Air Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) Trial
2020; National Institute of Environmental Health Sciences; Volume: 128; Issue: 4 Linguagem: Inglês
10.1289/ehp6422
ISSN1552-9924
AutoresMichael Johnson, Kyle Steenland, Ricardo Piedrahita, Maggie L. Clark, Ajay Pillarisetti, Kalpana Balakrishnan, Jennifer L. Peel, Luke P. Naeher, Jiawen Liao, Daniel Wilson, Jeremy A. Sarnat, Lindsay J. Underhill, Vanessa Burrowes, John P. McCracken, Ghislaine Rosa, Joshua P. Rosenthal, Sankar Sambandam, Oscar de León, Miles A. Kirby, Katherine Kearns, William Checkley, Thomas Clasen,
Tópico(s)Energy, Environment, and Transportation Policies
ResumoVol. 128, No. 4 ResearchOpen AccessAir Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) Trialis companion ofDesign and Rationale of the Biomarker Center of the Household Air Pollution Intervention Network (HAPIN) TrialDesign and Rationale of the HAPIN Study: A Multicountry Randomized Controlled Trial to Assess the Effect of Liquefied Petroleum Gas Stove and Continuous Fuel Distribution Michael A. Johnson, Kyle Steenland, Ricardo Piedrahita, Maggie L. Clark, Ajay Pillarisetti, Kalpana Balakrishnan, Jennifer L. Peel, Luke P. Naeher, Jiawen Liao, Daniel Wilson, Jeremy Sarnat, Lindsay J. Underhill, Vanessa Burrowes, John P. McCracken, Ghislaine Rosa, Joshua Rosenthal, Sankar Sambandam, Oscar de Leon, Miles A. Kirby, Katherine Kearns, William Checkley, Thomas Clasen, and HAPIN Investigators Michael A. Johnson Address correspondence to M. Johnson, Berkeley Air Monitoring Group, 1900 Addison St., Suite 350, Berkeley, CA 94704 USA. Telephone: (510) 649-9355. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-4886-1534 Berkeley Air Monitoring Group, Berkeley, California, USA Search for more papers by this author , Kyle Steenland https://orcid.org/0000-0001-7873-4678 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Ricardo Piedrahita https://orcid.org/0000-0002-6658-2627 Berkeley Air Monitoring Group, Berkeley, California, USA Search for more papers by this author , Maggie L. Clark https://orcid.org/0000-0002-8613-5736 Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA Search for more papers by this author , Ajay Pillarisetti https://orcid.org/0000-0003-0518-2934 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Kalpana Balakrishnan https://orcid.org/0000-0002-5905-1801 Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , Jennifer L. Peel https://orcid.org/0000-0001-5155-1580 Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA Search for more papers by this author , Luke P. Naeher https://orcid.org/0000-0003-3077-5440 Department of Environmental Health Science, College of Public Health, University of Georgia, Athens, Georgia, USA Search for more papers by this author , Jiawen Liao https://orcid.org/0000-0001-8246-0542 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Daniel Wilson https://orcid.org/0000-0002-5142-0529 Geocene, Vallejo, California, USA Search for more papers by this author , Jeremy Sarnat https://orcid.org/0000-0001-8733-8749 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Lindsay J. Underhill https://orcid.org/0000-0002-1511-812X Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Search for more papers by this author , Vanessa Burrowes https://orcid.org/0000-0002-4573-074X Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Search for more papers by this author , John P. McCracken https://orcid.org/0000-0002-0714-2971 Center for Health Studies, Universidad del Valle de Guatemala, Guatemala City, Guatemala Search for more papers by this author , Ghislaine Rosa https://orcid.org/0000-0002-8776-4200 Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK Search for more papers by this author , Joshua Rosenthal https://orcid.org/0000-0002-3891-126X Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA Search for more papers by this author , Sankar Sambandam https://orcid.org/0000-0002-6424-1140 Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , Oscar de Leon https://orcid.org/0000-0003-1344-4412 Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Search for more papers by this author , Miles A. Kirby https://orcid.org/0000-0003-3468-9793 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Katherine Kearns https://orcid.org/0000-0002-1201-8529 Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , William Checkley https://orcid.org/0000-0003-1106-8812 Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Search for more papers by this author , Thomas Clasen https://orcid.org/0000-0003-4062-5788 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , and HAPIN Investigators Search for more papers by this author Published:29 April 2020CID: 047009https://doi.org/10.1289/EHP6422Cited by:1AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground: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/EHP6422IntroductionGlobally, nearly 3 billion people burn solid fuels (e.g., wood, dung, charcoal) in inefficient and poorly vented combustion devices (i.e., open fires, traditional stoves) to meet daily cooking needs (Bonjour et al. 2013). The resulting household air pollution (HAP) is a leading risk factor for global morbidity and mortality (GBD 2017 Risk Factor Collaborators 2018). However, the