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Evaluation and application of prototype air quality monitors for household air pollution exposure assessment Birch, Aaron


Household air pollution (HAP) from burning of low-quality fuels is a significant contributor to global burden of disease, particularly in low- and middle- income countries. Epidemiological studies of HAP have been hampered in their ability to collect quantitative exposure measurements from a lack of affordable, durable and easily usable air quality monitors. New devices offer potential to overcome these obstacles but must be tested in real world conditions before deployment. This study’s goal was to evaluate the performance of three prototype monitors compared to two reference monitors and their applicability for use in a prospective cohort epidemiologic study. Prototype monitors tested included a filter-based monitor, and two particle counters. Simple linear regression models of HAP exposure were constructed using questionnaires and observational data. 55 households were recruited for HAP monitoring in two villages in India in 2015. Monitors were placed in the household kitchens for 24- and 48-hour sample periods. Male and female household residents were recruited for personal fine particle (PM₂.₅) exposure monitoring using the filter-based prototype monitor. All filter samples were analyzed for PM₂.₅ mass concentrations and particle light absorbance. Successful filter samples collected with the V1.0 Ultrasonic Personal Aerosol Sampler (UPAS, Access Sensor Technologies, Fort Collins, CO), were obtained in 81% of homes with successful reference measurements. Fewer successful samples were collected with prototype particle counters, (43% and 75%). Personal monitoring with the UPAS succeeded in 54% of attempts. There was a high level of agreement between prototype filter and reference monitor (R² = 0.85 and slope = 0.98 for PM₂.₅ and R² = 0.88 with slope = 1.63 for absorbance). Neither prototype particle counter performed well enough for subsequent analyses. The best performing models of HAP exposure were for individual communities with a broad pool of predictors; including multiple types and amounts of fuels and cooking times, versus models combining communities with a narrower set of predictors. Using a broader variable pool improved adjusted R² values by as much as 0.35. Recommendations were made for improvements for the UPAS sampler. An updated (V2.0) UPAS sampler was selected by the PURE AIR study of HAP.

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