Assessing household fine particulate matter (PM_2.5) through measurement and modeling in the Bangladesh cook stove...

Biomass fuel burning is a significant contributor of household fine particulate matter (PM_2.5) in the low to middle income countries (LMIC) and assessing PM_2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM_2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM_2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM_2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM_2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m³ vs. 213 μg/m³). Exposure model performances showed 10-fold cross validated R² ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM_2.5 from the third wave of monitoring and ambient PM_2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM_2.5 included ambient PM_2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM_2.5 with moderate to high confidence using ambient PM_2.5 and household characteristics. Our results present a framework for estimating household PM_2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.