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Abstract Air pollution in Africa is a significant public health issue responsible for 1.1 million premature deaths annually. Sub-Saharan Africa has the highest rate of population growth and urbanization of any region in the world, with substantial potential for future emission growth and worsening air quality. Accurate and extensive observations of meteorology and atmospheric composition have underpinned successful air pollution mitigation strategies in the Global North, yet Africa in general and East Africa in particular remain among the most sparsely observed regions in the world. This paper is based on the discussion of these issues during two international workshops, one held virtually in the United States in July 2021 and one in Kigali, Rwanda, in January 2023. The workshops were designed to develop a measurement, capacity building, and collaboration strategy to improve air quality-relevant measurements, modeling, and data availability in East Africa. This paper frames the relevant scientific needs and describes the requirements for training and infrastructure development for an integrated observing and modeling strategy that includes partnerships between East African scientists and organizations and their counterparts in the developed world. 

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. 

Abstract India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM_2.5). Ground‐level PM_2.5concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM_2.5estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM_2.5over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM_2.5estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM_2.5as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research. 

Framework for analysis of PM_2.5estimates. 

Maternal PM_2.5exposures in informal settlements in Nairobi exceeded WHO air quality targets, with low-quality cooking fuel use identified as the most important non-ambient source.