burden of disease related to these exposures is highly uncertain, partly due to the relatively few studies with quantitative data on personal exposures. Furthermore, the implementation of household energy interventions intended to reduce the burden of disease has not been well-informed owing to the limited understanding of exposure–response relationships for HAP. Because of financial and technical constraints associated with conducting large-scale HAP measurements in low- and middle-income country settings, many studies have relied on imprecise proxy exposure measures (Dherani et al. 2008). Measures of fine particulate matter [PM < 2.5μm in aerodynamic diameter (PM2.5)] have been particularly challenging due to the limitations of affordable, feasible, and reliable instrumentation (Balakrishnan et al. 2014; Clark et al. 2013; Pillarisetti et al. 2017).The Household Air Pollution Intervention Network (HAPIN) trial is a four-country (Rwanda, India, Guatemala, Peru) randomized controlled trial (RCT) evaluating the effects of a liquefied petroleum gas cookstove and fuel intervention vs. cooking on traditional biomass stoves among 800 households (split equally between control and intervention arms) in each of the four countries, for a total of 3,200 households. For the primary objective of the HAPIN trial, investigators will compare outcomes between the intervention and controls arms, including birthweight, severe pneumonia incidence, and stunting among infants, as well as blood pressure among older women. Although the primary analysis will not require data on exposure, describing the exposure contrast achieved between the intervention and control arms will inform the interpretation of health effect estimates. For the secondary objective of the HAPIN trial, exposure–response analyses will be conducted for these same health outcomes. The exposure–response analyses will produce results that may be transferable to other communities and stove types, given that for each proposed outcome, this information will help to refine existing exposure–response curves. Furthermore, this information, combined with our intensive evaluation of behaviors surrounding stove use, will be critical for benchmarking future stove dissemination efforts.Here we summarize our methods used for estimating personal exposure for the HAPIN participants. A description of the overall trial methods can be found in the paper by Clasen et al. (2020) and a description of the biomarker methods, including repeated measures of biomarkers of exposure [e.g., urinary polycyclic aromatic hydrocarbons (PAHs), levoglucosan], can be found in the paper by Boyd Barr et al. (2020). Our methods build on previous efforts while making use of newer approaches and tools with the aim of maximizing the quality and accuracy of personal exposure estimates. In addition to providing key personal exposure data for this study, we hope that lessons from our exposure assessment will help inform future efforts to characterize exposure to HAP.Study Setting and Exposure Sampling Design OverviewThe HAPIN trial will be conducted across four sites in India, Rwanda, Guatemala, and Peru. Study settings are mainly rural, as described in more detail by Clasen et al. (2020). Briefly, each study site recruits 800 households (400 intervention and 400 control) with pregnant women who are between 18 and 35 years of age, demonstrate 9 to <20 weeks of gestation, primarily use biomass for cooking within the home, and are nonsmokers. The specific study areas at each site are in the rural areas of Tamil Nadu, India; Department of Puno, Peru; Eastern Province, Rwanda; and Jalapa Municipality, Guatemala. These areas were largely selected based on prevalence of biomass use, low background ambient concentrations, and accessibility for field staff. Following an 18-month period of planning and formative (pilot) research, the study began recruiting participants in May 2018 and completed enrollment in February 2020. During the formative research, 40 households were enrolled in a 3-month before-and-after gas stove and fuel intervention in three of the sites (Guatemala, India, Rwanda). In Peru, formative research was done within the context of the Cardiopulmonary Outcomes and Household Air Pollution (CHAP) trial in Peru (Fandiño-Del-Rio et al. 2017). Participant acceptance of instrumentation and wearing comfort were assessed through structured surveys and informal interviews at all sites during formative work.In the main trial, we are measuring personal exposure at multiple time points for three study populations of interest: pregnant women, infants, and older adult women (40–79 years of age). All three groups will come from the same households. We are collecting three measurements in pregnant women (one at baseline prior to randomization/intervention and two at follow-up during pregnancy), three measurements in infants in the first year of life, and six measurements in older adult women (one pre-intervention) during the approximately 18 months they will be under observation (Figure 1). The purpose of the multiple measurements will be to estimate subject-specific typical exposure levels during follow-up in order to characterize exposure–contrasts between