Abstract
Many urban households in developing countries use biomass fuels for cooking. The proportion of household biomass use varies among neighborhoods, and is generally higher in low socioeconomic status (SES) communities. Little is known of how household air pollution varies by SES and how it is affected by biomass fuels and traffic sources in developing country cities. In four neighborhoods in Accra, Ghana, we collected and analyzed geo-referenced data on household and community particulate matter (PM) pollution, SES, fuel use for domestic and small-commercial cooking, housing characteristics, and distance to major roads. Cooking area PM was lowest in the high-SES neighborhood, with geometric means of 25 (95% confidence interval, 21–29) and 28 (23–33) μg/m3 for fine and coarse PM (PM2.5 and PM2.5–10), respectively; it was highest in two low-SES slums, with geometric means reaching 71 (62–80) and 131 (114–150) μg/m3 for fine and coarse PM. After adjustment for other factors, living in a community where all households use biomass fuels would be associated with 1.5- to 2.7-times PM levels in models with and without adjustment for ambient PM. Community biomass use had a stronger association with household PM than household's own fuel choice in crude and adjusted estimates. Lack of regular physical access to clean fuels is an obstacle to fuel switching in low-income neighborhoods and should be addressed through equitable energy infrastructure.
Keywords: sustainable development, urbanization, global health, household energy, Africa
The populations of cities in the developing world are growing, with sub-Saharan Africa having the highest urban population growth rate worldwide (1). Some urban environmental health risks in the developing world are similar to those in high-income countries, such as the role of transportation as a determinant of particulate matter (PM) pollution levels and spatial patterns (2–5). Urban environmental health risks in developing countries also have some unique features, including high exposure to multiple risks in low-income “slum” neighborhoods (6, 7). A feature of urban PM pollution that, with few exceptions, is unique to developing countries is the widespread household use of biomass fuels (8, 9). Therefore, PM pollution in urban homes may be because of household or neighborhood biomass use in addition to sources that are also found in high-income countries, such as transportation and industrial pollution.
The patterns and sources of indoor air pollution in high-income countries have been studied (10–12). There is also increasing attention to residential indoor air quality in developing countries, including the concentrations of various pollutants, their sources, and the role of ventilation (13–15). However, most current studies of biomass fuels and household air pollution in developing countries have focused on the indoor environment in rural areas, where biomass is the most common or even universal household fuel. There are few studies of household PM in developing country cities, especially in relation to household and community biomass fuel use and socioeconomic status (SES) (7, 16–21). This is an important gap in our knowledge about sources of PM pollution in the home environment for the large number of people in urban areas where biomass fuels are common.
We systematically collected and analyzed data on PM in homes in four neighborhoods in Accra, Ghana. We also collected data on household SES, fuel use for domestic and small-commercial cooking, and housing characteristics. All our data were geo-referenced so we could also measure distance to major roads. We obtained small-area community SES and fuel use from the Ghana 2000 Population and Housing Census. Using this unique dataset, we examined household PM pollution in relation to household and neighborhood SES, fuel use, and selected other characteristics.
Our study took place in four neighborhoods in Accra, the capital of Ghana. Accra is located on the Gulf of Guinea and has a total area of more than 250 km2. The population of the Accra metropolitan area increased from 600,000 in 1970 to 1.7 million in 2000. The four study neighborhoods lie on a line from the coast to the northern boundaries of the Accra metropolitan area: Jamestown/Ushertown (JT), Asylum Down (AD), Nima (NM), and East Legon (EL) (Fig. S1). JT and NM are poor, densely populated communities where biomass is the predominant household fuel and is also used for small-scale commercial purposes, such as cooking street food (Fig. 1). AD is a middle class, mostly residential neighborhood, where fewer people use biomass; street food vendors are less common in AD than in JT and NM. EL is an upper-class, sparsely populated, residential neighborhood, with most families living on large plots of land.
Fig. 1.
(A) Population density, (B) community SES, and (C) percentage of households using biomass fuel by EA. Each EA has approximately the same population size, hence the area of an EA is inversely related to population density.
Results
Community and Household SES, Fuel Use, and Housing.
