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. 2016 Apr 19;50(8):4564-71.
doi: 10.1021/acs.est.5b06141. Epub 2016 Mar 30.

Use of Temperature Sensors to Determine Exclusivity of Improved Stove Use and Associated Household Air Pollution Reductions in Kenya

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Use of Temperature Sensors to Determine Exclusivity of Improved Stove Use and Associated Household Air Pollution Reductions in Kenya

Matthew J Lozier et al. Environ Sci Technol. .

Abstract

Household air pollution (HAP) contributes to 3.5-4 million annual deaths globally. Recent interventions using improved cookstoves (ICS) to reduce HAP have incorporated temperature sensors as stove use monitors (SUMs) to assess stove use. We deployed SUMs in an effectiveness study of 6 ICSs in 45 Kenyan rural homes. Stove were installed sequentially for 2 weeks and kitchen air monitoring was conducted for 48 h during each 2-week period. We placed SUMs on the ICSs and traditional cookstoves (TCS), and the continuous temperature data were analyzed using an algorithm to examine the number of cooking events, days of exclusive use of ICS, and how stove use patterns affect HAP. Stacking, defined as using both a TCS and an ICS in the same day, occurred on 40% of the study days, and exclusive use of the ICS occurred on 25% of study days. When researchers were not present, ICS use declined, which can have implications for long-term stove adoption in these communities. Continued use of TCSs was also associated with higher HAP levels. SUMs are a valuable tool for characterizing stove use and provide additional information to interpret HAP levels measured during ICS intervention studies.

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Figures

Figure 1
Figure 1
Stove temperatures recorded by stove use monitors (SUMs) with data from one home where cooking events were easier to count (A), and data from another home where cooking events were more difficult to clearly identify (B).
Figure 2
Figure 2
Detail of 2-week stove installation rounds showing data used for statistical analyses: 1) SUMSs data from entire 2-week period, 2) TAL from the 48-hour period during the last 2 days of the 2-week period; and 3) PM2.5 and CO 24-hour average concentrations from day 13.
Figure 3
Figure 3
Daily average ICS cooking events by study day. NOTE: Dots above the box plots represent outliers; solid line within boxplot represents median; dotted line within boxplot represents mean. Data includes days for which data were available to assess stacking (n=1,098 household-days).
Figure 4
Figure 4
Scatter plots of 48-hour cooking events for the TCS (n=54) and ICS (n=58) as recorded by stove use monitors (x-axis) and time activity log (y-axis).
Figure 5
Figure 5
PM2.5 (A) and CO (B) concentration distributions on day 13 stratified by stove use patterns. NOTE: Baseline data were included in “Other Stove Only” to increase sample size from n=6 to n=44. “ICS Only” = household-days when only the ICS was used; “Other Stove Only” = household-days when only the TCS, charcoal, or kerosene stoves were used; and “Stacking” = household-days when stacking occurred. The dotted lines represent (A): the 24-hour WHO PM2.5 air quality guideline value of 25 μg/m3; and (B) the WHO 24 hour CO Indoor Air Quality Guideline of 7 mg/m3 (6.1 ppm based on standard conditions at 25°C and 1 atmosphere). * Geometric mean PM2.5 concentration of ICS Only and Stacking is significantly lower than Other Stove Only geometric mean concentration (p<0.05). ** Geometric mean CO concentration of ICS Only is significantly lower than Other Stove Only geometric mean concentration (p<0.05).

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