the two study arms and to assess associations with health outcomes via exposure–response analyses. For example, the pregnancy period exposures may be associated with fetal growth, birthweight, and adverse birth outcomes; and exposures over the first year of life may be associated with pneumonia, growth, and development among infants. Exposure among older adult women may be associated with changes in mean blood pressure after baseline.Figure 1. Exposure assessment timeline including frequency of assessment for intervention and control households. The intervention arm will have gas stoves, whereas the control arm will use traditional biomass stoves. In each country, direct personal measurements will be collected for 800 pregnant women during gestation and an estimated 120 older women, 40–79 years of age, living in the same households. Indirect measurements of personal exposure using a microenvironmental approach will be conducted on 800 infants from birth to 1 year of age. Traditional biomass stove usage will be continuously measured by stove use monitors during the trial, whereas gas usage will be tracked by the number of cylinders used by each household throughout the trial. Note: BC, black carbon; CO, carbon monoxide; LPG, liquid petroleum gas; PM2.5, particulate matter <2.5μm in aerodynamic diameter.We are utilizing the Enhanced Children’s MicroPEM™ (ECM), a robust, lightweight, and validated gravimetric PM2.5 monitor and the Lascar CO logger, for repeated personal 24-h measurements of pregnant women and older adult women. For infants <1 year of age, we use a newly adapted and validated indirect assessment approach that pairs microenvironmental pollutant sampling and participant proximity sensing. The microenvironmental sampling occurs in the most commonly occupied rooms and on the mother (wearing a personal monitor as a mobile microenvironment). This approach will allow us to better reconstruct infant exposures to PM2.5 compared with the use of estimating location via participant recall, while also not having to rely on a proxy measure for PM2.5 such as CO (Carter et al. 2017).An intensified exposure assessment that doubles the number of measurements over time is being conducted in a random subsample of 10% of participants per site. The random sample is selected monthly among newly recruited households. The purpose of collecting these additional measurements is to compare the average exposure level of subsample participants via the usual number of measurements with the average exposure level of subsample participants using more numerous measurements. Assuming these two averages differ only via random error, we can use the intensified assessment to correct for bias by calculating the intra-class correlation matrix in the 10% subsample [between variance/(between + within variance)], and use this to correct for classical measurement error (bias to the null) in the main study exposure–response analysis (Rosner et al. 1989). If there appears to be systematic error, for example due to seasonal effects, in our usual estimate compared with the intensified assessment, we can also use this comparison to correct our main study results (Rosner et al. 1989). We will judge that there is systematic error by whether the long-term average is significantly different (at the 0.05 level) from the short-term average from the same households. With approximately 320 short- and long-term samples across the four study sites, we should have good power to detect a systematic bias. For example, data from the formative phase indicate a mean personal exposure after intervention of about 40 μg/m3 with a standard deviation of about 20 μg/m3 across our four sites ( https://ehp.niehs.nih.gov/doi/10.1289/isesisee.2018.O02.03.31). Let us assume that there are 320 women (80 in each of four sites, a 10% sample) with both short- and long-term measurements (each with three observations each for both short- and long-term samples). However, observations within a household are correlated; therefore, for our purposes here we consider that we have only 320 independent observations for each type of sample. We would then have 80% power (with α<0.05) to detect a significant difference between short- and long-term samples if their means differed by more than about 40 μg/m3.Another important aspect of our sampling plan is employing stove use monitors to assess compliance with the intervention (Pillarisetti et al. 2017; Ruiz-Mercado et al. 2013). These monitors are small temperature sensors that can be installed inside a stove and give a continual readout of temperature that is stored for later downloading. Stove stacking (e.g., the use of baseline stoves in conjunction with the new intervention stove/fuel) has been common in studies of stove interventions (Masera et al. 2000; Rehfuess et al. 2014) and clean fuel stoves (Puzzolo et al. 2016; Quinn et al. 2018). As HAPIN is an efficacy trial, we are undertaking substantial efforts to ensure correct and consistent use of the intervention and to minimize stacking. Here, we note that it is important to monitor stove use, both to support behavioral reinforcement and to determine the extent to which stove use behaviors are associated with exposure.Exposure