NM has the highest population density (441 people per 10,000 m2), followed by JT (329 per 10,000 m2), AD (27 per 10,000 m2), and EL (5 per 10,000 m2) (Fig. 1A). The SES index in census enumeration areas (EAs) in JT and NM are in the lowest quintile of all EAs. In contrast, the SES of AD and EL fall into the wealthiest quintile (Fig. 1B). In the census, about 80% of households in JT and NM used biomass fuels, compared with 43% in AD and 53% in EL (Fig. 1C). In our study households, biomass use was highest in JT, where 95% [95% confidence interval (CI) 85–100%] and 45% (23–67%) of households used biomass for their own and small-commercial cooking, respectively (Table S1). At the low end, only 22% (3–41%) and 6% (0–17%) of surveyed households in EL used biomass for their own and small-commercial cooking. EL was surrounded by other high-SES and below-median biomass communities. The other three neighborhoods were closer to the city center and were surrounded by communities that may have had lower or higher SES and biomass use prevalence (Fig. 1).
The housing arrangement in most study households in JT (90%) and NM (100%) was a compound room, with multiple households living in different parts of a larger single structure built around a central courtyard. Most households in JT and NM cooked outdoors in the open-air shared compound courtyard, where their neighbors may also cook (Table S1). In EL, 89% of study households lived in separate, free-standing houses, and 83% of the households cooked in separate indoor kitchens (Table S1).
Average Daily PM.
In most households in AD and JT, cooking area PM2.5 (particles with aerodynamic diameter ≤ 2.5 μm; fine PM) concentrations were lower than ambient levels (Fig. 2A), with geometric mean household-to-ambient ratios of about 0.70. Some cooking area PM2.5 concentrations in NM were lower than the ambient levels, whereas others were higher, leading to a geometric mean household-to-ambient ratio of 0.97. Cooking area PM2.5 was similar to the ambient levels in most EL households. However, the household-to-ambient ratios had a geometric mean of 1.22 because of higher cooking area concentrations in five households. Two of these five households used charcoal as their primary fuel, another raised poultry on their compound.
Fig. 2.
The relationship between cooking area and ambient PM using data from simultaneous measurement periods for (A) fine PM (PM2.5), (B) coarse PM (PM2.5–10), and (C) PM2.5-to- PM10 ratio.
Unlike PM2.5, cooking area coarse PM (PM2.5–10) concentrations exceeded corresponding ambient levels everywhere, except in one AD household (Fig. 2B). Mean residual cooking area PM2.5–10 (cooking area minus ambient) was 58 μg/m3 and mean household-to-ambient ratio was 2.32. These results suggest the disproportionate presence of household sources for coarse PM, such as sweeping and resuspension. As a result of such fine and coarse PM patterns, cooking area PM2.5-to-PM10 ratios were lower than the ambient ratios on the same day (Fig. 2C).
Cooking area PM was lowest in EL, with geometric means of 25 (21–29) μg/m3 for PM2.5 and 28 (23–33) μg/m3 for PM2.5–10, and highest in JT with 71 (62–80) μg/m3 for PM2.5 and 118 (101–138) μg/m3 for PM2.5–10, and in NM with 52 (44–63) μg/m3 for PM2.5 and 131 (114–150) μg/m3 for PM2.5–10. Although measurement periods varied, data on seasonal patterns of ambient PM reported elsewhere (2) and the above household-ambient comparisons suggest that PM in JT households would likely be the highest of all neighborhoods, regardless of season, as was ambient PM in this neighborhood.
Association of Average Daily PM with Household and Community Fuel Use.
Using biomass fuels and living in a high biomass-use community were both associated with higher cooking area PM (high vs. low biomass-use communities were defined based on whether the proportion of households using biomass fuels in the EA was above vs. below median of all EAs) (Tables 1 and 2). The lowest cooking area PM2.5 and PM2.5–10 were measured in homes with clean fuels and in low biomass-use communities, 27 (24–31) and 45 (34–59) μg/m3, respectively, and the highest in homes that used biomass and were in high biomass-use communities, 60 (53–68) and 128 (116–142) μg/m3, respectively. Of the other two groups, cooking areas in high biomass-use communities with clean fuels had higher PM than those that used biomass fuels but lived in low biomass-use communities. Similarly, commercial cooking with biomass fuels was associated with higher cooking area PM, making such households when located in high biomass-use communities the most polluted, with PM2.5 and PM2.5–10 geometric means of 77 (64–91) and 143 (120–169) μg/m3, respectively (Table 2). There was no meaningful difference between homes that did no commercial cooking and those that did so with clean fuels, but the sample size for the latter was four and hence should be considered as suggestive only.
