Health Policy and Planning Advance Access originally published online on October 9, 2006
Health Policy and Planning 2006 21(6):444-458; doi:10.1093/heapol/czl027
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Who suffers from indoor air pollution? Evidence from Bangladesh
Development Research Group, World Bank, Washington DC, USA
Correspondence: Susmita Dasgupta, Development Research Group, World Bank, 1818 H Street, NW, Washington DC 20433, USA. E-mail: sdasgupta{at}worldbank.org
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Abstract |
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Top
Abstract
IntroductionIn this paper, we investigate individuals’ exposure to indoor air pollution. Using new survey data from Bangladesh, average hours spent by members of households in the cooking area, living area and outdoors in a typical day are combined with the estimates of pollution concentration in different locations in order to estimate exposure. We analyse exposure at two levels: differences within households attributable to family roles, and differences across households attributable to income and education. Within households, we relate individuals’ exposure to pollution in different locations during their daily round of activities. We find high levels of exposure for children and adolescents of both sexes, with particularly serious exposure for children under 5 years. Among prime-age adults, we find that men have half the exposure of women (whose exposure is similar to that of children and adolescents). We also find that elderly men have significantly lower exposure than elderly women. Across households, we draw on results from a previous paper, which relate pollution variation across households to choices of cooking fuel, cooking locations, construction materials and ventilation practices. We find that these choices are significantly affected by family income and adult education levels (particularly for women). Overall, we find that the poorest, least-educated households have twice the pollution levels of relatively high-income households with highly educated adults.
Our findings further suggest that young children and poorly educated women in poor households face pollution exposures that are four times those for men in higher income households organized by more highly educated women. Since infants and young children suffer the worst mortality and morbidity from indoor air pollution, in this paper we consider measures for reducing their exposure. Our recommendations for reducing the exposure of infants and young children are based on a few simple, robust findings. Hourly pollution levels in cooking and living areas are quite similar because cooking smoke diffuses rapidly and nearly completely into living areas. However, outdoor pollution is far lower. At present, young children are only outside for an average of 3 hours per day. For children in a typical household, pollution exposure can be halved by adopting two simple measures: increasing their outdoor time from 3 to 5 or 6 hours per day, and concentrating outdoor time during peak cooking periods.
Key Words: indoor air pollution, human exposure, household, Bangladesh
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Introduction |
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Indoor air pollution (IAP) from burning wood, animal dung and other biofuels is a major cause of acute respiratory infections, which constitute the most important cause of death for young children in developing countries (Murray and Lopez 1996
; Smith et al. 2004). Acute lower respiratory infection (ALRI), the most serious type of acute respiratory infections, is often associated with pneumonia (Kirkwood et al. 1995). ALRI accounts for 59% of all premature deaths in young children, and nearly all of these deaths occur in developing countries, with the heaviest losses in Asia and Africa (Smith et al. 2004). Through its effect on respiratory infections, IAP is estimated to cause between 1.6 and 2 million deaths per year in developing countries (Smith 2000; Smith et al. 2004). Most of the dead are in poor households and approximately 1 million are children (Smith 1993; Smith et al. 1993; Smith and Mehta 2000). Table 1 provides estimates of health damage from IAP by region.1
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The size of IAP's estimated impact has prompted the World Bank (2001
) and other international development institutions to identify the reduction of IAP as a critical objective for the coming decade. The current scientific consensus is that most respiratory health damage comes from inhalation of respirable particles whose diameter is less than 10 microns (PM10), and recent attention has focused particularly on fine particles (PM2.5).
In a previous paper, we analysed variations in average IAP levels across Bangladeshi households (Dasgupta et al. 2004). We found that common variations in fuel use, cooking locations, construction materials and ventilation characteristics lead to large differences in IAP. Non-fuel characteristics are so influential that some households using ‘dirty’ biomass fuels have PM10 concentrations comparable with those in households using clean fuels such as liquid natural gas. Under adverse conditions, on the other hand, Bangladeshi households using dirty fuels can experience 24-hour average PM10 concentrations as high as 800 ug/m3.