MeasurementsMeasured PollutantsThree primary pollutants were selected for measurement because of their health implications and associations with household fuel combustion: PM2.5, CO, and black carbon. PM2.5 has the strongest evidence linking its exposure to a variety of key health outcomes (Adetona et al. 2016; Bruce et al. 2014), allowing for the estimation of integrated exposure–risk functions for several health outcomes (Burnett et al. 2014). CO is a major product of incomplete combustion in smoke, and elevated, short-term CO exposures are linked to acute symptoms and mortality due to CO binding with hemoglobin (Goldstein 2008; WHO 2010). Evidence also suggests chronic CO exposure may be linked with other health outcomes, including asthma, cardiovascular disease, and neurological development (Dix-Cooper et al. 2012; WHO 2010). PM2.5 and CO are also the pollutants included in the World Health Organization’s Air Quality Guidelines for Household Fuel Combustion (WHO 2014), highlighting their importance in this area of environmental exposures. Black carbon was included because evidence has shown that the black carbon fractions within PM2.5 may be more strongly linked with some specific health outcomes compared with PM2.5 as a whole (Cassee et al. 2013; Janssen et al. 2001), such as for blood pressure (Baumgartner et al. 2014), one of HAPIN’s primary health outcomes.InstrumentationEquipment selection, deployment protocols, and quality assurance procedures for the main trial were evaluated during the formative phases of HAPIN.PM2.5.Our primary instrument for measuring PM2.5 is the ECM, which is well suited for our application due to its combination of small size and quiet operation compared with previous devices. The ECM (Figure 2), developed by RTI International, is a combined nephelometric and gravimetric sampler weighing approximately 150g and capable of operating continuously at 0.3L/min for up to 48 h. The ECM is virtually silent during operation; participants can wear the sampler on a shoulder band or in a pocket on a customized garment within their breathing zone. The ECM collects PM2.5 gravimetrically with a filter by drawing air through an impactor attached to a cassette containing 15-mm Teflon® filters (PT15-AN-PF02; MTL Corporation). The ECM contains a calibrated mass–flow element, a six-axis accelerometer (to log activity rate and to verify the user complies with wearing the sampler), and measures real-time PM2.5 with a nephelometer (light scattering sensor). It also logs temperature, relative humidity, and filter-pressure drop. The ECM has been used for household energy studies (Fandiño-Del-Rio et al. 2017), as has the earlier version of the instrument (the MicroPEM™) (Bruce et al. 2018; Chartier et al. 2017; Dutta et al. 2017).Figure 2. (A) Enhanced Children’s MicroPEM™ (ECM) developed by RTI International; (B) CO data logger, model EL-USB-300 (Lascar Electronics); (C) E-Sampler (Met One Instruments) installed in the Peru site; Beacon (Model O Roximity Inc.); (D) Beacon Logger (Berkeley Air Monitoring Group); and (E) Geocene stove use monitors (Geocene). [Photo credits: Michael Johnson (A), Ricardo Piedrahita (B), Ajay Pillarisetti (C), and Ricardo Piedrahita (D), and Daniel Wilson (E).]ECM preparation before deployment includes component cleaning using ethanol and lint-free wipes and device calibration. Three-point flow calibrations are performed before each deployment, as well as nephelometer, temperature, and humidity offsets. Flow calibration is done with National Institute of Standards and Technology–traceable flow calibrators. Post-deployment, ECMs are transported in coolers to the field offices, where the data is downloaded and viewed using a web-based analysis tool to assess data quality. Post-sample flows are checked and recorded, after which the filters are transferred to cold storage (quality controls for filter processing and analysis are described above). Maintenance is performed as needed for ECM components and is based on calibration performance and data analysis checks. The real-time data files are assessed biweekly using an automated system to check the volumetric flow rate, nephelometer, inlet pressure, compliance (accelerometry), temperature, and relative humidity. Flags are generated and reported to the sites based on predetermined thresholds for each variable. Data quality is also assessed through the use of duplicate ECM deployments and field blank filters. Duplicate ECM deployments, for which two ECMs are placed side-by-side, are being conducted on at least 30 personal and 30 area samples at each site, and field blank filters are being collected for 3% of all samples to correct for changes in mass associated with filter handling and processing. In addition, at least 20% of PM2.5 ECM microenvironmental area measures include a pre-weighed filter for gravimetric collection and analysis, while the remainder rely on those gravimetric values to adjust the nephelometer readings.Carbon monoxide.Real-time carbon monoxide (CO) concentrations are being measured with Lascar CO monitors (model EL-USB-300; Lascar Electronics). As with most personal CO monitors, the Lascar CO monitor uses an electrochemical cell to detect CO. The instrument