Table 1.
Cooking area concentrations of PM2.5 and PM2.5–10 (μg/m3) stratified by household and neighborhood fuel use
| Biomass fuels | Nonbiomass fuels | ||
| PM2.5 | |||
| Low biomass use community* | Number of households | 14 | 21 |
| Geometric mean (95% CI) | 31 (25, 38) | 27 (24, 31) | |
| High biomass use community† | Number of households | 42 | 2 |
| Geometric mean (95% CI) | 60 (53, 68) | 53 (3, 983) | |
| PM2.5–10 | |||
| Low biomass use community | Number of households | 13 | 20 |
| Geometric mean (95% CI) | 71 (45, 110) | 45 (34, 59) | |
| High biomass use community | Number of households | 42 | 2 |
| Geometric mean (95% CI) | 128 (116, 142) | 80 (37, 175) | |
*The proportion of households using biomass fuels in the EA is below median of all EAs.
†The proportion of households using biomass fuels in the EA is above median of all EAs.
Table 2.
Cooking area concentrations of PM2.5 and PM2.5–10 (μg/m3) stratified by small-commercial cooking and neighborhood fuel use
| Commercial cooking (biomass fuels) | Commercial cooking (nonbiomass fuels) | No commercial cooking | ||
| PM2.5 | ||||
| Low biomass use community* | Number of households | 5 | 4 | 26 |
| Geometric mean (95% CI) | 27 (18, 39) | 23 (11, 47) | 30 (26, 33) | |
| High biomass use community† | Number of households | 16 | 0 | 28 |
| Geometric mean (95% CI) | 77 (64, 91) | — | 52 (45, 60) | |
| PM2.5–10 | ||||
| Low biomass use community | Number of households | 4 | 4 | 25 |
| Geometric mean (95% CI) | 63 (24, 167) | 47 (11, 194) | 53 (40, 71) | |
| High biomass use community | Number of households | 16 | 0 | 28 |
| Geometric mean (95% CI) | 143 (120, 169) | — | 117 (103, 132) | |
*The proportion of households using biomass fuels in the EA is below median of all EAs.
†The proportion of households using biomass fuels in the EA is above median of all EAs.
The results in Tables 1 and 2 show crude associations, without controlling for other variables that may vary across households. The multivariate associations confirm that using biomass fuels for own and commercial cooking, and living in EAs with higher biomass-use prevalence, were associated with higher cooking area PM; only the effects of neighborhood fuel use were consistently significant (Table 3). Beyond their statistical significance, living in an EA with 26% higher biomass-use prevalence had about the same effect on cooking area PM2.5 as switching from a cleaner fuel to biomass in model 1; the equivalence would be at 69% higher biomass-use prevalence for PM2.5–10. A household located in an EA where all households use biomass fuels would have 149% (104–223%) of the PM2.5 level and 165% (122–246%) of the PM2.5–10 level compared with its counterpart in an EA with no biomass use after adjustment for neighborhood ambient PM; the effects were 272% (182–406%) and 272% (165–448%) in the model that did not adjust for ambient PM. For fine PM, the (proportional) effects of using biomass for commercial cooking seemed larger than using it for household purposes, whereas the opposite was seen for coarse PM, although the differences were not statistically significant. The associations with household size, average distance to main roads, cooking location, and the presence of smokers in the house were generally nonsignificant. Adjusting for neighborhood PM weakened the association with EA biomass use for both size fractions, and that of household biomass use for PM2.5, but the effect of household biomass use on PM2.5–10 became larger and significant after this adjustment.
Table 3.