Such concentrations are far higher than outdoor PM10 levels considered dangerous for public health in industrial societies (Galassi et al. 2000). In those societies, however, use of clean fuels is so pervasive that attention focuses on outdoor pollution. In biofuel-using Bangladeshi households, particularly in rural areas, the calculus is often reversed: IAP may be much worse than outdoor pollution, and health risks may be severe for household members who are exposed to IAP for long periods during the day.
In this paper, we use our survey data to estimate IAP exposure for family members by age/sex group, with a particular focus on young children. We investigate the two major sources of differential exposure: individuals’ time spent in different locations (cooking areas, living areas and outside), and hourly fluctuations in pollution from cooking. We also assess the effect of parents’ income and education on average household pollution levels.
We first present the methods of our analysis, describe the sampling strategy, and how indoor air and exposure to IAP have been monitored for this exercise. The following four sections discuss the sources of variation in individuals’ exposure to pollution within households. These sections analyse individuals’ daily location patterns, their interaction with daily cycles in pollution from cooking, the implications for pollution exposure, and some possible remedies for the most vulnerable family members (particularly young children). We then compare our results with those from a recent study in India. In the penultimate section, we assess the effects of income and adult education on both determinants of exposure: average pollution levels across households, and patterns of activity within households. Finally, we provide a summary and conclusions.
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Methods |
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Selection of study households where indoor air was monitored
Previous studies have identified several potential determinants of exposure to IAP: fuel type, time spent in cooking, cooking location, structural characteristics of houses and household ventilation practices (opening of windows and doors etc.) (Brauer and Saxena 2002; Freeman and Sanez de Tajeda 2002; Moschandreas et al. 2002; World Bank 2002). All of these factors may be important in Bangladeshi households, which exhibit significant diversity in cooking fuels, stove types, cooking locations and ventilation characteristics of houses. Discussion with local experts revealed widespread use of gas, electricity, kerosene, firewood, cow dung, rice husks, straw, jute sticks, bagasse and sawdust as fuel; four cooking locations (separate attached, separate detached, outside/open, single room dwelling – no separate kitchen); and thatch, tin, mud and brick as common structural materials of houses.
For this research, we used stratified sampling in urban and peri-urban areas of Narshingdi (Dhaka region) to incorporate representative variations in fuel use, cooking arrangements and structural characteristics that affect ventilation.2 We separated the households into groups defined by cooking fuel, kitchen type and location, and construction material. Then we selected households independently from each group.3 Our sample size was 236, given cost constraints.
Monitoring of indoor air
At each household, PM10 concentrations in the cooking and living areas were monitored for a 24-hour period during December 2003 – February 2004. We used two devices for monitoring indoor air: (1) a real-time monitoring instrument, the Thermo Electric Personal DataRAM (pDR-1000) (Thermo Electron 2004), and (2) a 24-hour instrument, the Airmetrics MiniVol Portable Air Sampler (Airmetrics 2004). The pDR-1000 uses a light scattering photometer (nephelometer) to measure airborne particle concentrations. The operative principle is real-time measurement of light scattered by aerosols, integrated over as wide a range of angles as possible. This instrument operated continuously for 24-hour periods, recording PM10 concentrations at 2-minute intervals. The Airmetrics MiniVol Portable Air Sampler, on the other hand, is a more conventional device that samples ambient air for 24 hours. The MiniVols were generally programmed to draw air at 5 litres per minute through PM10 particle size separators. The particles were caught on the filters, and the filters were weighed pre- and post-exposure with a precisely calibrated microbalance at Airmetrics, Inc.4 The readings of pD-RAM and MiniVol air samplers provide a detailed record of IAP concentration in each household.
Household questionnaire administration
A short questionnaire was administered to each household on the same day as the air monitoring to obtain information on socio-economic characteristics of the household members, fuel type, fuel quantity, stove location, cooking time, number of people cooked for, duration of fire after cooking, the use of iron, mud, thatch and concrete for construction of the house and kitchen, the placement and size of windows, doors and ventilation spaces between walls and roofs, ventilation practices such as opening doors and windows after cooking, smoking practices, and the use of lanterns and mosquito coils. In addition, all members of the households were questioned regarding their time activity pattern: average hours spent in the cooking area, living areas and outdoors in a typical day to get an indication of exposure.
