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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.)

Interventions implementing clean fuels to mitigate household air pollution in low- and middle-income countries have focused on environmental and health outcomes, but few have evaluated time savings. We performed a systematic review, searching for studies of clean fuel interventions that measured time use. A total of 868 manuscripts were identified that met the search criteria, but only 2 met the inclusion criteria. Both were cross-sectional and were conducted in rural India. The first surveyed the female head of household (141 using biogas and 58 using biomass) and reported 1.2 h saved per day collecting fuel and 0.7 h saved cooking, resulting in a combined 28.9 days saved over an entire year. The second surveyed the head of household (37 using biogas and 68 using biomass, 13% female) and reported 1.5 h saved per day collecting fuel, or 22.8 days saved over a year. Based on these time savings, we estimated that clean fuel use could result in a 3.8% or 4.7% increase in daily income, respectively, not including time or costs for fuel procurement. Clean fuel interventions could save users time and money. Few studies have evaluated this potential benefit, suggesting that prospective studies or randomized controlled trials are needed to adequately measure gains.

Competing Interests: The authors declare they have no actual or potential competing financial interests.