Regression coefficients for multivariate analysis of the association of cooking area PM with sources, cooking area location, and meteorological covariates
| Model 1 |
Model 2 |
|||
| Variable | Coefficient (95% CI) | P value | Coefficient (95% CI) | P value |
| Dependent variable: ln (PM2.5) | n = 79; adjusted R2 = 0.68 | n = 79; adjusted R2 = 0.50 | ||
| Constant | 1.246 (0.610, 1.881) | <0.001 | 3.038 (2.704, 3.372) | <0.001 |
| ln (neighborhood average) | 0.517 (0.351, 0.683) | <0.001 | ||
| Households using biomass in the EA (%) | 0.004 (0.000, 0.008) | 0.03 | 0.010(0.006, 0.014) | <0.001 |
| Household size | 0.014 (−0.015, 0.043) | 0.35 | 0.000 (−0.035, 0.036) | 0.99 |
| Average distance to main roads (km) | 0.527 (−0.200, 1,254) | 0.15 | −0.143(−1.008, 0.722) | 0.74 |
| Household cooking fuel | ||||
| Nonbiomass | 0.0 | NA | 0.0 | NA |
| Biomass | 0.104 (−0.153, 0.362) | 0.42 | 0.174 (−0.146, 0.493) | 0.28 |
| Small commercial cooking fuel | ||||
| No commercial cooking | 0.0 | NA | 0.0 | NA |
| Nonbiomass | −0.093(−0.428, 0.242) | 0.58 | −0.116 (−0.533, 0.301) | 0.58 |
| Biomass | 0.211 (0.025, 0.396) | 0.03 | 0.255 (0.025, 0.486) | 0.03 |
| Cooking area | ||||
| Inside the house | 0.0 | NA | 0.0 | NA |
| Open air | −0.040(−0.342, 0.217) | 0.78 | −0.112 (−0.456, 0.231) | 0.52 |
| Separate cookhouse | −0.221(−0.536, 0.094) | 0.17 | −0.227 (−0.620, 0.165) | 0.25 |
| Secondhand smoke | ||||
| No smoker in the house | 0.0 | NA | 0.0 | NA |
| Smoker in the house | −0.053(-0.345, 0.239) | 0.72 | 0.160 (−0.193, 0.514) | 0.37 |
| Meteorological factor | ||||
| Raining duration (hours) | −0.000(−0.033, 0.032) | 0.98 | −0.024 (−0.063, 0.015) | 0.22 |
| Dependent variable: ln (PM2.5–10) | n = 77; adjusted R2 = 0.86 | n = 77; adjusted R2 = 0.60 | ||
| Constant | 1.049 (0.487, 1.611) | <0.001 | 3.903 (3.516, 4.289) | <0.001 |
| ln (neighborhood average) | 0.750 (0.615, 0.885) | <0.001 | ||
| Households using biomass in the EA (%) | 0.005 (0.002, 0.009) | 0.001 | 0.010 (0.005, 0.015) | <0.001 |
| Household size | 0.017 (−0.009, 0.042) | 0.19 | −0.022 (−0.064, 0.019) | 0.29 |
| Average distance to main roads (km) | −0.480 (−1.100, 0.139) | 0.13 | −1.522(−2.519, −0.525) | 0.003 |
| Household cooking fuel | ||||
| Nonbiomass | 0.0 | NA | 0.0 | NA |
| Biomass | 0.343 (0.126, 0.561) | 0.002 | 0.225 (−0.140, 0.591) | 0.22 |
| Small commercial cooking fuel | ||||
| No commercial cooking | 0.0 | NA | 0.0 | NA |
| Nonbiomass | 0.050 (−0.233, 0.334) | 0.72 | −0.012 (−0.490, 0.467) | 0.96 |
| Biomass | 0.103 (−0.054, 0.261) | 0.20 | 0.155 (−0.111, 0.421) | 0.25 |
| Cooking location | ||||
| Inside the house | 0.0 | NA | 0.0 | NA |
| Open air | −0.033(−0.266, 0.201) | 0.78 | 0.094(−0.299, 0.488) | 0.63 |
| Separate cook house | −0.144 (−0.411, 0.123) | 0.29 | 0.021 (−0.427, 0.468) | 0.93 |
| Secondhand smoke | ||||
| No smokers in the house | 0.0 | NA | 0.0 | NA |
| Smokers in the house | −0.098 (−0.338, 0.142) | 0.42 | −0.041 (−0.446, 0.364) | 0.84 |
| Meteorological factor | ||||
| Raining duration (hours) | −0.007 (−0.035, 0.021) | 0.62 | −0.063 (−0.108, -0.018) | 0.006 |
NA, not applicable. Model 1 is adjusted for neighborhood average PM concentrations at nontraffic rooftop sites and model 2 is not. See SI Text for details.
