Spatial variation of fine particulate matter levels in Nairobi before and...

deSouza, P., P. A. Oriama, J. Klopp, R. Kahn, S. Bin, W. Mbula, S. Wairimu, S. O. Ogolla, C. Otieno, I. F. Mwangi, P. Pederson, S. Horstmann, L. Gordillo-Dagallier, C. N. Christensen, C. Franck, R. Ayman, S. Mora, K. Messier, and P. Kinney (2021), Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: Implications for environmental justice, Environ. Res. Comm., 3, 071003, doi:10.1088/2515-7620/ac1214.

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 inPM2.5

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.Wedeveloped 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 inPM2.5 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 lowcost

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.

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Research Program: 
Applied Sciences Program (ASP)
Atmospheric Composition Modeling and Analysis Program (ACMAP)