Air Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) Trial.

BACKGROUND: High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure-response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru. OBJECTIVES: The primary objectives of the exposure assessment are to estimate the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure-response relationships across a range of personal exposures. METHODS: Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM < 2.5µm in aerodynamic diameter (PM2.5)] with the Enhanced Children's MicroPEM™ (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and children <1 year of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, respectively) over the course of the 18-month follow-up period using lightweight monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to estimate the accuracy of subject-specific typical exposure estimates. In addition, we are conducting ambient air monitoring to help characterize potential contributions of PM2.5 exposure from background concentration. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addition to the intervention gas stove) may occur. CONCLUSIONS: The tools and approaches being used for HAPIN to estimate personal exposures build on previous efforts and take advantage of new technologies. In addition to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts. https://doi.org/10.1289/EHP6422.

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno, Peru.

Assessment of personal exposure to PM2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM ρ = 0.91 vs UPAS ρ = 0.88) and agreement (ECM mean difference of 121.7 µg/m3 vs UPAS mean difference of 93.9 µg/m3 ) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 µg/m3 vs UPAS mean difference of 65.4 µg/m3 ) and personal exposure samples (ECM mean difference of -3.8 µg/m3 vs UPAS mean difference of -12.9 µg/m3 ). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup.

Microbial Indicator Profiling of Fresh Produce and Environmental Samples from Farms and Packing Facilities in Northern Mexico.

To compare microbiological indicator and pathogen contamination among different types of fresh produce and environmental samples along the production chain, 636 samples of produce (rinsates from cantaloupe melons, jalapeño peppers, and tomatoes) and environmental samples (rinsates from hands of workers, soil, and water) were collected at four successive steps in the production process (from the field before harvest through the packing facility) on 11 farms in northern Mexico during 2011 and 2012. Samples were assayed for enteric pathogens (Escherichia coli O157:H7, other Shiga toxigenic E. coli, Salmonella, and Listeria monocytogenes) and microbial indicators (coliforms, other E. coli strains, and Enterococcus spp.). Salmonella was the only pathogen detected; it was found in one preharvest jalapeño sample (detection limits: 0.0033 CFU/ml in produce and hand samples, 0.0013 CFU/ml in water, and 0.04 CFU/g in soil). Microbial indicator profiles for produce, worker hands, and soil from jalapeño and tomato farms were similar, but cantaloupe farm samples had higher indicator levels (P < 0.05 for all comparisons) on fruit (6.5, 2.8, and 7.2 log CFU per fruit) and hands (6.6, 3.1, and 7.1 log CFU per hand) for coliforms, E. coli, and Enterococcus, respectively, and lower E. coli levels in soil (<1 CFU/g). In water from tomato farms, E. coli indicators were significantly more prevalent (70 to 89% of samples were positive; P = 0.01 to 0.02), and geometric mean levels were higher (0.3 to 0.6 log CFU/100 ml) than those in cantaloupe farm water (32 to 38% of samples were positive, geometric mean <1 CFU/100 ml). Microbial indicators were present during all production steps, but prevalence and levels were generally highest at the final on-farm production step (the packing facility) (P < 0.03 for significant comparisons). The finding that microbial contamination on produce farms is influenced by produce type and production step can inform the design of effective approaches to mitigate microbial contamination.