Regression analysis to explore the determinants of IAP
A regression analysis for these 236 households was conducted to explore the determinants of IAP. Households’ PM10 concentrations were regressed on fuels used during the monitored day, cooking time, duration of fire after cooking, number of people cooked for, stove location, the use of iron, mud, thatch and concrete for construction of the house and kitchen, the placement and size of windows, doors and ventilation spaces between walls and roofs, ventilation practices such as opening doors and windows after cooking, smoking practices, and the use of lanterns and mosquito coils. Among these variables, a small set were found to significantly affect household PM10 concentrations: fuel type, stove locations, building materials, and opening doors and windows after cooking.
Extrapolation and exposure reconstruction
Since we have analysed the determinants of IAP using a stratified sample of urban and peri-urban households in the Dhaka region, the sample was not intended to represent all Bangladeshi households. In order to assess the broader implications, a representative household survey was conducted in six districts in six geographical regions: Rangpur in the Northwest, Sylhet in the Northeast, Rajshahi and Jessore in the West, Faridpur in the Centre, and Cox's Bazar in the Southeast. Table 2 documents total population, urban population and total number of villages in each district.
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In each district, we have attempted to randomly survey 25 peri-urban households, 25 urban households5 and 50 rural households.6 The same household questionnaire was administered to each household to obtain information on socio-economic characteristics of the household members, fuel, cooking time, structural characteristics, ventilation practices and time activity pattern of each household member. The regression results for Dhaka region were then extrapolated to estimate cooking and living area PM10 levels for this random sample of 600 households. Outdoor 24-hour PM10 concentrations were monitored for a number of peri-urban and rural monitoring points. Respondent estimates of exposure duration (in cooking areas, living areas and outside) from the household surveys were then combined with the respective estimates of pollutant concentration to estimate exposure to PM10. We estimated IAP exposure for family members by age/sex group, with a particular focus on young children.
Analysis of differential exposure
Finally, we investigated the two major sources of differential exposure: individuals’ time spent in different locations (cooking areas, living areas and outside), and hourly fluctuations in pollution from cooking. Exposure was analysed at two levels: differences within households and differences across households. Within households, we examined individuals’ exposure to pollution in different locations during their daily round of activity. Across households, we assessed the effects of income and education on average household pollution levels with regression analysis.
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Daily location patterns in Bangladeshi households |
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Within households, individuals’ pollution exposure may vary significantly because they spend very different amounts of time in cooking areas, living areas and outside the house. Table 3 reports average daily hours in the three locations for a representative sample of 4612 individuals drawn from surveyed households in rural, peri-urban and urban areas of seven Bangladeshi regions (Figure 1): Rangpur (491 individuals) in the Northwest, Sylhet (578) in the Northeast, Rajshahi (491) and Jessore (490) in the West, Faridpur (497) and Dhaka (1493) in the Centre, and Cox's Bazar (572) in the Southeast.
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Table 3 presents statistics by sex, because gender roles are quite different in Bangladeshi households. Among age groups, we distinguish infants (age 0–1) and young children (age 1–5) because they are most vulnerable to air pollution, and most tied to their mothers’ patterns of activity. We divide school-age youths into two groups with different patterns of school attendance that may have implications for exposure to indoor pollution: students aged 6–8 years, who attend school in the morning (9 a.m.–12 a.m.), and students aged 9–19 years, who attend from midday until late afternoon (11.30 a.m.–4.30 p.m.). We divide adults into prime-age (20–60) and older (60+) categories.
Table 3 shows that time-location patterns are very similar for infants of both sexes. They spend relatively short periods in cooking areas (1 hour per day), very long periods in living areas (20 hours/day), and the residual time (3 hours) outside the house. Infants spend more time indoors than any other age group. Children from 1–5 exhibit a very similar pattern, with little difference between the sexes. After age 5, however, gender differences emerge strongly. In cooking areas, the gap between female and male hours rises steadily through maturity (to 3.6 extra hours for women aged 20–60), and then falls substantially for older women. Living areas exhibit a similar pattern, with the gap between women and men increasing steadily from early adolescence through old age. Men spend much more time outside of the house than women: the gap is 3.4 hours for adolescents, 6.5 hours for adults aged 20–60, and 4.5 hours for older adults.
