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Inspecting what you expect: Applying modern tools and techniques to evaluate the effectiveness of household energy interventions


Exposure to fine particles (PM2.5) resulting from solid fuel use for household energy needs – including cooking, heating, and lighting – is one of the leading causes of ill-health globally and is responsible for approximately 4 million premature deaths and 84 million lost disability-adjusted life years globally. The well-established links between cooking and ill-health are modulated by complex social, behavioral, technological, and environmental issues that pose unique challenges to efforts that seek to reduce this large health burden. Despite growing interest in the field – and numerous technical solutions that, in the laboratory at least, reduce emissions of harmful air pollutants from solid fuel combustion – there exists a need for refined tools, models, and techniques (1) for measuring environmental pollution in households using solid fuel, (2) for tracking adoption of interventions, and (3) for estimating the potential health benefits attributable to an intervention.

Part of the need for higher spatial and temporal resolution data on particular concentrations and dynamics is being met by low-cost sensing platforms that provide large amounts of time-resolved data on critical parameters of interest, including PM2.5 concentrations and time-of-use metrics for heat-generating appliances, like stoves. Use of these sensors can result in non-trivial challenges, including those related to data management and analysis, and field logistics, but also enables novel lines of inquiry and insight. Chapter 2 presents a long-term deployment of real-time PM2.5 sensors in rural, solid-fuel-using kitchens, specifically seeking to evaluate how well commonly measured 24 or 48-hour samples represent long-term means. While short-term measures were poor predictors of long-term means, the dataset enabled evaluation of numerous sampling strategies – including sampling once per week, month, or season – that had much lower errors and higher probabilities of estimating the true mean.

Chapters 3 and 4 describe the selection and deployment of 200 advanced cookstoves to pregnant women in rural Palwal District, Haryana, India. Chapter 3 focuses on selection and evaluation of an intervention stove in the community, including preliminary measurement of exposure to PM2.5 and CO. These data suggest one method of piloting interventions and exposure assessment methods prior to larger rollouts to ensure community acceptability and feasibility. Chapter 4 specifically addresses adoption and use of the intervention stove over a period of approximately one year through the deployment of data-logging thermometers on 200 traditional and intervention stoves. Intervention stove use declined steadily over time and stabilized after approximately 200 days, while use of the traditional stove remained constant, emphasizing the need for monitoring both traditional and intervention stoves and for monitoring for periods of time beyond just the initial deployment to truly understand use. Chapter 4 additionally investigated intervention stove failures and how well short measures of stove use predict long- term trends (similar to the analysis performed in Chapter 2).

Chapter 5 focuses on utilizing the best available knowledge of exposure-response relationships to estimate the potential health impacts of an intervention at the national level in a software package called HAPIT, the Household Air Pollution Intervention Tool. HAPIT combines background disease data from the 2010 Global Burden of Disease with demographic and socioeconomic data and relative risk estimates from the integrated exposure-response curves to estimate disability-adjusted life years (DALYs) and deaths that could be averted by an exposure- reducing household air pollution intervention. Chapter 5 outlines the methodologies powering HAPIT and contains two example scenarios – one in which open fires are replaced by well- operating chimney stoves, and a second where they are replaced by LPG -- informed by data from the RESPIRE trial and ongoing work in Guatemala.

Chapter 6 synthesizes work from the proceeding chapters and offers suggestions for future lines of inquiry.

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