Household air pollution exposure and associations with household characteristics among biomass cookstove users in Puno, Peru

Abstract

Background: Household air pollution (HAP) from combustion of biomass fuel, such as wood and animal dung, is among the leading environmental risk factors for preventable disease. Close to half of the world's population relies on biomass cookstoves for their daily cooking needs. Understanding factors that affect HAP can inform measures to maximize the effectiveness of cookstove interventions in a cost-effective manner. However, the impact of kitchen and household characteristics, as well as the presence of secondary stoves, on HAP concentrations is poorly understood in Puno, Peru.

Objective: To explore how household characteristics explain variability of kitchen area concentrations and personal exposures to CO, PM_2.5 and BC from biomass cookstoves among women in rural Peru.

Methods: Household characteristics (including kitchen materials and layout, wealth, and cooking behaviors) and HAP measurements were collected from 180 households in Puno, Peru, from baseline measurements of a randomized trial. Kitchen area concentrations and personal exposures to carbon monoxide (CO), fine particulate matter (PM_2.5) and black carbon (BC) were sampled for 48 h. We implemented simple and multivariable linear regression models to determine the associations between household characteristics and both kitchen area concentration and personal exposure to each pollutant.

Results: Mean daily kitchen area concentrations and personal exposures to HAP were, on average, 48 times above World Health Organization indoor guidelines for PM_2.5. We found that roof type explained the most variability in HAP and was strongly associated with both kitchen area concentrations and personal exposures for all pollutants after adjusting for other household variables. Personal exposures were 27%-36% lower for PM_2.5, CO and BC, in households with corrugated metal roofs, compared to roofs made of natural materials (straw, totora or reed) after adjusting for other factors. Higher kitchen area concentrations were also associated with less wealth, owning more animals, or sampling during the dry season in multivariable models. Having a liquefied petroleum gas (LPG) stove and having a chimney were associated with lower personal exposures, but were not associated with kitchen area concentrations. Personal exposures were lower by 21% for PM_2.5 and 28% for CO and BC concentrations among participants who had both LPG and biomass stoves compared to those with only biomass cookstoves adjusting for other household factors.

Conclusions: Characterizing HAP within different settings can help identify effective and culturally-relevant solutions to reduce HAP exposures. We found that housing roof type is strongly related to kitchen area concentrations and personal exposures to HAP, perhaps because of greater ventilation in kitchens with metal roofs compared to those with thatch roofs. Although HAP concentrations remained above guidelines for all households, promoting use of metal roof materials and LPG stoves may be actionable interventions that can help reduce exposures to HAP in high-altitude rural Peru and similar settings.

Keywords: Biomass cookstove; Black carbon; Carbon monoxide; Household air pollution; Particulate matter.

Figure 1.

Personal exposure monitor placement on apron for household participants

Figure 2.

Baseline 48-hour mean kitchen area concentrations (K) and personal exposures (P) box plots for CO, PM_2.5, and BC. Interquartile ranges of the box plots represent the 25^th and the 75^th percentiles of the 48-hr means for each group; the middle line of the box represents the 50^th percentile; the circle represents the average of the group; the sample size is indicated under each box plot. The red lines represent the WHO indoor 24-hour guideline for CO (9.4 ppm) and PM_2.5 (25 μg/m³). There is no WHO indoor guideline for BC. Acronyms: BC: black carbon; PM_2.5: fine particulate matter; CO: carbon monoxide, WHO: World Health Organization.

Figure 3.

Examples of LPG stoves as secondary stoves for cooking used in the study area

Figure 4.

Examples of traditional stoves located in recessed areas in the kitchens of study participants (A, B) and chimneys used by study participants with traditional stoves (C, D).

Figure 5.

Forest plots of multivariable linear regression coefficients (with 95% confidence intervals) of the associations between household variables and kitchen area concentrations. Multivariable model results are shown in black with numeric coefficients and 95% CI; single variable regression coefficients are shown in lighter gray. Multivariable model covariates of each kitchen area pollutant: PM_2.5 includes roof type, wealth quintile and number of open windows; CO covariates include roof type, wealth quintile, rainy season and number of pigs; BC covariates include: roof type, rainy season, use of wood, number of open windows, having dogs and samples with only the first 24 h. Each of the regression model estimates represents the ratio of the geometric mean on the pollutant compared to the reference category based on the final multivariable linear regression models. For example, a ratio of 1.1 translates to 10% higher concentrations and a value of 0.9 translates in a 10% lower concentration compared to the reference category. Abbreviations: PM_2.5: fine particulate matter; CO: carbon monoxide; BC: black carbon.

Figure 6.

Personal exposure forest plots of linear regression coefficient results of multivariable linear regression models of household variables (with 95% confidence intervals). Multivariable model results are shown in black with numeric coefficients and 95% CI; single variable regression coefficients are shown in lighter gray. Multivariable model covariates of each personal exposure pollutant: PM_2.5 includes roof type, number of bedrooms, LPG stove, stove ventilation, kitchen with adjacent wall to the main residence, having dogs and samples with only the first 24 h; CO covariates include roof type, wealth quintile and LPG stove; BC covariates include: roof type, LPG stove, stove ventilation, kitchen with adjacent wall to the main residence, number of open windows and samples with only the first 24 h. Each of the regression model estimates represents the ratio of the geometric mean on the pollutant compared to the reference category based on the final multivariable linear regression models. For example, a ratio of 1.1 translates to 10% higher concentrations and a value of 0.9 translates in a 10% lower concentration compared to the reference category. Abbreviations: PM_2.5: fine particulate matter; CO: carbon monoxide; BC: black carbon.