PM Patterns During the Day.
In all neighborhoods, both the ambient and cooking area PM2.5 rose in the early morning hours. This morning rise started as early as 0300 hours in JT and NM vs. around 0600 hours in EL (Fig. 3). Although in any single neighborhood this pattern may either be because of morning residential and small commercial cooking, other commercial activities that use biomass (e.g., fish smoking and bakeries), and traffic, or because of overnight surface temperature inversions, the differences in start time and rise across neighborhoods make the differential patterns of sources a more likely explanation. Specifically, in both JT and NM, cooking street food and other activities that use biomass fuels begin at very early hours. In JT and NM, we also observed a midday peak around 1100 hours, which may correspond to midday cooking and traffic. As described elsewhere (2), ambient PM also showed a rise in PM2.5 in the evening (1800–2100 hours) except in EL, possibly because of evening rush hour and biomass use; this evening rise was less noticeable in cooking areas. In JT and AD, ambient PM2.5 was higher than cooking-area levels, whereas the two environments had similar PM2.5 in EL and NM.
Fig. 3.
Continuous PM2.5 concentrations in the household cooking and living areas and at ambient rooftop sites. The measurements were standardized for variation in relative humidity throughout the day, corrected against gravimetric measurements and smoothed as described in SI Text. In each panel, measurements from all days over the measurement period are averaged.
On average, PM2.5 concentrations in the cooking and living areas tracked relatively well, suggesting diffusion between household environments or from the ambient air to household environments (Fig. 3). However, the pairwise correlations between continuous PM2.5 in different indoor and ambient environments varied substantially, with living area concentration in households using nonbiomass fuels having higher correlation with ambient levels than those that used biomass (Fig. S2).
Discussion and Conclusions
To our knowledge, this study is unique in presenting a detailed analysis of the association between household air pollution and its household and community determinants in a large city in the developing world, especially in sub-Saharan Africa, where urban population is growing faster than any other region (1). In summary, we found that household and community biomass fuel use were important predictors of household PM pollution in Accra neighborhoods. Notably, community biomass use had a stronger effect on cooking area PM than a household's own fuel in crude and adjusted estimates. At the household level, fuel use for both own and small-commercial cooking seemed to be associated with PM pollution. We also considered associations by PM size fraction and found that cooking area PM2.5–10 concentrations consistently exceeded corresponding ambient levels, suggesting the presence of household sources for coarse particles, such as sweeping and resuspension; the pattern for ambient and household PM2.5 was more mixed.
Although in rural areas better ventilation may be able to reduce exposure to indoor air pollution from solid fuels, our results on the role of both household and community biomass use indicate that population-based reduction in solid fuel use is necessary for reducing air pollution exposure and its health effects in developing country cities, also supported by the recent evaluation of the Dublin coal sale ban (22). As seen in our data and in previous studies (8, 9, 23), in Accra and in other developing country cities, biomass use is indeed more common in low-income households and communities. Fuel price and the initial cost of stove price are likely to be one of the reasons for this pattern, which should be addressed through policies that facilitate financial access to cleaner fuel for the poor. However, community-level lack of regular physical access may be a larger obstacle to fuel switching than actual fuel cost and household level affordability (24). For example, in our household questionnaire, fuel price ranked lower than “availability when needed,” “availability near home,” or “ease of use when cooking” as a reason for fuel choice. This finding is consistent with the fact that both JT and NM also have a large number of biomass fuel vendors (Fig. S1).
In contrast, liquefied petroleum gas purchase would involve taking an empty cylinder to a fuel depot, itself requiring a private car or taxi, with a nontrivial risk that the depot will not have replacement fuel when they arrive there. With such issues, households do not make the initial investment in liquefied petroleum gas equipment (a stove, hose, regulator, and cylinder) or revert back to biomass fuels after some period. Ghana has planned to use the West Africa Gas Pipeline (http://www.wagpco.com/) to increase its supply of natural gas, primarily for power generation and large industrial use. This project, which has been affected by multiple delays, does not have a residential energy component. Ghana has also recently found crude oil off the shores of its Western Atlantic Coast; it is expected that natural gas would be produced together with oil. Given the public financing of both projects, a relevant policy debate should focus on whether a portion of the proceeds and supply from these projects should be used to develop energy infrastructure in low- and middle-income Accra neighborhoods. Such a community-based approach may ultimately be the only effective way to reduce air pollution in Accra communities and homes, contributing toward Millennium Development Goal 7 (ensure environmental sustainability) as well as the associated Millennium Development Goal 4 (reduce child mortality), which is directly affected by biomass air pollution.