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Daily pollution cycles |
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To assess the implications of Table 3 for pollution exposure, we need information on the levels and daily variations of PM10 pollution in cooking areas, living areas and outside the house. Results from Dasgupta et al. (2004
), cited in Appendix 3, documented extensive indoor air-quality monitoring in the peri-urban area of Narshingdi (Dhaka region).7 In 236 Narshingdi households, the average 24-hour PM10 concentration was 260 ug/m3 (s.d. = 156) for cooking areas and 210 ug/m3 (s.d. = 115) for living areas.8 To estimate outdoor concentrations in peri-urban and rural areas, we took 24-hour ambient readings in Narshingdi (8 monitoring points) and in rural areas of Jessore (16 points) and Rangpur (11 points).9 Average outdoor concentrations in the three locations were 36, 48 and 62 ug/m3, respectively (s.d. = 11, 18 and 11, respectively). The overall average for 35 monitoring points was 50 ug/m3 (s.d. = 17), which we have adopted as our estimate of outdoor pollution for this exercise.
In Dasgupta et al. (2004), we have shown that pollution generated in cooking areas diffuses almost immediately into living areas (for illustrations of the close relationship, see Appendix 2). As a result, pollution in both areas exhibits strong pollution cycles in response to fuel combustion for cooking. Drawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi with the pDR-1000,10 Figure 2 displays a typical daily pollution cycle as a 24-hour plot of the ratio of the hourly mean PM10 concentration to the daily mean concentration.11 Its distinguishing features include two peaks during morning and evening cooking times, when pollution rises to over 3 times the daily average, and extensive periods in the afternoon and evening when pollution is substantially lower than the daily average. Daily indoor pollution cycles are also reflected in outdoor ambient cycles, as many houses emit cooking smoke. Figure 3 illustrates a typical ambient cycle, drawn from 24-hour monitoring at 7 points in rural villages of Jessore and Rangpur.12 We combine mean indoor and outdoor pollution concentrations with the hourly ratios in Figures 2 and 3 to produce Table 4, which provides hourly estimates of PM10 in cooking areas, living areas and outside the house.
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To interpret these results, it is useful to note that India's 24-hour standard13 for rural exposure to PM10 is 100 ug/m3. This 24-hour standard is exceeded in the indoor cooking area by 2.5 times, and in living areas by 2 times, in Bangladesh. It is also noteworthy that during peak cooking periods, 1-hour PM10 concentrations rise to 845 ug/m3 in the cooking area and 683 ug/m3 in the living areas.
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Daily exposure for different household members |
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We combine the information in Table 3 and Table 4 to produce estimates of daily average PM10 exposure concentration by age/sex group. To incorporate our hourly estimates for PM10 concentrations in cooking, living and outside areas, we adopt a set of conventions for assigning family members to locations during the 24-hour cycle. We provide a complete accounting of assignments for all age/sex groups in Appendix 1. We assume that infants and children aged 0–5 spend their single hour in the cooking area during the morning peak cooking time. We assume that women's cooking-area time is during peak cooking periods, and that young children have their outside time during the mid-afternoon.
For older children, part of outside time reflects schooling schedules; we assume that the balance is devoted to play in the mid to late afternoon. Men's outside work times lie in the interval 7 a.m.–6 p.m., with total hours reflecting the totals in Table 3. After accounting for cooking-area and outside times, all family members are assigned to inside living areas for periods that match the totals in Table 3. We compute daily exposure for members of each age/sex group by adding the 24 hourly PM10 concentrations for their assigned locations and calculating the mean concentration. Table 5 presents the results.
