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Background: Relatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM 2·5 ) and carbon monoxide (CO) than polluting fuels (eg, wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa., Methods: In this measurement study, we recruited households cooking primarily with LPG or exclusively with wood or charcoal in peri-urban Cameroon, Ghana, and Kenya from previously surveyed households. In 2019-20, we conducted monitoring of 24 h PM 2·5 and CO kitchen concentrations (n=256) and female cook (n=248) and child (n=124) exposures. PM 2·5 measurements used gravimetric and light scattering methods. Stove use monitoring and surveys on cooking characteristics and ambient air pollution exposure (eg, walking time to main road) were also administered., Findings: The mean PM 2·5 kitchen concentration was five times higher among households cooking with charcoal than those using LPG in the Kenyan community (297 μg/m 3 , 95% CI 216-406, vs 61 μg/m 3 , 49-76), but only 4 μg/m 3 higher in the Ghanaian community (56 μg/m 3 , 45-70, vs 52 μg/m 3 , 40-68). The mean CO kitchen concentration in charcoal-using households was double the WHO guideline (6·11 parts per million [ppm]) in the Kenyan community (15·81 ppm, 95% CI 8·71-28·72), but below the guideline in the Ghanaian setting (1·77 ppm, 1·04-2·99). In all communities, mean PM 2·5 cook exposures only met the WHO interim-1 target (35 μg/m 3 ) among LPG users staying indoors and living more than 10 min walk from a road., Interpretation: Community-level variation in the relative difference in HAP exposures between LPG and polluting cooking fuel users in peri-urban sub-Saharan Africa might be attributed to differences in ambient air pollution levels. Thus, mitigation of indoor and outdoor PM 2·5 sources will probably be critical for obtaining significant exposure reductions in rapidly urbanising settings of sub-Saharan Africa., Funding: UK National Institute for Health and Care Research., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM 2.5 , black carbon, CO); household air pollution (PM 2.5 , CO); personal exposure (PM 2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM 2.5 (R 2  = 0.45) and CO (R 2  = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R 2  = 0.76) and a root mean-squared error of 85 µg/m 3 , and the survey-only-based regression model was able to predict PM2.5 exposures with an R 2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R 2 of 0.57 and RMSE of 98 µg/m 3 ., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)