Real-time indoor measurement of health and climate-relevant air pollution concentrations during a carbon-finance-approved cookstove intervention in rural India. (2018) - Kelp, Makoto M.; Grieshop, Andrew P.; Reynolds, Conor C.O.; Baumgartner, Jill; Jain, Grishma; Sethuraman, Karthik; Marshall, Julian D.
Abstract: Biomass combustion in residential cookstoves is a major source of air pollution and a large contributor to the global burden of disease. Carbon financing offers a potential funding source for health-relevant energy technologies in low-income countries. We conducted a randomized intervention study to evaluate air pollution impacts of a carbon-finance-approved cookstove in rural South India. Prior research on this topic often has used time-integrated measures of indoor air quality. Here, we employed real-time monitors (∼24 h measurement at ∼ minute temporal resolution), thereby allowing investigation of minutely and hourly temporal patterns. We measured indoor concentrations of fine particulate matter (PM2.5 ), black carbon (BC) and carbon monoxide (CO) in intervention households (used newer, rocket-type stoves) and control households ("nonintervention"; continued using traditional open fire stoves). Some intervention households elected not to use only the new, intervention stoves (i.e., elected not to follow the study-design protocol); we therefore conducted analysis for "per protocol" versus "intent to treat." We compared 24 h averages of air pollutants versus cooking hours only averages. Implementation of the per protocol intervention cookstove decreased median concentrations of CO (by 1.5 ppm (2.8 − 1.3; control − per protocol), p = 0.28) and PM2.5 (by 148 μg/m 3 (365 − 217), p = 0.46) but increased BC concentration (by 39 μg/m 3 (26 − −12), p < 0.05) and the ratio of BC/PM2.5 (by 0.25 (−0.28 − −0.03), p < 0.05) during cooking-relevant hours-of-day relative to controls. Calculated median effective air exchange rates based on decay in CO concentrations were stable between seasons (season 1: 2.5 h −1, season 2: 2.8 h −1 ). Finally, we discuss an analytical framework for evaluating real-time indoor datasets with limited sample sizes. For the present study, use of real-time (versus time-averaged) equipment substantially reduced the number of households we were able to monitor.