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To check the general validity of our estimates for a typical household,14 we replicate our approach 236 times, using the mean PM10 concentration for each household monitored by our study. These concentrations vary widely, for reasons explored in Dasgupta et al. (2004
). We average the results and present them alongside the typical household results in Table 5. Differences in the two sets of results stem from variations in average pollution levels, household age/sex compositions and time allocations by household members in the full set of monitored households. We present the results for the typical household in order to display exposure variations when inside/outside pollution concentrations and individuals’ time allocations are held constant. In any case, the two sets of results are quite similar. The most striking finding is the high exposure – around 200 – for infants and children, regardless of gender. Exposures for student-age individuals (6–19) are somewhat lower (although still quite high), and also similar for both sexes. The real gender-based divergence occurs among adults, with women's exposures nearly twice those for men in the age group 20–60, and about 40% higher for older women (over 60).
Table 5 indicates that only adult males aged 20–60 have daily PM10 exposures low enough to approach the Indian standard (100 ug/m3). All other household members have significantly higher exposure levels, and the youngest children of both sexes have exposures that are among the most dangerous. Mortality from respiratory disease among children in this age range attests to the potent impact of such pollution levels.
Two features of our results warrant particular scrutiny. First, most studies have traditionally focused on pollution in cooking areas. However, our monitoring of Bangladeshi households suggest that simultaneous pollution in living areas is the root cause. This is particularly true for young children, who spend only 1 hour per day in cooking areas, on average. Living-area pollution is only moderately below cooking-area pollution and follows the same cycle, so most daily inhalation of particulates occurs in the living areas. Adult males have lower exposures simply because they are out of the house for many more hours per day.
Secondly, we should qualify our results with a cautionary note about the impact of very intense pollution on women and children during peak cooking periods. It is possible that peak pollution during a few hours per day causes disproportionate health damage. Scientific evidence currently available suggests that health damage is associated with daily average exposure levels, not peak hourly exposures. However, the evidence is far from conclusive, and it is mostly derived from research on outdoor pollution effects in industrial economies.15 Given the large peaks of PM10 concentration during cooking in many Bangladeshi households, further research on this issue seems justified.
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Reducing exposure for young children |
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We use the case of infants (age 0–1) in our typical household to illustrate some implications of our results. We focus on infants because their documented vulnerability to indoor pollution seems to be the greatest. Our example applies to both male and female infants, since Table 5 shows insignificant gender difference for daily exposure concentrations (216 ug/m3 for females vs. 214 for males).
Figure 4 presents a simple experiment with data for the typical household. Starting with the status quo, with infants spending 3 hours outside in mid-afternoon, we optimize the 3-hour outside period by switching to other times (e.g. 8 a.m.) that provide the greatest relief from indoor pollution. Then we add 3 more hours outside, sequentially choosing times that yield the greatest incremental reductions in daily PM10 exposure. We plot the results in Figure 4.
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Figure 4 indicates that keeping infants outside during an optimally chosen 6-hour period during peak cooking times (7–10 a.m.; 5–7 p.m.) would reduce daily PM10 exposure to the Indian standard level (100 ug/m3). Since approximately half of the sample households have PM10 concentrations above the mean level for our survey population (260 ug/m3 in cooking areas, 210 in living areas), the potential reduction in exposure for infants in many homes could be much greater.16
Our results suggest that for households whose young children are kept inside during peak cooking periods, simply moving the children outside when weather permits could yield significant reduction in exposure. Household members assigned to outside supervision would also benefit from reduced pollution. In cases where family help is scarce, it might be possible for several households to pool supervision during peak periods. While this might create some inconvenience, families might well consider this option if they recognized the potential health benefits for their children. By the same logic, of course, other family members would benefit from extending their outside time during peak cooking periods.