Materials and Methods
This research was approved by the Harvard School of Public Health and by the Noguchi Memorial Institute for Medical Research at the University of Ghana Institutional Review Boards.
We measured PM2.5 and PM10 (aerodynamic diameter ≤ 10 μm) in 80 households in the four study neighborhoods (Fig. S1). The households were selected from those in the Women's Health Study of Accra (25), whose participants were a random sample of all adult women in Accra, through stratified SES and age-group sampling using the 2000 Population and Housing Census of Ghana as the sampling frame. We selected households in the study neighborhoods that had more than two members. Furthermore, we selected households at varying distances from main roads.
In each household, we measured 48-h integrated PM2.5 and PM10 concentrations in the cooking area. Over the same 48-h period, we measured PM2.5 continuously in both the cooking and living areas. We also measured integrated and continuous ambient PM2.5 and PM10 concentrations at rooftop sites in the same neighborhood, as described elsewhere (2). Further information on study design, pollution measurement methods, number of measurements, and meteorological variables is provided in SI Text and Table S2.
We also used a structured questionnaire to collect data on the number of household members, housing and cooking-area characteristics, ownership of assets, fuels and stoves used for domestic and small-commercial cooking, and the presence of other combustion sources and smokers in the house. Following previous analyses of household data in developing countries (23, 26), we measured household and community SES using an index based on housing characteristics, water and waste systems, and ownership of durable assets, using the questionnaire data and data from the Ghana 2000 Population and Housing Census. Details of data and SES analyses are provided in SI Text.
We used regression analysis to examine the association of cooking area PM with its potential household and neighborhood determinants that may be proxies for PM sources and for ventilation. Details of the statistical model are provided in SI Text.
Supplementary Material
Acknowledgments
We thank the residents of Nima, Jamestown/Ushertown, Asylum Down, and East Legon for their hospitality; Nana Prempeh and Adam Abdul Fatah for field assistance; and the Legal Resources Center and the Department of Geography and Resource Development at the University of Ghana for valuable help with logistical arrangements. Funding for this research was provided by National Science Foundation Grant 0527536, and laboratory support was provided by the Harvard National Institute on Environmental Health Sciences Center for Environmental Health.
Footnotes
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1019183108/-/DCSupplemental.
References
- 1.United Nations Department of Economic and Social Affairs (Population Division) World Urbanization Prospects: The 2006 Revision. New York: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat; 2007. [Google Scholar]
- 2.Dionisio KL, et al. Air pollution in Accra neighborhoods: spatial, socioeconomic, and temporal patterns. Environ Sci Technol. 2010;44:2270–2276. doi: 10.1021/es903276s. [DOI] [PubMed] [Google Scholar]
- 3.Dionisio KL, et al. Within-neighborhood patterns and sources of particle pollution: Mobile monitoring and geographic information system analysis in four communities in Accra, Ghana. Environ Health Perspect. 2010;118:607–613. doi: 10.1289/ehp.0901365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Jackson MM. Roadside concentration of gaseous and particulate matter pollutants and risk assessment in Dar-es-Salaam, Tanzania. Environ Monit Assess. 2005;104:385–407. doi: 10.1007/s10661-005-1680-y. [DOI] [PubMed] [Google Scholar]
- 5.Etyemezian V, et al. Results from a pilot-scale air quality study in Addis Ababa, Ethiopia. Atmos Environ. 2005;39:7849–7860. [Google Scholar]
