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Household air pollution resulting from solid fuel combustion is a leading cause of global morbidity and mortality. Strategies to measure area concentrations of and exposures to PM 2.5 in rural homes focus primarily on short-term measurements, often of 24 or 48 h. Little is known about how well these short-term measurements, commonly used exposure metrics in health risk assessment of the impacts of household air pollution exposure, predict longer-term averages. In San Lorenzo District, Guatemala, we deployed the relatively low-cost University of California, Berkeley (UCB) Particle and Temperature Sensor (PATS) for 120–333 days in the kitchens of 8 homes using biomass fuels. We evaluated how well short-term measurements predicted the household-level, entire-sample average. A single 24-h measurement had between a 32% and 39% chance of being within ±25% of the household-level mean of all measurements. The Root Mean Square Error (RMSE) of a single 24-h measurement was on average 4.5 times higher than that of the mean of measurements taken once per study week. Alternate strategies – including sampling once per study week or once per study month – with this class of lower-cost sensors yield estimates which have a higher probability of being closer to the overall average value and have smaller errors relative to the overall mean. Evaluation of how well short-term measures predict longer-term averages of household air pollution at prospective study sites allows optimization of field resources to better estimate indoor concentrations and personal exposures. • Single 24-h PM2.5 measurements do not predict longer-term averages well. • > 48 h sampling duration substantially reduced measurement variation. • Repeated short-term measurements led to better prediction of long-term mean. [ABSTRACT FROM AUTHOR]