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Comparison with results for India |
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A recent study of IAP exposure in India (World Bank 2002
; Balakrishnan et al. 2004a) provides a useful point of comparison for our results. The India study also considers different age/sex groups, cooking vs. living areas, and cooking vs. non-cooking times. Its conclusions are similar to our findings in some respects:Whereas women, in their traditional capacity as cooks, suffer from much greater average daily exposures than other family members, adult men experience the least exposure. Among non-cooks, those who are most vulnerable to the health risks of IAP — young children and elderly people — tend to experience higher levels of exposure because they spend more time indoors.(World Bank 2002
As Table 5 shows, we also find large differences between women and men in exposure to air pollution.17 However, we find essentially no difference in exposure for women and young children of both sexes. We also find relatively small differences between women's exposures and exposures for adolescents of either sex. The essential difference between our results and the India results lies in our findings for pollution in living areas. We find average living-area pollution concentrations to be much closer to cooking-area concentrations, and our 24-hour monitoring data indicate that daily pollution cycles are close to identical for the two areas.18 As a result, time spent in living areas does not provide much relief from pollution exposure.19 Time spent outside the house therefore emerges as the key variable in our analysis, and adult males have much smaller pollution exposure simply because of their outside orientation.
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Income, education and exposure |
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Households’ PM10 concentrations and individuals’ location patterns combine to produce pollution exposures for family members. As we have shown in Dasgupta et al. (2004
), household pollution levels are highly sensitive to fuel choices, cooking locations and construction materials that affect ventilation. Each of these factors may, in turn, be affected by household income and adult education levels. If such effects are significant, they may be related in part to pollution-related problems, and in part to other factors (e.g. status issues associated with choice of building materials and cooking locations). The positive income elasticity of clean fuel choice should also have a significant effect, at least in urban areas where clean fuel is a feasible option. Education may affect adults’ awareness of the relationship between pollution exposures and health risks for infants and young children. If this is the case, then we might expect children to spend more time outside in more highly educated families. We are agnostic about whether income could also have an effect in this context.
We test the effects of income and education on pollution exposure factors, using Ordinary Least Square regressions adjusting for heteroskedasticity, for our full sample of households in seven regions of Bangladesh. We use the econometric results presented in Dasgupta et al. (2004) to estimate PM10 in each household, incorporating the combined effects of fuel choices and structural characteristics (cooking locations and construction materials). We regress estimated PM10 on household income (in US$ per day) and the average education levels20 of men and women in the household. For infants and young children, we regress time spent outside on the same variables.
We present the regression results for both exposure components in Table 6.
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Education and household per capita income have the expected effects on determinants of indoor concentration of PM10. All estimated effects are large, highly significant and have the expected negative sign. Women's education has a particularly large effect. Our results indicate that when men's and women's education levels jointly increase from 0 (no primary schooling) to 4 (post-secondary education), predicted PM10 in the cooking area decreases by about 110 ug/m3. This is a very large effect, since the average PM10 concentration for our 236 monitored households is 260 ug/m3. Each increase of US$1.00/day is associated with a decline of 10 ug/m3, so the predicted reduction over the sample income range (less than $0.50/day to $15.00/day) is approximately 150 ug/m3.
Although education and income strongly reduce average pollution, they do not seem to change children's daily activity patterns in ways that reduce pollution exposure. Children's hours spent outdoors are not significantly affected by women's education or income per capita, and the measured effect of adult male education is actually perverse (children of more educated men spend less time outdoors).
Hence, we tentatively conclude that parents’ education and income do affect children's pollution exposure, but only through the determinants of pollution. We extend the analysis of income and education further by estimating separate Ordinary Least Square regressions for pollution determined by fuel choice and structural determinants of pollution (cooking locations, construction materials). We combine information on fuel choices and structural characteristics into two separate pollution indices, 21 with weights determined by the regression coefficients for cooking-area pollution determinants in Table 7 of Dasgupta et al. (2004). Table 7 presents the results, which suggest that female education, male education and family income all have large, highly significant effects on pollution via fuel choice. Female education has an equivalent effect on the structural determinants, but male education and family income do not appear to be significant. As in the composite result (Table 6), female education appears to be the strongest and most pervasive determinant of arrangements that reduce IAP.
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We illustrate our overall results in Table 8, which presents predicted average PM10 levels in cooking and living areas by income and women's education.
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Table 8 is generally consistent with our econometric results, while suggesting that some non-linear effects are not captured by the linear regression model in Table 6. For example, education seems to have minimal effects on pollution for the lowest income group, but pronounced effects when income is higher. Similarly, increased income does not reduce pollution consistently for the lowest education group, but it has a strong effect at higher levels of education. The combined effects of income and women's education are sufficient to approximately halve PM10 pollution, from near 300 ug/m3 in the poorest, least-educated groups to around 150 in the highest-income, best-educated groups. As our sample composition statistics clearly show in Table 9, most of the individuals (and households) in our survey are in the four table cells associated with the lowest income and education levels.
