Impact of neighborhood biomass cooking patterns on episodic high indoor particulate matter concentrations in clean fuel homes in Dhaka, Bangladesh

Abstract

Exposure to particulate matter (PM2.5 ) from the burning of biomass is associated with increased risk of respiratory disease. In Dhaka, Bangladesh, households that do not burn biomass often still experience high concentrations of PM2.5 , but the sources remain unexplained. We characterized the diurnal variation in the concentrations of PM2.5 in 257 households and compared the risk of experiencing high PM2.5 concentrations in biomass and non-biomass users. Indoor PM2.5 concentrations were estimated every minute over 24 h once a month from April 2009 through April 2010. We found that households that used gas or electricity experienced PM2.5 concentrations exceeding 1000 μg/m(3) for a mean of 35 min within a 24-h period compared with 66 min in biomass-burning households. In both households that used biomass and those that had no obvious source of particulate matter, the probability of PM2.5 exceeding 1000 μg/m(3) were highest during distinct morning, afternoon, and evening periods. In such densely populated settings, indoor pollution in clean fuel households may be determined by biomass used by neighbors, with the highest risk of exposure occurring during cooking periods. Community interventions to reduce biomass use may reduce exposure to high concentrations of PM2.5 in both biomass and non-biomass using households.

Keywords: Air pollution; Bangladesh; Biomass; Cookstove; Particulate matter.

Figure 1. Risk of PM_2.5 concentrations exceeding 1000 μg/m³ by time of day and time of year in households with no obvious source of particulate matter

(A) Model estimates of the probability that PM_2.5 exceeds 1000 μg/m³ by time of day. Axes are on a logarithmic scale for visualization purposes. (B) Model estimates of the probability that PM_2.5 exceeds 1000 μg/m³ by time of day and time of year. (C) Mean number of minutes over 24-hours that PM_2.5 exceeds 1000 μg/m³ by time of year in these households using a loess curve.

Figure 2. Variation in the effects of (A) biomass use and (B) having more than two external windows and doors throughout a 24-hour period on the odds of PM_2.5 concentrations exceeding 1000 μg/m³ for biomass and clean fuel households

(A) Model estimates of the probability that PM_2.5 exceeds 1000 μg/m³ in clean fuel households (solid line) and biomass households (dashed line). The blue line is the odds ratio of PM_2.5 exceeding 1000 μg/m³ between the two household types. (B) Odds ratio of PM_2.5 exceeding 1000 μg/m³ in households with more than two external windows and doors versus households with fewer external windows and doors for clean fuel households (solid line) and biomass households (dashed line). The shaded areas are 95% confidence intervals.