Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: implications for environmental justice

Abstract

The temporary decrease of fine particulate matter (PM2.5) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM2.5 concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM2.5 surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM2.5 concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM2.5 were heterogeneous over space. PM2.5 concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low-cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.

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

“…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.…”

“…In recent years, there is increasing research interest in understanding air pollution trends and their associated health and environmental impacts in Sub-Saharan East Africa, typically through short-term measurement campaigns (Vliet and Kinney, 2007;Kume et al, 2010;Kinney et al, 2011;Gaita et al, 2014;Schwander et al, 2014;Ngo et al, 2015;Egondi et al, 2016;Amegah and Agyei-Mensah, 2017;deSouza et al, 2017;Pope et al, 2018;Gatari et al, 2019;Coker et al, 2021;deSouza et al, 2021;Mutahi et al, 2021). Researchers have made efforts toward filling the air quality data gap in the region through ambient air quality monitoring (specifically, particulate pollutants), however, the absence of long-term air quality data and a related monitoring network make it difficult to develop a complete assessment of the magnitude of the air pollution problem (Pope et al, 2018;Singh et al, 2020).…”

An analysis of degradation in low-cost particulate matter sensors

“…Accurate air quality data is crucial for tracking long-term trends in air quality levels, and for the development of effective pollution management plans. Levels of fine particulate matter (PM 2.5 ), a criteria pollutant that poses more danger to human health than other widespread pollutants 7 , can vary over distances as small as ~ 10's of meters in complex urban environments [8][9][10][11][12] . Therefore, dense monitoring networks are often needed to capture relevant spatial variations.…”

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area

“…Mobile monitoring has provided great insight into urban hot spots and sources of primary air pollutants, including PM 2.5 , UFPs, oxides of nitrogen (NO x = NO + NO 2 ), carbon monoxide (CO), and black carbon (BC). ,− In addition, the emergence of low-cost air pollution sensors in the past decade, particularly low-cost optical PM sensors, , has significantly boosted the amount of spatially dense air pollution monitoring around the world, especially in highly polluted areas in low-income regions. − While increased spatial coverage is a tremendous benefit of low-cost sensors, the greatest challenge with their use compared to advanced, research-grade instrumentation is that of data quality arising from issues such as signal nonlinearity, noise, poor limits of detection and quantitation, sensor bias and drift, and interference from environmental conditions . Another particular challenge involved with optical PM sensors is that the primary mode of detection is the amount of light scattered by a population of particles, which is then converted to mass concentration using empirical assumptions of properties such as particle morphology, refractive index, and density. , As a result, an optical PM sensor calibrated against a gravimetric reference instrument for one type of particle population (e.g., urban background, where the mode particle diameter is in the range of 0.5–2 μm) may not perform well when measuring a plume of smaller, freshly emitted particles.…”

“… 10 , 17 − 23 In addition, the emergence of low-cost air pollution sensors in the past decade, particularly low-cost optical PM sensors, 24 , 25 has significantly boosted the amount of spatially dense air pollution monitoring around the world, especially in highly polluted areas in low-income regions. 26 − 33 While increased spatial coverage is a tremendous benefit of low-cost sensors, the greatest challenge with their use compared to advanced, research-grade instrumentation is that of data quality arising from issues such as signal nonlinearity, noise, poor limits of detection and quantitation, sensor bias and drift, and interference from environmental conditions. 24 Another particular challenge involved with optical PM sensors is that the primary mode of detection is the amount of light scattered by a population of particles, which is then converted to mass concentration using empirical assumptions of properties such as particle morphology, refractive index, and density.…”