Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots

Abstract

Poor air quality is a development challenge. Urbanization and industrial development along with increased populations have brought clear socio-economic benefits to Low-and Middle-Income Countries (LMICs) but can also bring disadvantages such as decreasing air quality. A lack of reliable air quality data in East African cities makes it difficult to understand air pollution exposure and to predict future air quality trends. This work documents urban air quality and air pollution exposure in the capital cities of Kampala (Uganda), Addis Ababa (Ethiopia) and Nairobi (Kenya). We build a situational awareness of air pollution through repeated static and dynamic mobile monitoring in a range of urban locations, including urban background, roadside (pavement and building), rural background, and bus station sites, alongside vehicle-based measurements including buses and motorcycle taxis. Data suggest that the measured particulate matter mass concentrations (PM2.5, PM10) in all studied cities was at high concentrations, and often hazardous to human health, as defined by WHO air quality guidelines. Overall, the poorest air quality was observed in Kampala, where mean daily PM2.5 and PM10 concentrations were significantly above the WHO limits at urban background locations by 122% and 69% and at roadside locations by 193% and 215%, respectively. Traffic is clearly a major contributor to East African urban air pollution; monitoring in Kampala and Addis Ababa, on motorcycle taxis, in buses and at bus stations indicated that drivers and commuters were exposed to poor air quality throughout their commute. Road-related air pollution can also impact indoor locations near roads. Using one exemplar building located within Nairobi’s Central Business District, it is shown that measured outdoor PM concentrations significantly correlate with the indoor air quality (r = 0.84). This link between roadside emissions and indoor air pollution within buildings located close to the road should be explored more fully. This study, through a series of case studies, provides clear evidence that roads and traffic need to be a focus for mitigation strategies to reduce air pollution exposure in East African cities.

Use of biomass fuels predicts indoor particulate matter and carbon monoxide concentrations; evidence from an informal urban settlement in Fort Portal city, Uganda

“…Additionally, the Ministry of Lands, Housing and Urban Development oversee the physical planning of areas in Uganda and regulate and approve building plans for each district to reduce informal settlements and unregulated structures. Despite these measures, IAP is still prevalent [26]. Although there is evidence of IAQ and associated health effects and environmental risks such as exposure levels in rural and urban formal settings [8,[27][28][29], a few studies have been conducted in informal urban settlements, where a vast majority of the urban population in low-income countries reside [30].…”

A street-level assessment of greenhouse gas emissions associated with traffic congestion in the city of Nairobi, Kenya

“…These results build on existing evidence that traffic congestion is a significant contributor to CO 2 emissions and air pollution. Previous research has focused on the contribution of traffic congestion to air pollution (Kinney et al, 2011;Gaita et al, 2014;Rajé et al, 2018;Singh et al, 2021). These results demonstrate that congestion is also a contributor to CO 2 emissions.…”

Urban diagnostics and a systems approach to air quality management: Pathways towards sustainable economic development and a healthy nairobi, Kenya

“…In the above context, the focus is on the analysis of primary PM 2.5 emissions available from global emission inventories. The choice of this pollutant was firstly motivated by the key role that PM 2.5 play in the deterioration of urban air quality in East Africa (Singh et al, 2021). Secondly, there is evidence to suggest the efficacy of mitigation policies in reducing PM 2.5 emission in urban areas in other parts of the world (Sofia et al, 2020).…”

“…Over the last 10 years a number of short-term and geographically restricted air pollution studies have been undertaken in Nairobi to understand temporal trends in PM pollution (Kinney et al, 2011;Ngo et al, 2015;Egondi et al, 2016;Gaita et al, 2016;deSouza et al, 2017;deSouza et al, 2021;Pope et al, 2018;Gatari et al, 2019;Singh et al, 2021). Most of these studies determined PM pollution within the city, by measuring PM 2.5 and PM 10 mass concentrations using gravimetric methods and low-cost sensor technologies.…”

“…Kinney et al (2011) undertook a height dispersion experiment which showed that at about 1.5 m agl on the curbside the mean concentration of PM 2.5 was 110 μg m −3 ± 19 μg m −3 and at 17 m agl it was 43 μg m −3 ± 8 μg m −3 at the same site while at the urban background measurement site (Gaita et al, 2014) it was 35 μg m −3 ± 12 μg m −3 . Findings from recent monitoring studies (Pope et al, 2018;Singh et al, 2021) clearly indicate that current air quality in Nairobi, especially at roadside locations is at an unhealthy level as defined by the EPA daily US Environmental Protection Agency (EPA) air quality index (AQI) (AirNow, 2022). Pope et al (2018) measured PM mass concentrations using low-cost sensors at three locations (roadside and urban background in Nairobi and in rural Kenya) during February-March 2017.…”