The paucity of fine particulate matter (PM2.5) measurements limits
estimates of air pollution mortality in Sub-Saharan Africa. If well
calibrated, low-cost sensors can provide reliable data especially where
reference monitors are unavailable. We evaluate the performance of
Clarity Node-S PM monitors against a Tapered element oscillating
microbalance (TEOM) 1400a and develop a calibration model in Mombasa,
Kenya’s second largest city. As-reported Clarity Node-S data from
January 2023 through April 2023 was moderately correlated with the
TEOM-1400a measurements (R2 = 0.61) and exhibited a mean absolute error
(MAE) of approximately 7.03 µg m–3. Employing three calibration models,
namely, multiple linear regression (MLR), gaussian mixture regression
(GMR) and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40
µg m–3 respectively. The R2 value improved to 0.63 for the MLR model
but all other models registered a decrease (R2 = 0.44 and 0.60
respectively). Applying the correction factor to a 5-sensor network in
Mombasa that was operated between July 2021 and July 2022 gave insights
to the air quality in the city. The average daily concentrations of
PM2.5 within the city ranged from 12 to 18 µg m–3. The concentrations
exceeded the WHO daily PM2.5 limits more than 50% of the time, in
particular at the sites nearby frequent industrial activity. Higher
averages were observed during the dry and cold seasons and during early
morning and evening periods of high activity. These results represent
some of the first air quality monitoring measurements in Mombasa and
highlight the need for more study.