The Contributions of Emissions and Spatial Microenvironments to Exposure to Indoor Air Pollution from Biomass Combustion in Kenya
Ezzati, Majid, Saleh, Homayoun, Kammen, Daniel M., Environmental Health Perspectives
Acute and chronic respiratory diseases, which are causally linked to exposure to indoor air pollution in developing countries, are the leading cause of global burden of disease. Efforts to develop effective intervention strategies and detailed quantification of the exposure-response relationship for indoor particulate matter require accurate estimates of exposure. We used continuous monitoring of indoor air pollution and individual time-activity budget data to construct detailed profiles of exposure for 345 individuals in 55 households in rural Kenya. Data for analysis were from two hundred ten 14-hour days of continuous real-time monitoring of concentrations of particulate matter [is less than or equal to] 10 [micro]m in aerodynamic diameter and the location and activities of household members. These data were supplemented by data on the spatial dispersion of pollution and from interviews. Young and adult women had not only the highest absolute exposure to particulate matter (2,795 and 4,898 [micro]g/[m.sup.3] average daily exposure concentrations, respectively) but also the largest exposure relative to that of males in the same age group (2.5 and 4.8 times, respectively). Exposure during brief high-intensity emission episodes accounts for 31-61% of the total exposure of household members who take part in cooking and 0-11% for those who do not. Simple models that neglect the spatial distribution of pollution within the home, intense emission episodes, and activity patterns underestimate exposure by 3-71% for different demographic subgroups, resulting in inaccurate and biased estimations. Health and intervention impact studies should therefore consider in detail the critical role of exposure patterns, including the short periods of intense emission, to avoid spurious assessments of risks and benefits. Key words: Africa, biomass combustion, exposure assessment, field study, household energy, indoor air pollution, particulate matter, public health. Environ Health Perspect 108:833-839 (2000). [Online 27 July 2000]
Acute respiratory infections and chronic respiratory diseases (obstructive pulmonary disease in particular) together account for [is greater than] 10% of the global burden of disease (1-3). In 1997 and 1998, acute lower respiratory infections were the leading causes of death from infectious diseases, with an estimated 3.7 and 3.5 million deaths worldwide for the 2 years, respectively (3, 4). Exposure to indoor air pollution, especially to particulates, resulting from the combustion of biomass (wood, crop residues, dung, and charcoal) has been implicated as a causal agent of respiratory and eye diseases (including cataracts, blindness, and possibly conjunctivitis) (5-12). This association, coupled with the fact that globally more than two billion people rely on biomass as their primary source of domestic energy, has put preventive measures to reduce exposure to indoor air pollution high on the agenda of international development and public health organizations (1,13-15).
For efficient and successful design of measures to reduce exposure to indoor air pollution, it is necessary to determine the factors that influence the level of exposure and the relative contributions of each. These factors include household energy technology (the fuel-stove combination), housing characteristics, and behavioral determinants of exposure such as the amount of time spent inside the house or near the cooking area. Accurate measurement or estimation of exposure is also essential for quantifying the exposure-response relationship for indoor particulate matter. Numerous epidemiologic studies on the health impacts of indoor air pollution have used indirect measures, such as fuel or housing type, as proxies for personal exposure (16). Given the nearly universal use of biomass fuels in rural areas, this indirect approach to exposure estimation artificially clusters numerous people into a single exposure category. …