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Abstract The paucity of fine particulate matter (PM_2.5) measurements limits estimates of air pollution mortality in Sub‐Saharan Africa. 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 7.03 μg m⁻³. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 μg m⁻³respectively. TheR²value 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 five‐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 PM_2.5within the city ranged from 12 to 18 μg m⁻³. The concentrations exceeded the WHO daily PM_2.5limits 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. 

Abstract Smoke particulate matter emitted by fires in the Amazon Basin poses a threat to human health. Past research on this threat has mainly focused on the health impacts on countries as a whole or has relied on hospital admission data to quantify the health response. Such analyses do not capture the impact on people living in Indigenous territories close to the fires and who often lack access to medical care and may not show up at hospitals. Here we quantify the premature mortality due to smoke exposure of people living in Indigenous territories across the Amazon Basin. We use the atmospheric chemistry transport model GEOS-Chem to simulate PM_2.5from fires and other sources, and we apply a recently updated concentration dose-response function. We estimate that smoke from fires in South America accounted for ∼12 000 premature deaths each year from 2014–2019 across the continent, with about ∼230 of these deaths occurring in Indigenous lands. Put another way, smoke exposure accounts for 2 premature deaths per 100 000 people per year across South America, but 4 premature deaths per 100 000 people in the Indigenous territories. Bolivia and Brazil represent hotspots of smoke exposure and deaths in Indigenous territories in these countries are 9 and 12 per 100 000 people, respectively. Our analysis shows that smoke PM_2.5from fires has a detrimental effect on human health across South America, with a disproportionate impact on people living in Indigenous territories.