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Summary and conclusions |
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In this paper, we have investigated individuals’ exposure to IAP in Bangladesh. We have analysed exposure at two levels: differences within households attributable to family roles, and differences across households attributable to income and education. Within households, we have related individuals’ exposure to pollution in different locations during their daily round of activity. We find high levels of exposure for children and adolescents of both sexes, with particularly serious exposure for children under 5. Among prime-age adults, we find that men have half the exposure of women (whose exposure is similar to that of children and adolescents). We also find that elderly men have significantly lower exposure than elderly women.
Across households, we draw on results from a previous paper (Dasgupta et al. 2004), which relates pollution variation across households to choices of cooking fuel, cooking locations, construction materials and ventilation practices. We find that these choices are significantly affected by family income and adult education levels (particularly for women). Overall, we find that the poorest, least-educated households have twice the pollution levels of relatively high-income households with highly educated adults.
To summarize, we find that young children and poorly educated women in poor households face pollution exposures that are four times those of men in higher-income households organized by more highly educated women. In Dasgupta et al. (2004), we recommended feasible changes in cooking locations, construction materials and ventilation practices that could greatly reduce average household pollution levels. In this paper, we consider measures for narrowing the exposure gap within households. We focus particularly on changes for infants and young children, since they suffer the worst mortality and morbidity from IAP, but our findings also apply to women and adolescents. Our recommendations for reducing their exposure are based on a few simple, robust findings: hourly pollution levels in cooking and living areas are quite similar because cooking smoke diffuses rapidly and nearly completely into living areas. At the same time, outdoor pollution is far lower. At present, young children are only outside for an average of 3 hours per day. For children in a typical household, pollution exposure can be halved by adopting two simple measures: increasing their outdoor time from 3 to 5 or 6 hours per day, and concentrating outdoor time during peak cooking periods. We recognize that weather and other factors may intervene occasionally, and that child supervision outdoors may be difficult for some households. However, the potential benefits are so great that neighbours might well agree to pool outdoor supervision once they are aware of the implications for their children's health.
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Biographies |
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Susmita Dasgupta is Senior Economist in the Infrastructure/Environment Unit of the World Bank's Development Research Group. Dasgupta is a specialist in empirical research in environmental economics and has done extensive research on setting priorities in pollution control, estimation of pollution abatement cost, cost-effective regulations, and poverty/environment nexus. She received her undergraduate degree from Presidency College, India; her Masters degree from University of Calcutta, India; and her PhD from the State University of New York at Albany.
Mainul Huq has extensive research experience in development economics and environmental issues. He is currently working as an Economist for the Development Policy Group (DPG) of Bangladesh.
M Khaliquzzaman has more than 20 years of experience in studies related to environmental sciences including ambient and indoor air quality. He has been involved in the measurement and source apportionment of size-fractionated airborne particulate matter (PM2.5 and PM10) in Bangladesh using PIXE as an analytical tool. Khaliquzzaman obtained his PhD from London University in 1971 and he is currently a consultant with the World Bank Office, Dhaka.
Kiran D Pandey, PhD, is Senior Environmental Economist at the Global Environment Facility. His interests include: (a) indicators and system development for environmental health and environmental policy, and (b) global environment financing and reform. He is also a member of the World Health Organization (WHO) working group on Urban Air Pollution for WHO's Global Burden of Disease Comparative Quantification of Health Risks Study.
David Wheeler is Lead Economist in the Infrastructure/Environment Unit of the World Bank's Development Research Group. He received his undergraduate degree from Princeton University (1968) and his PhD in economics from the Massachusetts Institute of Technology (1974). He has also taught on the economics faculties of Boston University, Massachusetts Institute of Technology and the National University of Zaire (Congo).
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Appendix 1. Hourly household member locations, by age and gender (X = presence in a location) |
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