is small (the size of a large pen), silent, can log continuously for days, has a range of 0–300 ppm, and has also been used to assess exposures and HAP in several other monitoring efforts (Das et al. 2018; Piedrahita et al. 2019a). Monthly two-point calibrations are performed with each Lascar CO logger. Data is also visually inspected after each deployment to ensure there are no signs of instrument malfunction. Side-by-side duplicate CO measures are being conducted for 10% of all data collected.Black carbon.Black carbon is being measured on the PM2.5 filters collected via the ECM and from the ambient monitors. Black carbon is being quantified on the filters using a SootScan™ Model OT21 transmissometer (Magee Scientific), which has been used often for characterizing black carbon for personal exposure and emissions studies (Baumgartner et al. 2014; Garland et al. 2017; Rajkumar et al. 2018). The instrument measures the light attenuation through the filter at the 880-nm wavelength, which is then converted into a black carbon surface deposition.Pregnant WomenExposures of pregnant women (and prenatal exposures of their children) are measured with ECMs and CO loggers worn in a vest or apron for three 24-h periods during the pregnancy (<20, 24–36, and 32–36 weeks of gestation) (Figures 1–3). The women are asked to wear the vest or apron at all times during each measurement period except when sleeping, bathing, or when conducting other activities for which the equipment cannot be safely worn.Figure 3. Example photos of participants wearing customized vests and/or aprons with exposure monitoring equipment. (A) Guatemala; (B) India; (C) Peru; and (D) Rwanda. The picture of the sampling garment in Guatemala was taken when a pump and cyclone setup was also being compared with the Enhanced Children’s MicroPEM™ (ECM) during Household Air Pollution Intervention Network (HAPIN)’s formative research phase. [Photo credits: Eric Mollinedo (A), Thangavel Gurusamy (B), Vanessa Burrowes (C), and Ephrem Dusabimana (D)].To estimate exposures to their children after birth, the mothers wear the sampling vest or apron during three 24-h periods (<3, ∼6, and ∼12 months) after their child’s birth. During these time periods, mothers are asked to place the vest or apron holding the equipment near their child when they are not wearing it and to leave the sampling vest where the child is expected to spend most of their time if they leave the home without their child. The vests and aprons secure the ECMs and CO loggers near the breathing zone, a similar approach to that used in other HAP exposure studies (Balakrishnan et al. 2018; Bruce et al. 2018; Delapena et al. 2018; Hill et al. 2019; Nagel et al. 2016). Compliance is checked via the ECM’s accelerometer data to determine if motion is detected during normal daily activities and participants are also directly asked at the end of each sampling duration about wearing the monitors as part of the survey.Older Adult WomenExposures among older women living in the same home as a pregnant participant are also measured by ECMs and CO loggers worn in a vest or apron during three 24-h periods during the pregnancy (<20, 24–36, and 32–36 weeks of gestation) and three 24-h periods after the pregnancy (<3, ∼6, and ∼12 months after birth) (Figures 1–3). As with the pregnant women, the older women are asked to hang the vest or apron nearby when it is not being worn and compliance is checked via accelerometry and questionnaire.ChildrenChildren’s exposure is estimated using a microenvironmental approach. The primary environment comes from data collected by ECMs and CO loggers worn in a vest or apron by their mother, as described above (Figures 1–3). ECMs and CO loggers are also placed in the primary cooking area and the infant’s sleeping area. Two coin-sized location Beacons [EMBC-01, EM Microelectronics-Marin SA (Figure 2)] are worn by the children and linked to receivers where the ECMs are located, including in the mother’s sampling vest. Personal exposures for the child are estimated by integrating corresponding area concentrations over the time spent in that location.The microenvironmental approach is used because even with the small size of the ECM, it is still impractical for use on young infants. The approach used here is similar to previous efforts (Balakrishnan et al. 2004; Ezzati et al. 2000; Saksena et al. 2003; Zuk et al. 2007) but adds an objective measure of location with the Beacons by tracking where the child is over 24 h (Piedrahita et al. 2019b; Liao et al. 2019). Objective measures of location are key for using this approach because participant recall of time–activity patterns can be unreliable and are often biased (Daum et al. 2018).Results from the HAPIN formative work in Guatemala have shown that the Beacon system provides accurate time–location patterns, and this microenvironmental approach can predict personal exposure better compared with a single area measurement (Liao et al. 2019). The system was piloted by the four sites and found to be acceptable to participants. Specific results from our formative work indicated that indirect exposure measurements had high correlation wi
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