- 6.Sclar ED, Garau P, Carolini G. The 21st century health challenge of slums and cities. Lancet. 2005;365:901–903. doi: 10.1016/S0140-6736(05)71049-7. [DOI] [PubMed] [Google Scholar]
- 7.Songsore J, McGranahan G. The political economy of household environmental management: Gender, environment and epidemiology. World Dev. 1998;26:395–412. [Google Scholar]
- 8.Bailis R, Ezzati M, Kammen DM. Mortality and greenhouse gas impacts of biomass and petroleum energy futures in Africa. Science. 2005;308:98–103. doi: 10.1126/science.1106881. [DOI] [PubMed] [Google Scholar]
- 9.Barnes DF, Krutilla K, Hyde WF. The Urban Household Energy Transition: Social and Environmental Impacts in the Developing World. Washington, DC: RFF Press; 2005. [Google Scholar]
- 10.Spengler JD, Samet JM, McCarthy JF, editors. Indoor Air Quality Handbook. New York: McGraw-Hill Co.; 2001. [Google Scholar]
- 11.Sexton K, Spengler JD, Treitman RD. Effects of residential wood combustion on indoor air quality: A case study in Waterbury, Vermont. Atmos Environ. 1984;18:1371–1383. [Google Scholar]
- 12.Dockery DW, Spengler JD. Indoor-outdoor relationships of respirable sulfates and particles. Atmos Environ. 1981;15:335–343. [Google Scholar]
- 13.Smith KR. Fuel combustion, air pollution exposure, and health: Situation in developing countries. Annu Rev Energy Environ. 1993;18:529–566. [Google Scholar]
- 14.Smith KR, Apte MG, Yuqing M, Wongsekiarttirat W, Kulkarni A. Air pollution and the energy ladder in asian cities. Energy. 1994;19:587–600. [Google Scholar]
- 15.Smith KR, Mehta S, Maeusezahl-Feuz M. In: Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Geneva: World Health Organization; 2004. pp. 1435–1493. [Google Scholar]
- 16.Saksena S, et al. Exposure of infants to outdoor and indoor air pollution in low-income urban areas—A case study of Delhi. J Expo Anal Environ Epidemiol. 2003;13:219–230. doi: 10.1038/sj.jea.7500273. [DOI] [PubMed] [Google Scholar]
- 17.Benneh G, et al. Environmental Problems and the Urban Household in the Greater Accra Metropolitan Area (GAMA) – Ghana. Stockholm: Stockholm Environment Institute; 1993. [Google Scholar]
- 18.Taneja A, Saini R, Masih A. Indoor air quality of houses located in the urban environment of Agra, India. Ann N Y Acad Sci. 2008;1140:228–245. doi: 10.1196/annals.1454.033. [DOI] [PubMed] [Google Scholar]
- 19.Smith KR. Indoor air pollution in developing countries: Recommendations for research. Indoor Air. 2002;12:198–207. doi: 10.1034/j.1600-0668.2002.01137.x. [DOI] [PubMed] [Google Scholar]
- 20.Dasgupta S, Huq M, Khaliquzzaman M, Pandey K, Wheeler D. Indoor air quality for poor families: new evidence from Bangladesh. Indoor Air. 2006;16:426–444. doi: 10.1111/j.1600-0668.2006.00436.x. [DOI] [PubMed] [Google Scholar]
- 21.Massey D, Masih J, Kulshrestha A, Habil M, Taneja A. Indoor/outdoor relationship of fine particles less than 2.5 μm (PM2.5) in residential homes locations in central Indian region. Build Environ. 2009;44:2037–2045. [Google Scholar]
- 22.Clancy L, Goodman P, Sinclair H, Dockery DW. Effect of air-pollution control on death rates in Dublin, Ireland: An intervention study. Lancet. 2002;360:1210–1214. doi: 10.1016/S0140-6736(02)11281-5. [DOI] [PubMed] [Google Scholar]
- 23.Gakidou E, et al. Improving child survival through environmental and nutritional interventions: The importance of targeting interventions toward the poor. JAMA. 2007;298:1876–1887. doi: 10.1001/jama.298.16.1876. [DOI] [PubMed] [Google Scholar]
- 24.Ezzati M, et al. Energy management and global health. Annu Rev Environ Resour. 2004;29:383–420. [Google Scholar]
- 25.Duda RB, et al. Results of the Women's Health Study of Accra: Assessment of blood pressure in urban women. Int J Cardiol. 2007;117:115–122. doi: 10.1016/j.ijcard.2006.05.004. [DOI] [PubMed] [Google Scholar]
- 26.Wagstaff A. Socioeconomic inequalities in child mortality: Comparisons across nine developing countries. Bull World Health Organ. 2000;78:19–29. [PMC free article] [PubMed] [Google Scholar]
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