lewis et al, 2018).…”